METHODS, KITS AND COMPOSITIONS FOR DETERMINING SEVERITY AND SURVIVAL OF HEART FAILURE IN A SUBJECT

FIELD OF THE DISCLOSURE

The disclosure relates to methods, kits and compositions for determining the severity and survival outcome of a subject with heart failure. More particularly, the disclosure relates to methods, kits and compositions for determining the severity and survival outcome by measuring a level of one or more gene products in blood of the subject.

BACKGROUND OF THE DISCLOSURE

Heart failure is increasing as a public health concern and rapidly growing as an economic burden. The enormous public health and economic burdens imposed by heart failure can be decreased only by introducing improved therapies and better patient management. The genomic approaches to disease that have revolutionized biologic and biomedical research over the past 10 years hold significant promise in tackling these issues.

Heart failure results from structural or functional cardiac disorders that lead to insufficient supply of blood throughout the body. With an aging population, heart failure has become a major public health concern with its incidence continuing to increase: the condition currently affects more than five million people in the United States, and more than 500,000 new cases occur annually (Rosamond et al., 2007). While advances in the management of heart failure have modestly improved outcomes in patients with this disease, heart failure still remains the leading hospital admission diagnosis in elderly patients and carries a 5 year mortality of nearly 50% (Roger et al., 2004; Schocken et al., 2008). Thus the overall morbidity and mortality of this disease remain unacceptably high. As such better diagnostic strategies are required to aid in defining the prognosis and treatment of patients with heart failure.

Heart failure has long been recognized as a systemic disease directly affecting circulating level of numerous neurohormones, cytokines and inflammatory markers (Braunwald, 2008; Mann and Bristow, 2005). These circulating factors directly affect (largely adversely) intracellular signaling and consequent gene expression which has been well demonstrated in the myocardium. Specifically, significant elevation of gene expression associated with cell growth, signal transduction and cell defense have been demonstrated using gene expression profiling with microarray (Cunha-Neto et al., 2005; Kittleson et al., 2005). Thus myocardial gene expression likely reflects direct tissue changes associated with the cardiomyopathic process as well as consequential alterations in gene expression secondary to the humoral response of the disease state.

Given the systemic nature of heart failure and the protean nature of neurohormonal signaling, other tissues are also affected by heart failure state. Expression profiling of blood samples has been successfully applied to identify blood expression patterns associated with coronary artery disease (Ma and Liew, 2003) and with plasma lipid levels (Ma et al., 2007).

There remains a need for blood expression signatures as diagnostic and prognostic tools in heart failure management.

SUMMARY OF THE DISCLOSURE

The present inventors have shown novel blood markers for determining the severity and survival outcome of heart failure in a subject. This use can be effected in a variety of ways as further described and exemplified herein.

Accordingly, in one aspect there is provided a method of determining a severity of heart failure in a human test subject, the method comprising, for each gene of a set of one or more genes listed in Table 2: a) providing test data representing a level of RNA encoded by the gene in blood of the test subject; b) providing positive control data representing levels of RNA encoded by the gene in blood of human control subjects having a categorized severity of heart failure; and c) comparing the level of step a) to the levels in blood of control subjects to thereby determine a value indicating whether the test data corresponds to the positive control data; wherein a correspondence between the test data and the positive control data indicates that the test subject has the categorized severity of heart failure. In one embodiment, the set of genes comprises or consists of an ASGR2 gene and a STAB1 gene. In another embodiment, the control data comprises the average level in control subjects. The categorized severity may be compensated heart failure or decompensated heart failure. The method may further comprise determining a level of RNA encoded by the gene in blood of the test subject, thereby providing the test data. The method may further comprise determining levels of RNA encoded by the gene in blood of human subjects having the categorized severity of heart failure, thereby providing the positive control data. Step c) may be effected by: inputting, to a computer, the test data, wherein the computer is for comparing data representing a level of RNA encoded by the gene in blood of a human subject to levels of RNA encoded by the gene in subjects having the categorized severity of heart failure, to thereby output a value indicating whether the test data corresponds to the positive control data; and causing the computer to compare the test data to the positive control data, to thereby output the value indicating whether the test data corresponds to the positive control data.

In another aspect there is provided a method of determining a severity of heart failure in a human test subject, the method comprising, for each gene of a set of one or more of the genes listed in Table 2 (a) determining a level of RNA encoded by the gene in blood of the test subject, thereby generating test data; (b) providing a first positive control data representing levels of RNA encoded by the gene in blood of human control subjects having a first categorized severity of heart failure; and (c) comparing the levels of a) and b); wherein a correspondence between the test data and the positive control data indicates that the test subject has the first categorized severity of heart failure. In one embodiment, the one or more genes is ASGR2, C3AR1 and/or STAB1. The first categorized severity may be compensated heart failure or decompensated heart failure.

In a further aspect, the method further comprises providing a second positive control data representing levels of RNA encoded by the gene in blood of human control subjects having a second categorized severity of heart failure, wherein correspondence between the test data and the second positive control data indicates that the test subject has the second categorized severity of heart failure. In one embodiment, the first categorized severity is compensated heart failure and the second categorized severity is decompensated heart failure.

According to further features described below the determining of the level of RNA encoded by the gene in blood of the test subject is determined as a ratio to a level of RNA encoded by the gene in blood of a healthy test subject.

In another aspect, the method further comprises determining levels of RNA encoded by the gene in blood of a population of human subjects having the first categorized severity of heart failure, thereby providing the positive control data representing the levels of RNA encoded by the gene in blood of human control subjects having the first categorized severity of heart failure. In yet another aspect, the method further comprises determining levels of RNA encoded by the gene in blood of a population of human subjects having the second categorized severity of heart failure, thereby providing the positive control data representing the levels of RNA encoded by the gene in blood of human control subjects having the second categorized severity of heart failure.

In a further aspect the method further comprises providing a third control data representing levels of RNA encoded by the gene in blood of human control subjects which are healthy, and wherein step c) is effected by comparing the test data to the first or second positive control data and the third control data, wherein correspondence between the test data and the first or second positive control data and not the third control data indicates that the test subject has the first or second categorized severity of heart failure.

According to another aspect, there is provided a computer-based method of determining a severity of heart failure in a human test subject, the method comprising inputting, to a computer, test data representing a level of RNA encoded by one or more of the genes listed in Table 2 in blood of the test subject; and causing the computer to compare the test data to a first positive control data representing levels of RNA encoded by the gene in blood of human control subjects having a first categorized severity of heart failure, wherein correspondence between the test data and the first positive control data indicates that the test subject has the first categorized severity of heart failure. In one embodiment, the one or more genes is ASGR2, C3AR1 and/or STAB1.

In a further aspect, there is provided a method of monitoring the progression of heart failure in a human subject, the method comprising for each gene of a set of one or more of ASGR2, C3AR1, STAB1, KRCC1, KYNU, LOH11CR2A, TMEM144, FKBP1B, VCAN, LTA4H, MGST1, or NOTCH2NL (a) determining a level of RNA encoded by an ASGR2 gene in blood of the subject at a first time point; (b) determining a level of RNA encoded by an ASGR2 gene in blood of the subject at a second time point; (c) comparing the levels in a) and b); wherein an increased level at the second time point indicates a progression of heart failure. In one embodiment, the one or more genes is/are ASGR2, C3AR1 and/or STAB1.

In another aspect, there is provided a method of monitoring the progression of heart failure in a human subject, the method comprising, for each gene of a set of one or more of FAM84B, RBL2, XRCC5, TNRC6C, ZBTB44, HERC1, SNORA72, WIPF1, PPP1R2, C4orf30, KIAA0888, TMEM106B, NR3c2, KLHL24, FLJ31306, MAP2K6, SATB1, WHDC1L1, EDG1, MBIP, RSU1, DYRK2, DOCK9, LOC283666, SLC4A7, ODF2L, PAQR8, or C14orf64 or the genes listed in Table 4 (a) determining a level of RNA encoded by the gene in blood of the subject at a first time point; (b) determining a level of RNA encoded by the gene in blood of the subject at a second time point; (c) comparing the levels in a) and b); wherein an decreased level at the second time point indicates a progression of heart failure.

According to yet another aspect, there is provided a computer-based method of monitoring the progression of heart failure in a human subject, the method comprising inputting, to a computer, test data representing a level of RNA encoded by one or more of ASGR2, C3AR1, STAB1, KRCC1, KYNU, LOH11CR2A, TMEM144, FKBP1B, VCAN, LTA4H, MGST1, or NOTCH2NL gene in blood of the subject at a first and second time point; and causing the computer to compare the data of the first time point to the data of the second time point, and to determine whether the level of RNA encoded by the gene in blood of the subject is increased at the second time point compared to the level of RNA encoded by the gene in blood of the subject at the first time point, wherein a determination that the level of RNA encoded by the gene in blood of the test subject is increased at the second time point indicates the progression of heart failure. In one embodiment, the one or more genes is ASGR2, C3AR1 and/or STAB1.

In an additional aspect, there is provided a computer-based method of monitoring the progression of heart failure in a human subject, the method comprising inputting, to a computer, test data representing a level of RNA encoded by one or more of FAM84B, RBL2, XRCC5, TNRC6C, ZBTB44, HERC1, SNORA72, WIPF1, PPP1R2, C4orf30, KIAA0888, TMEM106B, NR3c2, KLHL24, FLJ31306, MAP2K6, SATB1, WHDC1L1, EDG1, MBIP, RSU1, DYRK2, DOCK9, LOC283666, SLC4A7, ODF2L, PAQR8, or C14orf64 gene or the genes listed in Table 4 in blood of the subject at a first and second time point; and causing the computer to compare the data of the first time point to the data of the second time point, and to determine whether the level of RNA encoded by the gene in blood of the subject is decreased at the second time point compared to the level of RNA encoded by the gene in blood of the subject at the first time point, wherein a determination that the level of RNA encoded by the gene in blood of the test subject is decreased at the second time point indicates the progression of heart failure.

In another aspect, there is provided a method of classifying a human test subject as having decompensated heart failure, the method comprising (a) determining a level of RNA encoded by each gene of a set of one or more of ASGR2, C3AR1, STAB1, KRCC1, KYNU, LOH11CR2A, TMEM144, FKBP1B, VCAN, LTA4H, MGST1, or NOTCH2NL in blood of the test subject, thereby generating test data; (b) providing control data representing a level of RNA encoded by the gene in blood of human control subjects not having decompensated heart failure; and (c) comparing the test data to the control data, wherein a determination in step (c) that the level of RNA encoded by the gene in blood of the test subject is higher than the level of RNA encoded by the gene in blood of human control subjects not having decompensated heart failure is used to classify the test subject as having decompensated heart failure.

In yet another aspect, there is provided a method of classifying a human test subject as having decompensated heart failure, the method comprising for each gene of a set of one or more of FAM84B, RBL2, XRCC5, TNRC6C, ZBTB44, HERC1, SNORA72, WIPF1, PPP1R2, C4orf30, KIAA0888, TMEM106B, NR3c2, KLHL24, FLJ31306, MAP2K6, SATB1, WHDC1L1, EDG1, MBIP, RSU1, DYRK2, DOCK9, LOC283666, SLC4A7, ODF2L, PAQR8, or C14orf64 (a) determining a level of RNA encoded by the gene in blood of the test subject, thereby generating test data; (b) providing control data representing a level of RNA encoded by the gene in blood of human control subjects not having decompensated heart failure; and (c) comparing the test data to the control data, wherein a determination in step (c) that the level of RNA encoded by the gene in blood of the test subject is lower than the level of RNA encoded by the gene in blood of human control subjects not having decompensated heart failure is used to classify the test subject as having decompensated heart failure.

In a further aspect there is provided a computer-based method of classifying a human test subject as having decompensated heart failure, the method comprising for each gene of a set of one or more of ASGR2, C3AR1, STAB1, KRCC1, KYNU, LOH11CR2A, TMEM144, FKBP1B, VCAN, LTA4H, MGST1, or NOTCH2NL inputting, to a computer, test data representing a level of RNA encoded by the gene in blood of the test subject; and causing the computer to compare the test data to control data representing a level of RNA encoded by the gene in blood of human control subjects not having decompensated heart failure, and to determine whether the level of RNA encoded by the gene in blood of the test subject is higher than the level of RNA encoded by the gene in blood of human control subjects not having decompensated heart failure, wherein a determination that the level of RNA encoded by the gene in blood of the test subject is higher than the level of RNA encoded by the gene in blood of human control subjects not having decompensated heart failure is used to classify the test subject as having decompensated heart failure.

In yet a further aspect, there is provided a computer-based method of classifying a human test subject as having decompensated heart failure, the method comprising for each gene of a set of one or more of FAM84B, RBL2, XRCC5, TNRC6C, ZBTB44, HERC1, SNORA72, WIPF1, PPP1R2, C4orf30, KIAA0888, TMEM106B, NR3c2, KLHL24, FLJ31306, MAP2K6, SATB1, WHDC1L1, EDG1, MBIP, RSU1, DYRK2, DOCK9, LOC283666, SLC4A7, ODF2L, PAQR8, or C14orf64 inputting, to a computer, test data representing a level of RNA encoded by the gene in blood of the test subject; and causing the computer to compare the test data to control data representing a level of RNA encoded by the gene in blood of human control subjects not having decompensated heart failure, and to determine whether the level of RNA encoded by the gene in blood of the test subject is lower than the level of RNA encoded by the gene in blood of human control subjects not having decompensated heart failure, wherein a determination that the level of RNA encoded by the gene in blood of the test subject is lower than the level of RNA encoded by the gene in blood of human control subjects not having decompensated heart failure is used to classify the test subject as having decompensated heart failure.

In another aspect, there is provided a method of determining whether a human subject with heart failure has a prognosis of mortality, the method comprising for each gene of a set of one or more of the genes set forth in Table 3 (a) determining a level of RNA encoded by the gene in blood of the test subject, thereby generating test data; (b) providing positive control data representing levels of RNA encoded by the gene in blood of human control subjects having a prognosis of mortality; and (c) comparing the levels of a) to b), wherein a correspondence between the test data and the positive control data indicates that the test subject has a prognosis of mortality. In one embodiment, the determining whether the test data corresponds to the positive control data is effected by applying to the test data a mathematical model derived from the positive control data, and wherein the mathematical model is for determining the whether a level of RNA encoded by the gene corresponds to the positive control data. In one embodiment, the set of one or more genes comprise FAM134B, MGAT4A, ZCCHC14 or CD28.

In yet another aspect, there is provided a computer-based method for determining whether a human subject with heart failure has a prognosis of mortality, the method comprising inputting, to a computer, test data representing a level of RNA encoded by one or more of the genes set forth in Table 3 in blood of the test subject; and causing the computer to compare the test data to a positive control data representing levels of RNA encoded by the gene in blood of human control subjects having a prognosis of mortality, wherein correspondence between the test data and the positive control data indicates that the test subject has the prognosis of mortality.

According to another aspect, there is provided a method of ranking two or more test subjects having heart failure according to risk of death, the method comprising for each gene of a set of one or more of the genes set forth in Table 3: (a) determining a level of RNA encoded by the gene in blood of each test subject, thereby generating test data; (b) calculating the risk score for each test subject based on the level of expression in (a); (c) ranking the risk scores of the test subjects, wherein the test subjects are ranked according to risk of death.

In yet a further aspect, there is provided a computer-based method of ranking two or more test subjects having heart failure according to risk of death, the method comprising for each gene of a set of one or more of FAM134B, MGAT4A, ZCCHC14 or CD28: inputting, to a computer, test data representing a level of RNA encoded by one or more of a FAM134B, MGAT4A, ZCCHC14 or CD28 gene in blood of each test subject; causing the computer to apply the test data to a relative risk equation; and causing the computer to rank the results of each test subject, wherein the computer provides a ranking of the test subjects based on the relative risk.

According to still another aspect of the invention there is provided a kit comprising packaging and containing, for each gene of a set of one or more of the genes listed in Table 2, a primer set capable of generating an amplification product of DNA complementary to RNA encoded, in a human subject, only by the gene. In one embodiment, the set of one or more genes comprises ASGR2, C3AR1 and/or STAB1. In another embodiment, the set of one or more genes comprises or consists of an ASGR2 gene and a STAB1 gene.

According to further features of the invention described below, the kit further comprises a computer-readable medium having instructions stored thereon that are operable when executed by a computer for comparing test data representing a level of RNA encoded by the gene in blood of a human test subject to a first positive control data representing levels of RNA encoded by the gene in blood of human control subjects having a first categorized severity of heart failure, to thereby output data representing a value indicating whether the test data and the positive control data correspond to each other, wherein correspondence between the test data and the first positive control data indicates that the test subject has the first categorized severity of heart failure.

In another embodiment, the computer readable medium further has instructions stored thereon that are operable when executed by a computer for comparing a second positive control data representing levels of RNA encoded by the gene in blood of human control subjects having a second categorized severity of heart failure, wherein correspondence between the test data and the second positive control data indicates that the test subject has the second categorized severity of heart failure.

In yet another aspect, there is provided a kit comprising packaging and containing, for each gene of a set of one or more of the genes set forth in Table 3, a primer set capable of generating an amplification product of DNA complementary to RNA encoded, in a human subject, only by the gene. In one embodiment, the one or more genes comprises FAM134B, MGAT4A, ZCCHC14 and/or CD28.

According to further features of the invention described below, the kit further comprises a thermostable polymerase, a reverse transcriptase, deoxynucleotide triphosphates, nucleotide triphosphates and/or enzyme buffer.

According to further features of the invention described below, the kit further comprises at least one labeled probe capable of selectively hybridizing to either a sense or an antisense strand of the amplification product.

According to further features of the invention described below, the level of RNA encoded by the gene in blood of the test subject is determined via quantitative reverse transcriptase-polymerase chain reaction analysis.

According to further features of the invention described below, the level of RNA encoded by the gene in blood of the test subject is determined by probing a microarray.

According to further features of the invention described below, the level of RNA encoded by the gene in blood of the test subject and the levels of RNA encoded by the gene in blood of the control subjects are determined via the same method.

In further aspects, there is provided isolated compositions, test systems and primer sets for use in the methods disclosed herein.

In one aspect, there is provided an isolated composition comprising, a blood sample from a test subject and for each gene of a set of one or more genes selected from the genes listed in Table 2, one or more components selected from the group consisting of exogenous RNA encoded by the gene, cDNA complementary to the RNA, an oligonucleotide which specifically hybridizes to the cDNA or the RNA under stringent conditions, a primer set capable of generating an amplification product of the cDNA complementary to RNA, and an amplification product of the cDNA. In one embodiment, the set of genes comprises or consists of an ASGR2 gene and a STAB1 gene.

In another aspect there is provided an isolated composition comprising, for each gene of a set of genes selected from the genes listed in Table 2, one or more components selected from the group consisting of: an exogenous isolated RNA encoded by the gene, cDNA complementary to the RNA, an oligonucleotide which specifically hybridizes to the cDNA or the RNA under stringent conditions, a primer set capable of generating an amplification product of the cDNA complementary to RNA, and an amplification product of the cDNA. In one embodiment, the set of genes comprises or consists of an ASGR2 gene and a STAB1 gene.

In a further aspect, there is provided a primer set comprising a first primer and a second primer, wherein the first primer is one of a set of primers capable of generating an amplification product of cDNA complementary to RNA encoded by a first gene, wherein the second primer is capable of generating an amplification product of cDNA complementary to RNA encoded by a second gene, and wherein the first gene and the second gene are different genes selected from the genes listed in Table 2, or composition thereof. In one embodiment, the set of genes comprises or consists of an ASGR2 gene and a STAB1 gene.

Other features and advantages of the present disclosure will become apparent from the following detailed description. It should be understood, however, that the detailed description and the specific examples while indicating preferred embodiments of the disclosure are given by way of illustration only, since various changes and modifications within the spirit and scope of the disclosure will become apparent to those skilled in the art from this detailed description.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure will now be described in relation to the drawings in which:

FIG. 1 shows genes differentially regulated in heart failure. A total of 243 unique known genes were identified. The dendrogram was constructed using average linkage as the distance measurement and Pearson correlation as the similarity measurement.

FIG. 2 is a graphical depiction functionally categorizing genes differentially regulated in heart failure.

FIG. 3 shows the pathway of T cell receptor signalling. Heart failure (HF)-regulated genes are marked in grey.

FIG. 4 shows an exemplary computer system.

DETAILED DESCRIPTION OF THE DISCLOSURE

As will become apparent, preferred features and characteristics of one aspect are applicable to any other aspect. It should be noted that, as used herein, the singular form “a”, “an” and “the” include plural references unless the context clearly dictates otherwise.

The term “encode” as used herein means that a polynucleotide, including a gene, is said to “encode” a RNA and/or polypeptide if, in its native state or when manipulated by methods well known to those skilled in the art, it can be transcribed and/or translated to produce the mRNA for and/or the polypeptide or a fragment thereof. The anti-sense strand is the complement of such a nucleic acid, and the encoding sequence can be deduced there from.

The term “label” as used herein refers to a composition capable of producing a detectable signal indicative of the presence of the target polynucleotide in an assay sample. Suitable labels include radioisotopes, nucleotide chromophores, enzymes, substrates, fluorescent molecules, chemiluminescent moieties, magnetic particles, bioluminescent moieties, and the like. As such, a label is any composition detectable by spectroscopic, photochemical, biochemical, immunochemical, electrical, optical or chemical means.

As used herein, a “sample” refers to a sample of tissue or fluid isolated from an individual, including but not limited to, for example, blood, plasma, serum, tumor biopsy, urine, stool, sputum, spinal fluid, pleural fluid, nipple aspirates, lymph fluid, the external sections of the skin, respiratory, intestinal, and genitourinary tracts, tears, saliva, milk, cells (including but not limited to blood cells), organs, and also samples of in vitro cell culture constituent.

The term “gene” as used herein is a polynucleotide which may include coding sequences, intervening sequences and regulatory elements controlling transcription and/or translation. Genes of the disclosure include normal alleles of the gene encoding polymorphisms, including silent alleles having no effect on the amino acid sequence of the gene's encoded polypeptide as well as alleles leading to amino acid sequence variants of the encoded polypeptide that do not substantially affect its function. These terms also may optionally include alleles having one or more mutations which affect the function of the encoded polypeptide's function.

The polynucleotide compositions, such as primers, of this disclosure include RNA, cDNA, DNA complementary to target cDNA of this invention or portion thereof, genomic DNA, unspliced RNA, spliced RNA, alternately spliced RNA, synthetic forms, and mixed polymers, both sense and antisense strands, and may be chemically or biochemically modified or may contain non-natural or derivatized nucleotide bases, as will be readily appreciated by those skilled in the art.

Where nucleic acid according to the disclosure includes RNA, reference to the sequence shown should be construed as reference to the RNA equivalent, with U substituted for T.

The term “amount” or “level” of RNA encoded by a gene described herein encompasses the absolute amount of the RNA, the relative amount or concentration of the RNA, as well as any value or parameter which correlates thereto.

The methods of nucleic acid isolation, amplification and analysis are routine for one skilled in the art and examples of protocols can be found, for example, in the Molecular Cloning: A Laboratory Manual (3-Volume Set) Ed. Joseph Sambrook, David W. Russel, and Joe Sambrook, Cold Spring Harbor Laboratory; 3rd edition (Jan. 15, 2001), ISBN: 0879695773. Particularly useful protocol source for methods used in PCR amplification is PCR (Basics: From Background to Bench) by M. J. McPherson, S. G. Moller, R. Beynon, C. Howe, Springer Verlag; 1st edition (Oct. 15, 2000), ISBN: 0387916008.

“Heart failure” as used herein means a condition that impairs the ability of the heart to fill with blood or pump a sufficient amount of blood through the body resulting from a structural or functional cardiac disorder. Heart failure may be interchangeably referred to as congestive heart failure (CHF) or congestive cardiac failure (CCF). Stages of heart failure may be defined using any one of various classification systems known in the art. For example, heart failure may be classified using the New York Heart Association (NYHA) classification system. According to the NYHA classification system, there are 4 main classes of heart failure; NYHA stage I (NYHA I) heart failure, NYHA stage II (NYHA II) heart failure, NYHA stage III (NYHA III) heart failure and NYHA stage IV (NYHA IV) heart failure. These stages classify heart failure according to the following: NYHA I: No symptoms and no limitation in ordinary physical activity; NYHA II: Mild symptoms (mild shortness of breath and/or angina pain) and slight limitation during ordinary activity; NYHA III: Marked limitation in activity due to symptoms, even during less-than-ordinary activity (e.g. walking short distances, about 20 to 100 meters). Comfortable only at rest; NYHA IV: Severe limitations. Symptoms are experienced even while at rest, mostly bedbound patients.

As used herein, “Compensated heart failure” corresponds to NYHA I/NYHA II heart failure.

As used herein, “Decompensated heart failure” corresponds to NYHA III/NYHA IV heart failure.

A “control population” refers to a defined group of individuals or a group of individuals with or without heart failure or with a particular heart failure classification, and may optionally be further identified by, but not limited to geographic, ethnic, race, gender, one or more other conditions or diseases, and/or cultural indices. In most cases a control population may encompass at least 10, 50, 100, 1000, or more individuals.

“Positive control data” encompasses data representing levels of RNA encoded by a target gene of the invention in each of one or more subjects having heart failure or a particular heart failure classification, and encompasses a single data point representing an average level of RNA encoded by a target gene of the invention in a plurality of subjects having heart failure or the particular heart failure classification.

“Negative control data” encompasses data representing levels of RNA encoded by a target gene of the invention in each of one or more subjects not having heart failure, and encompasses a single data point representing an average level of RNA encoded by a target gene of the invention in a plurality of subjects not having heart failure.

The probability that test data “corresponds” to positive control data or negative control data refers to the probability that the test data is more likely to be characteristic of data obtained in subjects having heart failure or the particular heart failure classification than in subjects not having any heart failure or the particular heart failure classification, or is more likely to be characteristic of data obtained in subjects not having any heart failure or the particular heart failure classification than in subjects having heart failure or the particular heart failure classification, respectively.

A gene expression profile for heart failure or a particular heart failure classification found in blood at the RNA level of one or more genes listed in Table 2 or Table 3 can be identified or confirmed using many techniques, including but preferably not limited to PCR methods, as for example discussed further in the working examples herein, Northern analyses and the microarray technique. This gene expression profile can be measured in a bodily sample, such as blood, using microarray technology. In an embodiment of this method, fluorescently labeled cDNA probes may be generated through incorporation of fluorescent nucleotides by reverse transcription of RNA extracted from blood. Labeled cDNA probes applied to the chip hybridize with specificity to each spot of DNA on the array. Quantitation of hybridization of each arrayed element allows for assessment of corresponding mRNA abundance. For example, with dual color fluorescence, separately labeled cDNA probes generated from two sources of RNA are hybridized pair wise to the array. The relative abundance of the transcripts from the two sources corresponding to each specified gene is thus determined simultaneously. Such methods have been shown to have the sensitivity required to detect rare transcripts, which are expressed at a few copies per cell, and to reproducibly detect at least approximately two-fold differences in the expression levels (Schena et al., Proc. Natl. Acad. Sci. USA 93(2):106-149 (1996)). Microarray analysis can be performed by commercially available equipment, following manufacturer's protocols, such as by using the Affymetrix GeneChip technology, or Incyte's microarray technology.

Methods

According to one aspect, there is provided a method of determining a severity of heart failure in a human test subject. The method comprises, for each gene of a set of one or more genes listed in Table 2, a step of providing test data representing a level of RNA encoded by the gene in blood of the test subject and providing positive control data representing levels of RNA encoded by the gene in blood of human control subjects having a categorized severity of heart failure. The method comprises a subsequent step of comparing the level of RNA in blood of the test subject to the levels in blood of control subjects to thereby determine a value indicating whether the test data corresponds to the positive control data, where a correspondence between the test data and the positive control data indicates that the test subject has the categorized severity of heart failure.

In one embodiment, the test data is provided by determining a level of RNA encoded by the gene in blood of the test subject, and/or the positive control data is provided by determining levels of RNA encoded by the gene in blood of human subjects having the categorized severity of heart failure.

In another embodiment, comparing the level of RNA encoded by the gene in blood of the test subject to the levels in blood of control subjects is effected by inputting, to a computer, the test data, where the computer is for comparing data representing a level of RNA encoded by the gene in blood of a human subject to levels of RNA encoded by the gene in subjects having the categorized severity of heart failure, to thereby output a value indicating whether the test data corresponds to the positive control data; and causing the computer to compare the test data to the positive control data, to thereby output the value indicating whether the test data corresponds to the positive control data.

According to another aspect, there is provided a method of determining whether a human test subject has heart failure as opposed to not having heart failure, the method comprising for each gene of a set of one or more of the genes listed in Table 2: (a) determining a level of RNA encoded by the gene in blood of the test subject, thereby generating test data; (b) providing a positive control data representing levels of RNA encoded by the gene in blood of human control subjects having heart failure and a negative control data representing levels of RNA encoded by the gene in blood of human control subject not having heart failure; and (c) comparing the levels of a) and b) to determine whether the test data corresponds to the positive control data or the negative control data; wherein a correspondence between the test data and the positive control data and not the negative control data indicates that the test subject has heart failure.

According to yet another aspect, there is provided a method of determining a severity of heart failure in a human test subject, the method comprising for each gene of a set of one or more of the genes listed in Table 2: (a) determining a level of RNA encoded by the gene in blood of the test subject, thereby generating test data; (b) providing a first positive control data representing levels of RNA encoded by the gene in blood of human control subjects having a first categorized severity of heart failure; and (c) comparing the levels of a) and b) to determine whether the test data corresponds to the positive control data; wherein a correspondence between the test data and the positive control data indicates that the test subject has the first categorized severity of heart failure.

In an embodiment, the set of genes comprises or consists of ASGR2 and STAB1.

In another embodiment, the set of genes comprises or consists of ASGR2, C3AR1 and/or STAB1.

In one embodiment, the first categorized severity is compensated heart failure or decompensated heart failure.

In another embodiment, the method further comprises providing a second positive control data representing levels of RNA encoded by the gene in blood of human control subjects having a second categorized severity of heart failure, wherein correspondence between the test data and the second positive control data indicates that the test subject has the second categorized severity of heart failure. In one embodiment, the first categorized severity is compensated heart failure and the second categorized severity is decompensated heart failure. In such an embodiment, the method allows determination of the likelihood that a particular heart failure patient falls within a compensated heart failure class or a decompensated heart failure class, which is relevant to types of treatment available to the subject.

In an embodiment, the method further comprises determining levels of RNA encoded by the gene in blood of a population of human subjects having the first categorized severity of heart failure, thereby providing the positive control data representing the levels of RNA encoded by the gene in blood of human control subjects having the first categorized severity of heart failure. In yet another embodiment, the method further comprises determining levels of RNA encoded by the gene in blood of a population of human subjects having the second categorized severity of heart failure, thereby providing the positive control data representing the levels of RNA encoded by the gene in blood of human control subjects having the second categorized severity of heart failure.

In a further embodiment, the method further comprises providing a third control data representing levels of RNA encoded by the gene in blood of human control subjects which are healthy, and wherein step c) is effected by comparing the test data to the first or second positive control data and the third control data, wherein correspondence with the first or second positive control data and not the third control data indicates that the test subject has the first or second categorized severity of heart failure.

In a further aspect, there is provided a method of monitoring the progression of heart failure in a human subject, the method comprising for each gene of a set of one or more of ASGR2, C3AR1, STAB1, KRCC1, KYNU, LOH11CR2A, TMEM144, FKBP1B, VCAN, LTA4H, MGST1, or NOTCH2NL (a) determining a level of RNA encoded by the gene in blood of the subject at a first time point; (b) determining a level of RNA encoded by an ASGR2 gene in blood of the subject at a second time point; (c) comparing the levels in a) and b); wherein an increased level at the second time point indicates a progression of heart failure. In an embodiment, the one or more genes comprise or consist of ASGR2, C3AR1 and/or STAB1.

In a further aspect, there is provided a method of classifying a human test subject as having decompensated heart failure, the method comprising (a) determining a level of RNA encoded by each gene of a set of one or more of ASGR2, C3AR1, STAB1, KRCC1, KYNU, LOH11CR2A, TMEM144, FKBP1B, VCAN, LTA4H, MGST1, or NOTCH2NL in blood of the test subject, thereby generating test data; (b) providing control data representing a level of RNA encoded by the gene in blood of human control subjects not having decompensated heart failure; and (c) comparing the test data to the control data, wherein a determination in step (c) that the level of RNA encoded by the gene in blood of the test subject is higher than the level of RNA encoded by the gene in blood of human control subjects not having decompensated heart failure is used to classify the test subject as having decompensated heart failure.

Determining whether the level of RNA of a gene in blood of the test subject is higher than the level of RNA encoded by the gene in blood of control subjects not having heart failure or in the same subject at a different time point may be effected by determining whether there is a fold-change in the level between the test subject and the control subject or different time point which is higher than a minimum fold-change and/or which is within a range of fold-changes.

Determining whether the level of RNA of a gene in blood of the test subject is lower than the level of RNA encoded by the gene in blood of control subjects not having heart failure or in the same subject at a different time point may be effected by determining whether there is a fold-change in the level between the test subject and the control subject or different time point which is lower than a maximum fold-change and/or which is within a range of fold-changes.

For levels of RNA encoded by a given gene, to classify a test subject as NYHA I-II, a suitable minimum fold-change is the fold-change value corresponding to NYHA I-II/control set forth in Table 2, and a suitable range of fold-changes is the fold-change value corresponding to NYHA I-II/control set forth in Table 2 to the fold-change value corresponding to NYHA III-IV/control set forth in Table 2, where control corresponds to an average level of RNA encoded by the gene in blood of healthy subjects. To classify a test subject as NYHA III-IV, a suitable minimum fold-change value is the fold-change value greater than or equal to the NYHA-III-IV/control set forth in Table 2.

Examples of suitable fold-changes and ranges of fold-changes for classifying a test subject are provided in Table 2, and include the following ones. The methods recited in the above and below paragraphs can be done with “about” the cited amounts.

For levels of RNA encoded by ASGR2, to classify a test subject as NYHA-I-II, a suitable minimum fold-change is 1.5 fold, and a suitable range of fold-changes is 1.59 to 2.45 fold, relative to an average level of RNA encoded by the gene in blood of healthy subjects. To classify a test subject as NYHA-III-IV, a suitable minimum fold-change is greater than or equal to 2.45, relative to an average level of RNA encoded by the gene in blood of healthy subjects.

For levels of RNA encoded by C3AR1, to classify a test subject as NYHA-I-II, a suitable minimum fold-change is 1.05 fold, and a suitable range of fold-changes is 1.05 to 1.95 fold, relative to an average level of RNA encoded by the gene in blood of healthy subjects. To classify a test subject as NYHA-III-IV, a suitable minimum fold-change is greater than or equal to 1.95, relative to an average level of RNA encoded by the gene in blood of healthy subjects.

For levels of RNA encoded by STAB1, to classify a test subject as NYHA-I-II, a suitable minimum fold-change is 1.33 fold, and a suitable range of fold-changes is 1.33 to 1.92 fold, relative to an average level of RNA encoded by the gene in blood of healthy subjects. To classify a test subject as NYHA-III-IV, a suitable minimum fold-change is greater than or equal to 1.92, relative to an average level of RNA encoded by the gene in blood of healthy subjects.

As used herein, the term “about” refers to a variability of plus or minus 10 percent.

Thus, a test subject is classified or determined as having or being more likely to have heart failure or a particular heart failure classification than to not have it if, for each marker gene of the particular set of marker genes used to practice the method of classifying or determining, the fold-change in level of RNA encoded by that gene in blood of the test subject relative to blood of the control subjects not having heart failure or the particular heart failure classification, classifies or determines that the test subject has or is more likely to have heart failure or the particular heart failure classification than to not have it.

Conversely, a test subject of the invention is classified or determined as having or being more likely to not have heart failure or the particular heart failure classification if, for each marker gene of the particular set of marker genes used to practice the method of classifying or determining, the fold-change in level of RNA encoded by that gene in blood of the test subject relative to blood of the control subjects does not classify or determine the test subject as having or being more likely to have heart failure or the particular heart failure classification than to not have it.

In one aspect, the set of one or more heart failure marker genes may consist of any one of the possible combinations of one or more of the genes set out in Table 2. In an embodiment, the one or more heart failure marker genes comprise or consist of ASGR2, C3AR1 and/or STAB1.

In a further aspect, there is provided a method of determining whether a human subject with heart failure has a prognosis of mortality, the method comprising for each gene of a set of one or more of the genes set forth in Table 3 (a) determining a level of RNA encoded by the gene in blood of the test subject, thereby generating test data; (b) providing positive control data representing levels of RNA encoded by the gene in blood of human control subjects having a prognosis of mortality; and (c) comparing the levels of a) to b), wherein a correspondence between the test data and the positive control data indicates that the test subject has a prognosis of mortality. In one embodiment, the determining whether the test data corresponds to the positive control data is effected by applying to the test data a mathematical model derived from the positive control data, and wherein the mathematical model is for determining the whether a level of RNA encoded by the gene corresponds to the positive control data. In an embodiment, the one or more heart failure marker genes comprise or consist of FAM134B, MGAT4A, ZCCHC14 or CD28

In yet a further aspect, there is provided a method of ranking two or more test subjects having heart failure according to risk of death, the method comprising for each gene of a set of one or more of the genes set forth in Table 3: (a) determining a level of RNA encoded by the gene in blood of each test subject, thereby generating test data; (b) calculating the risk score for each test subject based on the level of expression in (a); (c) ranking the risk scores of the test subjects, wherein the test subjects are ranked according to risk of death.

In one embodiment, the gene is FAM134B and the equation for calculating the relative risk for this gene is 0.192̂Expression. In another embodiment, the gene is MGAT4A and the equation for calculating the relative risk for this gene is 0.206̂Expression. In yet another embodiment, the gene is ZCCHC14 and the relative risk for this gene is 0.440̂Expression. In a further embodiment, the gene is CD28 and the equation for calculating the relative risk for this gene is 0.451̂Expression. “Expression” in the relative risk equations refers to blood RNA levels in log scale for the gene in a test subject, determined, e.g. as described in the Materials and Methods. These equations were derived using the Cox method described herein. The symbol “̂” indicates, according to convention, that the indicated gene-specific numerical coefficient is raised to an exponent corresponding to the value of the RNA level.

In an aspect of the invention, the level of RNA encoded by the gene in blood of the test subject and/or the levels in blood of positive control subjects are relative to a level of RNA encoded by the gene in blood of healthy test subjects. Thus, in one embodiment, the level of RNA encoded by the gene in blood of the test subject is determined as a ratio to a level of RNA encoded by the gene in blood of a healthy test subject. Thus, in another embodiment, the levels of RNA encoded by the gene in blood of control subjects are determined as a ratio to a level of RNA encoded by the gene in blood of a healthy test subject.

It will be appreciated that data representing levels of RNA encoded by a set of genes of the disclosure may be combined with data representing levels of gene products of other genes which are differently expressed in blood in subjects having heart failure relative to subjects not having any heart failure so as to determine a probability that a test subject has heart failure versus not having heart failure, or for the purposes of classifying the stage of heart failure.

In another aspect, the method further comprises determining levels of RNA encoded by the gene in blood of a population of control human subjects having heart failure, and/or in blood of a population of human control subjects not having heart failure, to thereby provide the positive control data and/or the negative control data, respectively. Alternately, it is envisaged that the level of RNA encoded by a gene of the invention in control subjects of the invention could be provided by prior art data corresponding to control data. In one embodiment, there is provided a first positive control data derived from subjects having a first categorized severity of heart disease, optionally, compensated or decompensated heart failure. In another embodiment, there is a first and second positive control data and the first positive control data is derived from subjects having compensated heart failure and the second positive control data is derived from subjects having decompensated heart failure.

The method may be practiced using any one of various types of control subjects.

In an aspect, the control subjects not having heart failure are subjects having been diagnosed as not having any heart failure as a result of routine examination. As is described in the Examples section which follows, the method of the invention may be practiced using subjects not having heart failure as the control subjects not having heart failure.

The methods described herein may furthermore be practiced using any one of various numbers of control subjects. One of ordinary skill in the art will possess the necessary expertise to select a sufficient number of control subjects so as to obtain control data having a desired statistical significance for practicing the method of the invention with a desired level of reliability.

For example, the method can be practiced using 10 or more, 20 or more, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 10 or more, 20 or more, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 110 or more, 120 or more, 130 or more, 140 or more, 150 or more, 160 or more, 170 or more, 180 or more, 190 or more, or 200 or more of control subjects having heart failure and/or a particular classification of heart failure and/or of control subjects not having heart failure.

In one aspect of the invention, the level of RNA encoded by a gene in blood of the test subject and the levels of RNA encoded by the gene in blood of the control subjects are determined via the same method. As is described in the Examples section, below, the method can be practiced where the level of RNA encoded by a gene in blood of the test subject and the levels of RNA encoded by the gene in blood of the control subjects are determined via the same method. Alternately, it is envisaged that the level of a gene in blood of a test subject of the invention and in blood of control subjects of the invention could be determined using different methods. It will be appreciated that use of the same method to determine the levels of RNA encoded by a gene of the invention in a test subject and in control subjects can be used to avoid method-to-method calibration to minimize any variability which might arise from use of different methods.

In one aspect, determining of the level of RNA encoded by a gene of the invention in blood of a subject of the invention is effected by determining the level of RNA encoded by the gene in a blood sample isolated from the subject. Alternately, it is envisaged that determination of the level of RNA encoded by the gene in blood of a subject of the invention could be effected by determining the level of RNA encoded by the gene in an in-vivo sample using a suitable method for such a purpose.

In one aspect, the level of RNA encoded by a gene in blood of a subject is determined in a sample of RNA isolated from blood of the subject.

Alternately, it is envisaged that the level of RNA of a gene in blood of a subject could be determined in a sample which includes RNA of blood of the subject but from which RNA has not been isolated therefrom, using a suitable method for such a purpose.

Any one of various methods routinely employed in the art for isolating RNA from blood may be used to isolate RNA from blood of a subject, so as to enable practicing of the methods described herein.

In one aspect, the level of RNA encoded by a gene in blood of a subject is determined in RNA of a sample of whole blood. Any one of various methods routinely employed in the art for isolating RNA from whole blood may be employed for practicing the method.

Alternately, it is envisaged that the level of RNA encoded by a gene in blood of a subject could be determined in RNA of a sample of fraction of blood which expresses the gene sufficiently specifically so as to enable the method. Examples of such blood fractions include preparations of isolated types of leukocytes, preparations of isolated peripheral blood mononuclear cells, preparations of isolated granulocytes, preparations of isolated whole leukocytes, preparations of isolated specific types of leukocytes, plasma-depleted blood, preparations of isolated lymphocytes, and the plasma fraction of blood.

In one aspect of the method, isolation of RNA from whole blood of a subject of the invention is effected using EDTA tubes, as described in the Examples section.

In another aspect of the method, isolation of RNA from whole blood of a subject of the invention may be effected by using a PAXgene Blood RNA Tube (obtainable from PreAnalytiX) in accordance with the instructions of the PAXgene Blood RNA Kit protocol.

Determination of a level of RNA encoded by a gene in a sample of the invention may be effected in any one of various ways routinely practiced in the art.

For example, the level of RNA encoded by a gene in a sample may be determined via any one of various methods based on quantitative polynucleotide amplification which are routinely employed in the art for determining a level of RNA encoded by a gene in a sample.

Alternatively, the level of RNA encoded by a gene may be determined via any one of various methods based on quantitative polynucleotide hybridization to an immobilized probe which are routinely employed in the art for determining a level of RNA encoded by a gene in a sample.

In one aspect of the methods described herein, quantitative polynucleotide amplification used to determine the level of RNA encoded by a gene is quantitative reverse transcriptase-polymerase chain reaction (PCR) analysis. Any one of various types of quantitative reverse transcriptase-PCR analyses routinely employed in the art to determine the level of RNA encoded by a gene in a sample may be used to practice the methods. For example, any one of various sets of primers may be used to perform quantitative reverse transcriptase-PCR analysis so as to practice the methods.

In one aspect, the quantitative reverse transcriptase-PCR analysis used to determine the level of RNA encoded by a gene is quantitative real-time PCR analysis of DNA complementary to RNA encoded by the gene using a labeled probe capable of specifically binding amplification product of DNA complementary to RNA encoded by the gene. For example, quantitative real-time PCR analysis may be performed using a labeled probe which comprises a polynucleotide capable of selectively hybridizing with a sense or antisense strand of amplification product of DNA complementary to RNA encoded by the gene. Labeled probes comprising a polynucleotide having any one of various nucleic acid sequences capable of specifically hybridizing with amplification product of DNA complementary to RNA encoded by the gene may be used to practice the methods described herein.

Quantitative real-time PCR analysis of a level of RNA encoded by a gene may be performed in any one of various ways routinely employed in the art.

In one aspect, quantitative real-time PCR analysis is performed by analyzing complementary DNA prepared from RNA of blood a subject of the invention, using the QuantiTect™ Probe RT-PCR system (Qiagen, Valencia, Calif.; Product Number 204345), a TaqMan dual labelled probe, and a Real-Time PCR System 7500 instrument (Applied Biosystems).

As specified above, the level of RNA encoded by a gene may be determined via a method based on quantitative polynucleotide hybridization to an immobilized probe.

In one aspect, determination of the level of RNA encoded by a gene via a method based on quantitative polynucleotide hybridization is effected using a microarray, such as an Affymetrix U133Plus 2.0 GeneChip oligonucleotide array (Affymetrix; Santa Clara, Calif.).

As specified above, the level of RNA encoded by a gene in a sample of the invention may be determined via quantitative reverse transcriptase-PCR analysis using any one of various sets of primers and labeled probes to amplify and quantitate DNA complementary to RNA encoded by a marker gene produced during such analysis. Examples of suitable primers for use in quantitative reverse transcriptase-PCR analysis of the level of RNA encoded by a target gene are within the knowledge of a person skilled in the art.

In one aspect, the primers may be selected so as to include a primer having a nucleotide sequence which is complementary to a region of a target cDNA template, where the region spans a splice junction joining a pair of exons. It will be appreciated that such a primer can be used to facilitate amplification of DNA complementary to messenger RNA, i.e. mature spliced RNA.

It will be appreciated that the probability that the test subject does not have any heart failure as opposed to having heart failure can be readily determined from the probability that the test subject has heart failure as opposed to not having heart failure. For example, when expressing the probability that the test subject has heart failure as a percentage probability, the probability that the test subject does not have any heart failure as opposed to having heart failure corresponds to 100 percent minus the probability that the test subject does not have any heart failure as opposed to having heart failure.

Determining the probability that the test data corresponds to positive control data and not to the negative control data may be effected in any one of various ways known to the ordinarily skilled artisan for determining the probability that a gene expression profile of a test subject corresponds to a gene expression profile of subjects having a pathology and not to a gene expression profile of subjects not having the pathology, where the gene expression profiles of the subjects having the pathology and the subjects not having the pathology are significantly different.

In one aspect of the method, determining the probability that the test data corresponds to the positive control data and not to the negative control data is effected by applying to the test data a mathematical model derived from the positive control data and from the negative control data.

In another aspect, determining whether the test data corresponds to positive control data may be effected in any one of various ways known to the ordinarily skilled artisan for determining whether a gene expression profile of a test subject corresponds to a gene expression profile of subjects having a pathology, where the gene expression profiles of the subjects having the pathology and the subjects not having the pathology are significantly different.

In one aspect, determining whether the test data corresponds to the positive control data is effected by applying to the test data a mathematical model derived from the positive control data.

Various suitable mathematical models which are well known in the art of medical diagnosis using disease markers may be employed to compare test data to control data so as to classify, according to the present teachings, a test subject as more likely to have or having heart failure or a particular heart failure classification than to not have heart failure or the particular classification, to determine a probability that a test subject is likely to have heart failure or a particular heart failure classification as opposed to not having heart failure or the particular classification, or to diagnose a test subject as having colorectal cancer according to the teachings described herein. Generally these mathematical models can be unsupervised methods performing a clustering whilst supervised methods are more suited to classification of datasets. (refer, for example, to: Dreiseitl S, Ohno-Machado L. Logistic regression and artificial neural network classification models: a methodology review. J Biomed Inform. 2002 October-December; 35(5-6):352-9; Pepe M S. The Statistical Evaluation of Medical Tests for Classification and Prediction. Oxford, England: Oxford University Press; 2003; Dupont WD. Statistical Modeling for Biomedical Researchers. Cambridge, England: Cambridge University Press; 2002; Pampel FC. Logistic regression: A Primer. Publication # 07-132, Sage Publications: Thousand Oaks, Calif. 2000; King E N, Ryan T P. A preliminary investigation of maximum likelihood logistic regression versus exact logistic regression. Am Statistician 2002; 56:163-170; Metz C E. Basic principles of ROC analysis. Semin Nucl Med 1978; 8:283-98; Swets J A. Measuring the accuracy of diagnostic systems. Science 1988; 240:1285-93; Zweig M H, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem 1993; 39:561-77; Witten I H, Frank Eibe. Data Mining: Practical Machine Learning Tools and Techniques (second edition). Morgan Kaufman 2005; Deutsch J M. Evolutionary algorithms for finding optimal gene sets in microarray prediction. Bioinformatics 2003; 19:45-52; Niels Landwehr, Mark Hall and Eibe Frank (2003) Logistic Model Trees. pp 241-252 in Machine Learning: ECML 2003: 14th European Conference on Machine Learning, Cavtat-Dubrovnik, Croatia, Sep. 22-26, 2003, Proceedings Publisher: Springer-Verlag GmbH, ISSN: 0302-9743). Examples of such mathematical models, related to learning machine, include: Random Forests methods, logistic regression methods, neural network methods, k-means methods, principal component analysis methods, nearest neighbour classifier analysis methods, linear discriminant analysis, methods, quadratic discriminant analysis methods, support vector machine methods, decision tree methods, genetic algorithm methods, classifier optimization using bagging methods, classifier optimization using boosting methods, classifier optimization using the Random Subspace methods, projection pursuit methods, genetic programming and weighted voting methods.

Computer-Based Methods

It will be appreciated that a computer may be used for determining the probability that the test subject has heart failure or a particular classification using a mathematical model, according to the methods described herein.

Thus, according to another aspect of the invention there is provided a computer-based method of determining a severity of heart failure in a human test subject. Accordingly, there is provided a computer-based method of determining a severity of heart failure in a human test subject, the method comprising inputting, to a computer, test data representing a level of RNA encoded by one or more of the genes listed in Table 2 in blood of the test subject; and causing the computer to compare the test data to a first positive control data representing levels of RNA encoded by the gene in blood of human control subjects having a first categorized severity of heart failure, wherein correspondence between the test data and the first positive control data indicates that the test subject has the first categorized severity of heart failure. In one embodiment, the one or more genes is ASGR2, C3AR1 and/or STAB1.

In another aspect, there is provided computer-based method of monitoring the progression of heart failure in a human subject. Accordingly, there is provided a computer-based method of monitoring the progression of heart failure in a human subject, the method comprising inputting, to a computer, test data representing a level of RNA encoded by one or more of ASGR2, C3AR1, STAB1, KRCC1, KYNU, LOH11CR2A, TMEM144, FKBP1B, VCAN, LTA4H, MGST1, or NOTCH2NL gene in blood of the subject at a first and second time point; and causing the computer to compare the data of the first time point to the data of the second time point, and to determine whether the level of RNA encoded by the gene in blood of the subject is increased at the second time point compared to the level of RNA encoded by the gene in blood of the subject at the first time point, wherein a determination that the level of RNA encoded by the gene in blood of the test subject is increased at the second time point indicates the progression of heart failure. In an additional aspect, there is provided a computer-based method of monitoring the progression of heart failure in a human subject, the method comprising inputting, to a computer, test data representing a level of RNA encoded by one or more of FAM84B, RBL2, XRCC5, TNRC6C, ZBTB44, HERC1, SNORA72, WIPF1, PPP1R2, C4orf30, KIAA0888, TMEM106B, NR3c2, KLHL24, FLJ31306, MAP2K6, SATB1, WHDC1L1, EDG1, MBIP, RSU1, DYRK2, DOCK9, LOC283666, SLC4A7, ODF2L, PAQR8, or C14orf64 gene or the genes listed in Table 4 in blood of the subject at a first and second time point; and causing the computer to compare the data of the first time point to the data of the second time point, and to determine whether the level of RNA encoded by the gene in blood of the subject is decreased at the second time point compared to the level of RNA encoded by the gene in blood of the subject at the first time point, wherein a determination that the level of RNA encoded by the gene in blood of the test subject is decreased at the second time point indicates the progression of heart failure.

According to another aspect of the invention there is provided a computer-based method of classifying a human test subject as having decompensated heart failure. Accordingly, there is provided a computer-based method of classifying a human test subject as having decompensated heart failure, the method comprising for each gene of a set of one or more of ASGR2, C3AR1, STAB1, KRCC1, KYNU, LOH11CR2A, TMEM144, FKBP1B, VCAN, LTA4H, MGST1, or NOTCH2NL inputting, to a computer, test data representing a level of RNA encoded by a STAB1 gene in blood of the test subject; and causing the computer to compare the test data to control data representing a level of RNA encoded by the gene in blood of human control subjects not having decompensated heart failure, and to determine whether the level of RNA encoded by the gene in blood of the test subject is higher than the level of RNA encoded by the gene in blood of human control subjects not having decompensated heart failure, wherein a determination that the level of RNA encoded by the gene in blood of the test subject is higher than the level of RNA encoded by the gene in blood of human control subjects not having decompensated heart failure is used to classify the test subject as having decompensated heart failure. In yet a further aspect, there is provided a computer-based method of classifying a human test subject as having decompensated heart failure, the method comprising for each gene of a set of one or more of FAM84B, RBL2, XRCC5, TNRC6C, ZBTB44, HERC1, SNORA72, WIPF1, PPP1R2, C4orf30, KIAA0888, TMEM106B, NR3c2, KLHL24, FLJ31306, MAP2K6, SATB1, WHDC1L1, EDG1, MBIP, RSU1, DYRK2, DOCK9, LOC283666, SLC4A7, ODF2L, PAQR8, or C14orf64 inputting, to a computer, test data representing a level of RNA encoded by the gene in blood of the test subject; and causing the computer to compare the test data to control data representing a level of RNA encoded by the gene in blood of human control subjects not having decompensated heart failure, and to determine whether the level of RNA encoded by the gene in blood of the test subject is lower than the level of RNA encoded by the gene in blood of human control subjects not having decompensated heart failure, wherein a determination that the level of RNA encoded by the gene in blood of the test subject is lower than the level of RNA encoded by the gene in blood of human control subjects not having decompensated heart failure is used to classify the test subject as having decompensated heart failure.

In yet another aspect, there is provided a computer-based method of determining whether a human test subject with heart failure has a prognosis of mortality. Accordingly, there is provided a computer-based method for determining whether a human subject with heart failure has a prognosis of mortality, the method comprising inputting, to a computer, test data representing a level of RNA encoded by one or more of the genes set forth in Table 3 in blood of the test subject; and causing the computer to compare the test data to a positive control data representing levels of RNA encoded by the gene in blood of human control subjects having a prognosis of mortality, wherein correspondence between the test data and the positive control data indicates that the test subject has the prognosis of mortality. In one embodiment, the one or more genes is FAM134B, MGAT4A, ZCCHC14 or CD28.

In yet a further aspect, there is provided a computer-based method of ranking two or more test subjects having heart failure according to risk of death, the method comprising for each gene of a set of one or more of the genes set forth in Table 3: inputting, to a computer, test data representing a level of RNA encoded by the gene in blood of each test subject; causing the computer to apply the test data to a relative risk equation for assigning a risk score to a test subject based on the level of RNA; and causing the computer to rank the risk score of the test subjects, thereby ranking the test subjects according to risk of death.

In one embodiment, the gene is FAM134B and the equation for calculating the relative risk for this gene is 0.192̂Expression. In another embodiment, the gene is MGAT4A and the equation for calculating the relative risk for this gene is 0.206̂Expression. In yet another embodiment, the gene is ZCCHC14 and the relative risk for this gene is 0.440̂Expression. In a further embodiment, the gene is CD28 and the equation for calculating the relative risk for this gene is 0.451̂Expression. “Expression” in the relative risk equations refers to the log of blood RNA levels for the gene in a test subject, determined, e.g. as described in the Materials and Methods. These equations were derived using the Cox method described herein. The symbol “̂” indicates, according to convention, that the indicated gene-specific numerical coefficient is raised to an exponent corresponding to the value of the RNA level.

Application of computers for determining a probability or whether a test subject has a disease as opposed to not having the disease, so as to enable the method, is routinely practiced in the art using computer systems, and optionally computer-readable media, routinely used in the art.

Thus, according to a further aspect of the invention there is provided a computer system for providing the probability or determining that the test subject has heart failure or a particular classification as opposed to not having heart failure or the particular classification. The computer system comprises a processor; and a memory configured with instructions that cause the processor to provide a user with the probability or answer, where the instructions comprise applying a mathematical model to test data, to thereby determine the probability or whether the test subject has heart failure or the particular classification as opposed to not having heart failure or the particular classification.

The instructions may be provided to the computer in any one of various ways routinely employed in the art. In one aspect, the instructions are provided to the computer using a computer-readable medium.

Thus, according to yet another aspect of the invention there is provided a computer-readable medium having instructions stored thereon that are operable when executed by a computer for applying a mathematical model to test data, thereby determine the probability or whether a test subject has heart failure or the particular classification as opposed to not having heart failure or the particular classification.

As described above, following the step of obtaining the test data, the method of classifying of the invention comprises the step of comparing test data representing a level of RNA encoded by a marker gene to positive control data and/or negative control data, and determining the fold-change between the levels.

It will be appreciated that a computer may be used for comparing test data representing a level of RNA encoded by a marker gene to positive control data and/or negative control data, and determining the fold-change between the levels, according to methods of the invention.

An exemplary computer system for practicing certain of the methods described herein is described in FIG. 4.

FIG. 4 shows a schematic of a general-purpose computer system 100 suitable for practicing the methods described herein. The computer system 100, shown as a self-contained unit but not necessarily so limited, comprises at least one data processing unit (CPU) 102, a memory 104, which will typically include both high speed random access memory as well as non-volatile memory (such as one or more magnetic disk drives) but may be simply flash memory, a user interface 108, optionally a disk 110 controlled by a disk controller 112, and at least one optional network or other communication interface card 114 for communicating with other computers as well as other devices. At least the CPU 102, memory 104, user interface 108, disk controller where present, and network interface card, communicate with one another via at least one communication bus 106.

Memory 104 stores procedures and data, typically including: an operating system 140 for providing basic system services; application programs 152 such as user level programs for viewing and manipulating data, evaluating formulae for the purpose of diagnosing a test subject; authoring tools for assisting with the writing of computer programs; a file system 142, a user interface controller 144 for handling communications with a user via user interface 108, and optionally one or more databases 146 for storing data of the invention and other information, optionally a graphics controller 148 for controlling display of data, and optionally a floating point coprocessor 150 dedicated to carrying out mathematical operations. The methods of the invention may also draw upon functions contained in one or more dynamically linked libraries, not shown in FIG. 1, but stored either in Memory 104, or on disk 110, or accessible via network interface connection 114.

User interface 108 may comprise a display 128, a mouse 126, and a keyboard 130. Although shown as separate components in FIG. 1, one or more of these user interface components can be integrated with one another in embodiments such as handheld computers. Display 128 may be a cathode ray tube (CRT), or flat-screen display such as an LCD based on active matrix or TFT embodiments, or may be an electroluminescent display, based on light emitting organic molecules such as conjugated small molecules or polymers. Other embodiments of a user interface not shown in FIG. 1 include, e.g., several buttons on a keypad, a card-reader, a touch-screen with or without a dedicated touching device, a trackpad, a trackball, or a microphone used in conjunction with voice-recognition software, or any combination thereof, or a security-device such as a fingerprint sensor or a retinal scanner that prohibits an unauthorized user from accessing data and programs stored in system 100.

System 100 may also be connected to an output device such as a printer (not shown), either directly through a dedicated printer cable connected to a serial or USB port, or wirelessly, or via a network connection.

The database 146 may instead, optionally, be stored on disk 110 in circumstances where the amount of data in the database is too great to be efficiently stored in memory 104. The database may also instead, or in part, be stored on one or more remote computers that communicate with computer system 100 through network interface connection 114.

The network interface 134 may be a connection to the internet or to a local area network via a cable and modem, or ethernet, firewire, or USB connectivity, or a digital subscriber line. Preferably the computer network connection is wireless, e.g., utilizing CDMA, GSM, or GPRS, or bluetooth, or standards such as 802.11a, 802.11b, or 802.11g.

It would be understood that various embodiments and configurations and distributions of the components of system 10 across different devices and locations are consistent with practice of the methods described herein. For example, a user may use a handheld embodiment that accepts data from a test subject, and transmits that data across a network connection to another device or location wherein the data is analyzed according to a formulae described herein. A result of such an analysis can be stored at the other location and/or additionally transmitted back to the handheld embodiment. In such a configuration, the act of accepting data from a test subject can include the act of a user inputting the information. The network connection can include a web-based interface to a remote site at, for example, a healthcare provider. Alternatively, system 10 can be a device such as a handheld device that accepts data from the test subject, analyzes the data, such as by inputting the data into a formula as further described herein, and generating a result that is displayed to the user. The result can then be, optionally, transmitted back to a remote location via a network interface such as a wireless interface. System 100 may further be configured to permit a user to transmit by e-mail results of an analysis directly to some other party, such as a healthcare provider, or a diagnostic facility, or a patient.

Kits and Compositions

It will be appreciated that components for practicing the methods described herein may be assembled in a kit.

“Kit” refers to a combination of physical elements, e.g., probes, including without limitation specific primers, labeled nucleic acid probes, antibodies, protein-capture agent(s), reagent(s), instruction sheet(s) and other elements useful to practice the invention, in particular to identify the levels of particular RNA molecules in a sample. These physical elements can be arranged in any way suitable for carrying out the invention. For example, probes and/or primers can be provided in one or more containers or in an array or microarray device.

In the context of this disclosure, the term “probe” refers to a molecule which can detectably distinguish between target molecules differing in structure, such as allelic variants. Detection can be accomplished in a variety of different ways but preferably is based on detection of specific binding. Examples of such specific binding include antibody binding and nucleic acid probe hybridization.

The present disclosure encompasses the use of diagnostic kits based on a variety of methodologies, e.g., PCR, reverse transcriptase-PCR, quantitative PCR, microarray, chip, mass-spectroscopy, which are capable of detecting RNA levels in a sample. There is also provided an article of manufacturing comprising packaging material and an analytical agent contained within the packaging material, wherein the analytical agent can be used for determining and/or comparing the levels of RNA encoded by one or more target genes of the disclosure, and wherein the packaging material comprises a label or package insert which indicates that the analytical agent can be used to identify levels of RNA that correspond to a probability that a test subject has heart failure, or to the severity of heart failure or to survival outcome, for example, a probability that the test subject has heart failure as opposed to not having heart failure.

Therefore, there is provided kits comprising degenerate primers to amplify polymorphic alleles or variants of target genes of the invention, and instructions comprising an amplification protocol and analysis of the results.

The kit may alternatively also comprise buffers, enzymes, and containers for performing the amplification and analysis of the amplification products. The kit may also be a component of a screening or prognostic kit comprising other tools such as DNA microarrays. The kit may also provides one or more control templates, such as nucleic acids isolated from sample of patients without heart failure or a categorized severity thereof, and/or nucleic acids isolated from samples of patients with heart failure or a categorized severity thereof.

The kit may also include instructions for use of the kit to amplify specific targets on a solid support. Where the kit contains a prepared solid support having a set of primers already fixed on the solid support, e.g. for amplifying a particular set of target polynucleotides, the kit also includes reagents necessary for conducting a PCR on a solid support, for example using an in situ-type or solid phase type PCR procedure where the support is capable of PCR amplification using an in situ-type PCR machine. The PCR reagents, included in the kit, include the usual PCR buffers, a thermostable polymerase (e.g. Taq DNA polymerase), nucleotides (e.g. dNTPs), and other components and labeling molecules (e.g. for direct or indirect labeling). The kits can be assembled to support practice of the PCR amplification method using immobilized primers alone or, alternatively, together with solution phase primers.

In one embodiment, the kit provides one or more primer pairs, each pair capable of amplifying RNA encoded by a target gene of the invention, thereby providing a kit for analysis of RNA expression of several different target genes of the invention in a biological sample in one reaction or several parallel reactions. Primers in the kits may be labeled, for example fluorescently labeled, to facilitate detection of the amplification products and consequent analysis of the RNA levels.

Examples of amplification techniques include strand displacement amplification, as disclosed in U.S. Pat. No. 5,744,311; transcription-free isothermal amplification, as disclosed in U.S. Pat. No. 6,033,881; repair chain reaction amplification, as disclosed in WO 90/01069; ligase chain reaction amplification, as disclosed in European Patent Appl. 320 308; gap filling ligase chain reaction amplification, as disclosed in U.S. Pat. No. 5,427,930; and RNA transcription-free amplification, as disclosed in U.S. Pat. No. 6,025,134.

In one embodiment, levels of RNA encoded by more than one target gene can be determined in one analysis. A combination kit may therefore include primers capable of amplifying cDNA derived from RNA encoded by different target genes. The primers may be differentially labeled, for example using different fluorescent labels, so as to differentiate between RNA from different target genes.

Multiplex, such as duplex, real-time RT-PCR enables simultaneous quantification of 2 targets in the same reaction, which saves time, reduces costs, and conserves samples. These advantages of multiplex, real-time RT-PCR make the technique well-suited for high-throughput gene expression analysis. Multiplex qPCR assay in a real-time format facilitates quantitative measurements and minimizes the risk of false-negative results. It is essential that multiplex PCR is optimized so that amplicons of all samples are compared in sub-plateau phase of PCR. Yun, Z., I. Lewensohn-Fuchs, P. Ljungman, L. Ringholm, J. Jonsson, and J. Albert. 2003. A real-time TaqMan PCR for routine quantitation of cytomegalovirus DNA in crude leukocyte lysates from stem cell transplant patients. J. Virol. Methods 110:73-79. Yun, Z., I. Lewensohn-Fuchs, P. Ljungman, and A. Vahlne. 2000. Real-time monitoring of cytomegalovirus infections after stem cell transplantation using the TaqMan polymerase chain reaction assays. Transplantation 69:1733-1736. [PubMed]. Simultaneous quantification of up to 2, 3, 4, 5, 6, 7, and 8 or more targets may be useful.

Accordingly, there is provided a kit comprising packaging and containing, for each gene of a set of one or more of the genes listed in Table 2, a primer set capable of generating an amplification product of DNA complementary to RNA encoded, in a human subject, only by the gene. In one embodiment, the set of genes comprises ASGR2 and STAB1. In another embodiment, the set of genes comprises ASGR2, C3AR1 and/or STAB1.

In another aspect, the kit further comprises a computer-readable medium having instructions stored thereon that are operable when executed by a computer for comparing the test data representing a level of RNA encoded by the gene in blood of a human test subject to positive control data representing levels of RNA encoded by the gene in blood of human control subjects having a categorized severity of heart failure, to thereby output data representing a value indicating whether the test data and the positive control data correspond to each other, wherein correspondence between the test data and the positive control data indicates that the test subject has the categorized severity of heart failure.

In yet another aspect, there is provided a kit comprising packaging and containing, for each gene of a set of one or more of the genes set forth in Table 3, a primer set capable of generating an amplification product of DNA complementary to RNA encoded, in a human subject, only by the gene. In one embodiment, the set of one or more genes comprises FAM134B, MGAT4A, ZCCHC14 and/or CD28

In another aspect, the kit further comprises a thermostable polymerase, a reverse transcriptase, deoxynucleotide triphosphates, nucleotide triphosphates and/or enzyme buffer.

In yet another aspect, the kit further comprises at least one labeled probe capable of selectively hybridizing to either a sense or an antisense strand of the amplification product.

In yet another aspect of the invention, the kit further contains a computer-readable medium of the invention.

In one aspect, the kit is identified in print in or on the packaging as being for determining severity of heart failure in a test subject, for example, a probability that a test subject has a particular heart failure classification as opposed to not having the particular heart failure classification.

In another aspect, the kit is identified in print in or on the packaging as being for monitoring the progression of heart failure in a test subject.

In a further aspect, the kit is identified in print in or on the packaging as being for classifying whether a test subject has decompensated heart failure as opposed to not having decompensated heart failure.

In yet another aspect, the kit is identified in print in or on the packaging as being for determining whether a human subject with heart failure has a prognosis of mortality as opposed to not having a prognosis of mortality.

In yet a further aspect, the kit is identified in print in or on the packaging as being for ranking a group of human test subjects based on relative risk.

In various aspects of the kits described herein, the set of genes may be any combination of two or more of the target genes, as described hereinabove and in the Examples section, below.

The disclosure also provides primer sets, isolated compositions and test systems.

Examples of a primer of the disclosure include an oligonucleotide which is capable of acting as a point of initiation of polynucleotide synthesis along a complementary strand when placed under conditions in which synthesis of a primer extension product which is complementary to a polynucleotide is catalyzed. Such conditions include the presence of four different nucleotide triphosphates or nucleoside analogs and one or more agents for polymerization such as DNA polymerase and/or reverse transcriptase, in an appropriate buffer (“buffer” includes substituents which are cofactors, or which affect pH, ionic strength, etc.), and at a suitable temperature. A primer must be sufficiently long to prime the synthesis of extension products in the presence of an agent for polymerase. A typical primer contains at least about 5 nucleotides in length of a sequence substantially complementary to the target sequence, but somewhat longer primers are preferred.

The terms “complementary” or “complement thereof”, as used herein, refer to sequences of polynucleotides which are capable of forming Watson & Crick base pairing with another specified polynucleotide throughout the entirety of the complementary region. This term is applied to pairs of polynucleotides based solely upon their sequences and does not refer to any specific conditions under which the two polynucleotides would actually bind.

A primer will always contain a sequence substantially complementary to the target sequence, that is the specific sequence to be amplified, to which it can anneal.

A primer which “selectively hybridizes” to a target polynucleotide is a primer which is capable of hybridizing only, or mostly, with a single target polynucleotide in a mixture of polynucleotides consisting of RNA of human blood, or consisting of DNA complementary to RNA of human blood.

Accordingly, there is provided an isolated composition comprising, a blood sample from a test subject and for each gene of a set of one or more genes selected from the genes listed in Table 2, one or more components selected from the group consisting of exogenous RNA encoded by the gene, cDNA complementary to the RNA, an oligonucleotide which specifically hybridizes to the cDNA or the RNA under stringent conditions, a primer set capable of generating an amplification product of the cDNA complementary to RNA, and an amplification product of the cDNA. In one embodiment, the set of genes consists of an ASGR2 gene and a STAB1 gene.

In another aspect, there is provided an isolated composition comprising, for each gene of a set of one or more genes selected from the genes listed in Table 2, a blood sample from a test subject and one or more components selected from the group consisting of exogenous RNA encoded by the gene, cDNA complementary to the RNA, an oligonucleotide which specifically hybridizes to the cDNA or the RNA under stringent conditions, a primer set capable of generating an amplification product of the cDNA complementary to RNA, and an amplification product of the cDNA. In one embodiment, the set of genes comprises or consists of an ASGR2 gene and a STAB1 gene.

In one embodiment, there is provided an isolated composition comprising a blood sample from a test subject and one or more of exogenous RNA encoded by an ASGR2 gene, a C3AR1 gene or a STAB1 gene, cDNA complementary to the RNA, an oligonucleotide which specifically hybridizes to the cDNA or the RNA under stringent conditions, a primer set capable of generating an amplification product of the cDNA complementary to RNA, and/or an amplification product of the cDNA. In another embodiment, there is provided an isolated composition comprising an isolated nucleic acid molecule of a blood sample from a test subject, wherein the nucleic acid molecule is one or more of exogenous RNA encoded by an ASGR2 gene, a C3AR1 gene or a STAB1 gene, cDNA complementary to the RNA, an oligonucleotide which specifically hybridizes to the cDNA or the RNA under stringent conditions, a primer set capable of generating an amplification product of the cDNA complementary to RNA, and/or an amplification product of the cDNA.

There is also provided an isolated composition comprising a blood sample from a test subject and one or more of exogenous RNA encoded by an FAM134B gene, a MGAT4A gene, a ZCCHC14 gene or a CD28 gene, cDNA complementary to the RNA, an oligonucleotide which specifically hybridizes to the cDNA or the RNA under stringent conditions, a primer set capable of generating an amplification product of the cDNA complementary to RNA, and/or an amplification product of the cDNA. Also provided is n isolated composition comprising an isolated nucleic acid molecule of a blood sample from a test subject, wherein the nucleic acid molecule is one or more of exogenous RNA encoded by an FAM134B gene, a MGAT4A gene, a ZCCHC14 gene or a CD28 gene, cDNA complementary to the RNA, an oligonucleotide which specifically hybridizes to the cDNA or the RNA under stringent conditions, a primer set capable of generating an amplification product of the cDNA complementary to RNA, and/or an amplification product of the cDNA.

In yet another aspect, there is provided a primer set comprising a first primer and a second primer, wherein the first primer is one of a set of primers capable of generating an amplification product of cDNA complementary to RNA encoded by a first gene, wherein the second primer is capable of generating an amplification product of cDNA complementary to RNA encoded by a second gene, and wherein the first gene and the second gene are different genes selected from the genes listed in Table 2, or composition thereof. In one embodiment, the set of genes comprises or consists of an ASGR2 gene and a STAB1 gene.

In yet another aspect, there is provided a primer set comprising a first primer, wherein the first primer is one of a set of primers capable of generating an amplification product of cDNA complementary to RNA encoded by an ASGR2 gene, and a second primer, wherein the second primer is one of a set of primers capable of generating an amplification product of cDNA complementary to RNA encoded by a C3AR1 gene, or composition thereof. Also provided is a primer set comprising a first primer, wherein the first primer is one of a set of primers capable of generating an amplification product of cDNA complementary to RNA encoded by an ASGR2 gene, and a second primer, wherein the second primer is one of a set of primers capable of generating an amplification product of cDNA complementary to RNA encoded by a STAB1 gene, or composition thereof. Further provided is a primer set comprising a first primer, wherein the first primer is one of a set of primers capable of generating an amplification product of cDNA complementary to RNA encoded by an C3AR1 gene, and a second primer, wherein the second primer is one of a set of primers capable of generating an amplification product of cDNA complementary to RNA encoded by a STAB1 gene, or composition thereof.

In a further aspect, there is provided a primer set comprising a first primer, wherein the first primer is one of a set of primers capable of generating an amplification product of cDNA complementary to RNA encoded by an FAM134B gene, and a second primer, wherein the second primer is one of a set of primers capable of generating an amplification product of cDNA complementary to RNA encoded by a MGAT4A gene, or composition thereof. Also provided is a primer set comprising a first primer, wherein the first primer is one of a set of primers capable of generating an amplification product of cDNA complementary to RNA encoded by an FAM134B gene, and a second primer, wherein the second primer is one of a set of primers capable of generating an amplification product of cDNA complementary to RNA encoded by a ZCCHC14 gene, or composition thereof. Further provided is primer set comprising a first primer, wherein the first primer is one of a set of primers capable of generating an amplification product of cDNA complementary to RNA encoded by an FAM134B gene, and a second primer, wherein the second primer is one of a set of primers capable of generating an amplification product of cDNA complementary to RNA encoded by a CD28 gene, or composition thereof. Also provided is a primer set comprising a first primer, wherein the first primer is one of a set of primers capable of generating an amplification product of cDNA complementary to RNA encoded by an MGAT4A gene, and a second primer, wherein the second primer is one of a set of primers capable of generating an amplification product of cDNA complementary to RNA encoded by a ZCCHC14 gene, or composition thereof. Further provided is a primer set comprising a first primer, wherein the first primer is one of a set of primers capable of generating an amplification product of cDNA complementary to RNA encoded by an MGAT4A gene, and a second primer, wherein the second primer is one of a set of primers capable of generating an amplification product of cDNA complementary to RNA encoded by a CD28 gene, or composition thereof. In addition, there is provided a primer set comprising a first primer, wherein the first primer is one of a set of primers capable of generating an amplification product of cDNA complementary to RNA encoded by an ZCCHC14 gene, and a second primer, wherein the second primer is one of a set of primers capable of generating an amplification product of cDNA complementary to RNA encoded by a CD28 gene, or composition thereof.

In a further aspect, there is provided a test system comprising: a) two or more blood samples wherein each blood sample is from a different test subject, and b) an isolated nucleic acid molecule of each of said blood samples, wherein said nucleic acid molecule is one or more of exogenous RNA encoded by an ASGR2, C3AR1 or STAB 1 gene, cDNA complementary to said RNA, an oligonucleotide which specifically hybridizes to said cDNA or complement thereof, or said RNA under stringent conditions, a primer set capable of generating an amplification product of said cDNA complementary to RNA, and/or an amplification product of said cDNA.

In yet another aspect, there is provided a test system comprising: (a) two or more blood samples wherein each blood sample is from a different test subject, and (b) an isolated nucleic acid molecule of each of said blood samples, wherein said nucleic acid molecule is one or more of exogenous RNA encoded by an FAM134B, MGAT4A, ZCCHC14 or CD28 gene, cDNA complementary to said RNA, an oligonucleotide which specifically hybridizes to said cDNA or complement thereof, or said RNA under stringent conditions, a primer set capable of generating an amplification product of said cDNA complementary to RNA, and/or an amplification product of said cDNA.

The above disclosure generally describes the present disclosure. A more complete understanding can be obtained by reference to the following specific examples. These examples are described solely for the purpose of illustration and are not intended to limit the scope of the disclosure. Changes in form and substitution of equivalents are contemplated as circumstances might suggest or render expedient. Although specific terms have been employed herein, such terms are intended in a descriptive sense and not for purposes of limitation.

The following non-limiting examples are illustrative of the present disclosure:

EXAMPLES
Example 1
Results
Subject Recruitment

A total of 87 subjects were recruited in this study: 15 were control (non-heart failure); 72 were heart failure patients. Heart failure subjects were categorized into two groups: 32 were compensated heart failure patients (NYHA I-II); 40 were de-compensated heart failure patients (NYHA III-IV). Demographic characteristics and medications of all subjects were summarized in Table 1.

Microarray analysis identified 294 genes differentially regulated (p<0.001) in HF (FIG. 1), including the genes ASGR2, C3AR1 and STAB1 (Table 2, listed in order of increasing p-value/decreasing statistical significance). Pathway analysis revealed that genes involved in T cell receptor signalling and natural killer cell signalling were significantly (p<0.001) over-presented in HF-regulated genes (FIG. 2). HF-regulated genes in the T cell receptor signalling pathway include genes in the upstream of the pathway, such as receptors, cell surface molecules and signal transduction molecules (Table 4; FIG. 3); their expression levels decreased in HF, and the magnitude of their differential expression increased with the severity of HF (Table 4).

Analysis of Gene Expression: Affymetrix GeneChip U133Plus2.0 is a whole-genome microarray containing over 56,000 probe sets. Cross-gene error model was applied to the 87 blood expression profiles processed with GC-RMA; after removing unreliable measurements, approximately 27,000 probe sets remained for further analysis. Of these probe sets, survival analysis and subsequent multi-test correction identified the genes listed in Table 3 as significantly (q<0.2) associated with survival time.

Functional categorization of the “survival associated genes” revealed that one functional group over-presented in this group was the one involved in T-cell receptor signaling as shown in Table 4 and FIG. 3.

Certain HF-regulated genes listed in Table 2 were associated with the survival time of HF patients with a statistical significance of p<0.05 (Table 3, listed in order of increasing p-value/decreasing statistical significance); including the genes FAM134B, MGAT4A, ZCCHC14 and CD28. Below are representative equations for ranking each of a group of heart failure patients according to probability of fatal outcome.

Briefly, a person skilled in the art would be able to apply survival analysis to the genes listed in Table 3 with the Survival package in R: the expression data of each gene and the survival data is fit with a Cox proportional hazards regression model; the significance of the association between gene expression and survival time can be assessed using a logrank test; Multi-test correction is performed using the Q value (Storey and Tibshirani, 2003) package in R; a q value of 0.2 was chosen as a significance cut-off for “survival associated gene” selection. Thus, a person skilled in the art would be able to rank each of a group of test subjects according to relative risk of death using the genes listed in Table 3 by applying the general formula: relative risk=coefficient̂expression, where coefficient refers to the gene-specific coefficient value listed in Table 3, and expression refers to the log of the gene-specific RNA level in blood of the test subject, determined, e.g. as described in Materials and Methods. The symbol “̂” indicates, according to convention, that the indicated gene-specific numerical coefficient is raised to an exponent corresponding to the log of the RNA level. Such ranking has utility, for example, for prioritizing patients to be monitored and/or treated, particularly in a context of limited monitoring and/or treatment resources requiring allocation.

The below representative equations were derived using the Cox method described herein to provide the risk score for a subject.

CD28: relative risk=0.451̂Expression

FAM134B: relative risk=0.192̂Expression

MGAT4A: relative risk=0.206̂Expression

ZCCHC14: relative risk=0.440̂Expression

Material and Methods

Subject recruitment. Heart failure subjects were identified from an outpatient clinic population or at the time of admission to hospital with primary diagnosis of HF. All patients had assessment of left ventricular function as part of routine cardiac care prior enrolment. The severity of HF was characterized using New York Heart Association (NYHA) classification. Controls were identified through the stress lab referred for atypical or non-cardiac chest pain and had no prior diagnosis of cardiac disease. Through this mechanism both the absence of significant coronary disease and normal ventricular function were confirmed by a negative stress test (stress echo and/or nuclear perfusion imaging).

Blood collection, RNA extraction and microarray hybridization. Overnight fasting blood samples were collected using a Vacutainer™ tube and stored on ice till RNA extraction. Blood samples were processed for RNA extraction within six hours after blood collection. Red blood cells were ruptured with hypotonic haemolysis buffer, followed by collection of white blood cells by centrifugation. White blood cell total RNA was extracted with Trizol® Reagent. The quality of RNA samples was assessed on an Agilent Bioanalyzer 2100 using RNA 6000 Nano Chips; the quantity of RNA was measured by UV spectrophotometry. Five microgram of total RNA of each sample was used for hybridization on a GeneChip U133Plus2.

Data analysis. Probe-level expression data were processed by GC-Robust Multichip Analysis (GC-RMA) using GeneSpring v7.3 software. Genes showing unreliable measurements, assessed by cross-gene error model, were removed from any further analysis. Differentially regulated genes by heart failure were identified by applying ANOVA to the three sample groups: control, NYHA I-II and NYHA III-IV; a p value of 0.001 was chosen as the significance cut-off. Genes with significant differential expression were subjected to cluster analysis using Spearman correlation and average linkage. Functional categorization of the HF-regulated genes were conducted using the Ingenuity Pathway Analysis software.

Survival analysis over a period of 43 months was applied to HF-regulated genes with the Survival package in R: the expression data of each gene and the survival data were fit with a Cox proportional hazards regression model; the significance of the association between gene expression and survival time was assessed using a logrank test; Multi-test correction was performed using the Q value (Storey and Tibshirani, 2003) package in R; a q value of 0.2 was chosen as a significance cut-off for “survival associate gene” selection. Differentially regulated “survival associated genes” between control and NYHA I-II, and between control and NYHA III-IV were identified by a Welch t-test; a p value of 0.05 was chosen as the significance cut-off.

Functional categorization of the “survival associated genes” was conducted using the Ingenuity Pathway Analysis software (Ingenuity Systems Inc., Redwood City, Calif.). Genes in significantly over-presented functional group(s) were subjected to cluster analysis using Pearson correlation and complete linkage. The 87 samples were re-classified based on the cluster analysis of “survival associated genes” into three groups. Survival analysis was applied to the reclassified three groups and to the original three groups based on NYHA classification (Control, NYHA I-II and NYHA III-IV); Kaplan-Meier plot was drawn using the Survival package in R; the significance was assessed using a logrank test.

Example 2
Classification of a Patient Suspected of Potentially Having Heart Failure as Having NYHA I-II or NYHA III/IV Stage Heart Failure

Overnight fasting blood samples are collected from a patient suspected of potentially having heart failure using a Vacutainer™ tube, and from healthy subjects and are stored on ice. The blood samples are processed for RNA extraction within six hours after blood collection. Red blood cells in the samples are ruptured with hypotonic haemolysis buffer, followed by collection of white blood cells by centrifugation. White blood cell total RNA is extracted with Trizol® Reagent. The quality of RNA samples is assessed on an Agilent Bioanalyzer 2100 using RNA 6000 Nano Chips; and the quantity of RNA is measured by UV spectrophotometry. Five micrograms of total RNA of the samples is used for hybridization on a GeneChip U133Plus2 to measure the levels of RNA encoded by the genes ASGR2 and STAB1 in the samples.

The ratio of the level of RNA encoded by ASGR2 in the sample from the patient to the average level of RNA encoded by ASGR2 in the blood samples of the healthy subjects is determined, and the ratio of the level of RNA encoded by STAB1 in the sample from the patient to the average level of RNA encoded by STAB1 in the blood samples of the healthy subjects is determined.

The patient is classified as having NYHA I/II stage heart failure if the level of RNA encoded by ASGR2 in the sample from the patient is between 1.59 to 2.45 fold, relative to the average level of RNA encoded by the gene in the blood samples of the healthy subjects, and if the level of RNA encoded by STAB1 in the sample from the patient is between 1.33 to 1.92 fold, relative to the average level of RNA encoded by the gene in the blood samples of the healthy subjects.

Alternately, the patient is classified as having NYHA III/IV stage heart failure if the level of RNA encoded by ASGR2 in the sample from the patient is greater than 2.45, relative to the average level of RNA encoded by the gene in the blood samples of the healthy subjects, and if the level of RNA encoded by STAB1 in the sample from the patient is greater than 1.92, relative to the average level of RNA encoded by the gene in the blood samples of the healthy subjects.

Example 3
Ranking of Patients Having Heart Failure According to Survival Time Prognosis

Overnight fasting blood samples are collected from patients diagnosed as having heart failure, using a Vacutainer™ tube and are stored on ice. The blood samples are processed for RNA extraction within six hours after blood collection. Red blood cells in the samples are ruptured with hypotonic haemolysis buffer, followed by collection of white blood cells by centrifugation. White blood cell total RNA is extracted with Trizol® Reagent. The quality of RNA samples is assessed on an Agilent Bioanalyzer 2100 using RNA 6000 Nano Chips; and the quantity of RNA is measured by UV spectrophotometry. Five micrograms of total RNA of the samples is used for hybridization on a GeneChip U133Plus2 to measure levels of RNA encoded by the gene FAM134B in the samples.

A relative risk score for risk of death for each patient is calculated according to the equation; risk score=0.192̂[level of FAM134B RNA in sample]; and the patients are ranked according to survival time prognosis as a function of risk score, where the higher the risk score, the worse the survival time prognosis.

While the present disclosure has been described with reference to what are presently considered to be the preferred examples, it is to be understood that the disclosure is not limited to the disclosed examples. To the contrary, the disclosure is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.

All publications, patents and patent applications are herein incorporated by reference in their entirety to the same extent as if each individual publication, patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety. In addition all sequences identified herein by accession number for example in Tables herein, are also specifically incorporated by reference.

TABLE 1

Control
NYHA I-II
NYHA III-IV

N
15
32
40

Age: mean (range)
58.0
60.2 (28.2-87.7)
66.9 (46.9-85.4)

(33.6-81.1)

Sex: Male/Female
7-Aug
21-Nov
28-Dec

LVEFpc: mean
63 (55-77)
27 (10-64)
22 (10-60)

(range)

Ischemic
0%
43.80%
62.50%

Cardiomyopathy

BNP: mean (range)
—
1249 (80-2230)
1251 (182-4890)

Diabetes Mellitus
20.00%
21.90%
52.50%

Hypertension
60.00%
71.90%
77.50%

Renal Insufficiency
0.00%
9.40%
37.50%

Atrial Fibrillation
0.00%
28.10%
52.50%

Loop Diuretic
20.00%
34.40%
80.00%

β blocker
40.00%
81.30%
82.50%

ACE-I/ARB
26.70%
87.50%
77.50%

TABLE 2

Severity genes - note that genes are listed in order of decreasing

statistical significance/preference.

Fold-change

Gene

expression
Fold-change expression

Probe set
RefSeq
symbol
Gene description
ANOVA p
(NYHA I-II/Control)
(NYHA III-IV/Control)

206130_s_at
NM_001181
ASGR2
asialoglycoprotein
3.49E−07
1.59
2.45

receptor 2

209906_at
NM_004054
C3AR1
complement
8.50E−07
1.05
1.94

component 3a receptor 1

204150_at
NM_015136
STAB1
stabilin 1
1.55E−06
1.33
1.92

225864_at
NM_174911
FAM84B
family with sequence
2.72E−06
0.82
0.56

similarity 84, member B

212331_at
NM_005611
RBL2
retinoblastoma-like 2
4.46E−06
1.00
0.85

(p130)

232633_at
NM_021141
XRCC5
X-ray repair
4.94E−06
0.90
0.80

complementing

defective repair in

Chinese hamster cells

5 (double-strand-break

rejoining; Ku

autoantigen, 80 kDa)

222820_at
NM_018996
TNRC6C
trinucleotide repeat
5.98E−06
0.96
0.71

containing 6C

222585_x_at
NM_016618
KRCC1
lysine-rich coiled-coil 1
6.05E−06
1.04
1.20

226148_at
NM_014155
ZBTB44
zinc finger and BTB
1.18E−05
0.91
0.80

domain containing 44

218306_s_at
NM_003922
HERC1
hect (homologous to
1.88E−05
0.91
0.86

the E6-AP (UBE3A)

carboxyl terminus)

domain and RCC1

(CHC1)-like domain

(RLD) 1

228047_at
NR_002581
SNORA72
small nucleolar RNA,
2.06E−05
0.90
0.76

H/ACA box 72

217388_s_at
NM_001032998
KYNU
kynureninase (L-
2.19E−05
1.00
1.58

kynurenine hydrolase)

210102_at
NM_014622
LOH11CR2A
loss of heterozygosity,
2.80E−05
1.19
1.68

11, chromosomal

region 2, gene A

228624_at
NM_018342
TMEM144
transmembrane protein
2.84E−05
1.34
1.98

144

202664_at
NM_001077269
WIPF1
WAS/WASL interacting
3.13E−05
0.94
0.86

protein family, member 1

202165_at
NM_006241
PPP1R2
protein phosphatase 1,
3.61E−05
0.92
0.82

regulatory (inhibitor)

subunit 2

228106_at
NM_017741
C4orf30
chromosome 4 open
4.09E−05
1.03
0.73

reading frame 30

235048_at
NM_015566
KIAA0888
KIAA0888 protein
4.58E−05
0.85
0.57

226529_at
NM_018374
TMEM106B
transmembrane protein
4.66E−05
0.92
0.80

106B

205259_at
NM_000901
NR3C2
nuclear receptor
4.73E−05
0.82
0.50

subfamily 3, group C,

member 2

226158_at
NM_017644
KLHL24
kelch-like 24
4.90E−05
0.95
0.77

(Drosophila)

225724_at
—
FLJ31306
hypothetical protein
5.17E−05
0.96
0.83

FLJ31306

205698_s_at
NM_002758
MAP2K6
mitogen-activated
5.27E−05
0.81
1.39

protein kinase kinase 6

203408_s_at
NM_002971
SATB1
SATB homeobox 1
5.42E−05
0.95
0.74

206857_s_at
NM_004116
FKBP1B
FK506 binding protein
5.48E−05
1.11
1.78

1B, 12.6 kDa

211571_s_at
NM_004385
VCAN
versican
5.75E−05
1.24
1.70

213908_at
NR_003521
WHDC1L1
WAS protein homology
5.82E−05
0.89
0.67

region 2 domain

containing 1-like 1

204642_at
NM_001400
EDG1
endothelial
6.34E−05
0.95
0.67

differentiation,

sphingolipid G-protein-

coupled receptor, 1

208771_s_at
NM_000895
LTA4H
leukotriene A4
7.10E−05
1.08
1.45

hydrolase

218411_s_at
NM_016586
MBIP
MAP3K12 binding
7.64E−05
1.05
0.79

inhibitory protein 1

230490_x_at
NM_012425
RSU1
Ras suppressor protein 1
7.67E−05
1.12
0.85

202970_at
NM_003583
DYRK2
dual-specificity
7.76E−05
1.00
0.73

tyrosine-(Y)-

phosphorylation

regulated kinase 2

212538_at
NM_015296
DOCK9
dedicator of cytokinesis 9
7.93E−05
0.93
0.64

226682_at
—
LOC283666
hypothetical protein
8.06E−05
0.98
0.64

LOC283666

209884_s_at
NM_003615
SLC4A7
solute carrier family 4,
8.62E−05
0.99
0.75

sodium bicarbonate

cotransporter, member 7

228577_x_at
NM_001007022
ODF2L
outer dense fiber of
9.04E−05
0.93
0.76

sperm tails 2-like

227626_at
NM_133367
PAQR8
progestin and adipoQ
9.07E−05
0.88
0.68

receptor family

member VIII

224918_x_at
NM_020300
MGST1
microsomal glutathione
9.11E−05
1.28
1.68

S-transferase 1

227067_x_at
NM_203458
NOTCH2NL
Notch homolog 2
9.51E−05
1.04
1.59

(Drosophila) N-terminal

like

1559097_at
—
C14orf64
chromosome 14 open
9.75E−05
0.94
0.59

reading frame 64

212672_at
NM_000051
ATM
ataxia telangiectasia
9.85E−05
1.00
0.75

mutated

227639_at
NM_005482
PIGK
phosphatidylinositol
0.0001
0.86
0.71

glycan anchor

biosynthesis, class K

204165_at
NM_001024934
WASF1
WAS protein family,
0.000101
1.23
1.74

member 1

226327_at
NM_014910
ZNF507
zinc finger protein 507
0.000101
0.97
0.80

211985_s_at
NM_006888
CALM1
calmodulin 1
0.000105
0.98
0.84

(phosphorylase kinase,

delta)

203556_at
NM_014943
ZHX2
zinc fingers and
0.000107
0.83
0.79

homeoboxes 2

205434_s_at
NM_001012987
AAK1
AP2 associated kinase 1
0.00011
0.94
0.73

228423_at
NM_001039580
MAP9
microtubule-associated
0.000112
0.91
0.61

protein 9

242945_at
NM_017565
FAM20A
family with sequence
0.000117
1.11
1.96

similarity 20, member A

225117_at
NM_015443
KIAA1267
KIAA1267
0.000122
0.92
0.86

200686_s_at
NM_004768
SFRS11
splicing factor,
0.000123
1.06
0.85

arginine/serine-rich 11

212609_s_at
NM_005465
AKT3
V-akt murine thymoma
0.000126
0.92
0.73

viral oncogene

homolog 3 (protein

kinase B, gamma)

230529_at
NM_016217
HECA
headcase homolog
0.000129
0.86
0.81

(Drosophila)

210156_s_at
NM_005389
PCMT1
protein-L-isoaspartate
0.000131
0.96
1.17

(D-aspartate) O-

methyltransferase

219607_s_at
NM_024021
MS4A4A
membrane-spanning 4-
0.000134
1.14
2.80

domains, subfamily A,

member 4

200663_at
NM_001040034
CD63
CD63 molecule
0.00014
0.99
1.20

202723_s_at
NM_002015
FOXO1
forkhead box O1
0.000142
0.84
0.81

204075_s_at
NM_014704
KIAA0562
KIAA0562
0.000143
1.04
0.82

201656_at
NM_000210
ITGA6
integrin, alpha 6
0.000145
0.96
0.64

223993_s_at
NM_014184
CNIH4
cornichon homolog 4
0.000146
0.85
1.23

(Drosophila)

204484_at
NM_002646
PIK3C2B
phosphoinositide-3-
0.000148
0.87
0.63

kinase, class 2, beta

polypeptide

213224_s_at
—
LOC92482
hypothetical protein
0.000148
0.89
0.83

LOC92482

212205_at
NM_012412
H2AFV
H2A histone family,
0.000149
0.90
0.79

member V

219387_at
NM_018084
CCDC88A
coiled-coil domain
0.000153
1.17
1.90

containing 88A

209604_s_at
NM_001002295
GATA3
GATA binding protein 3
0.000154
1.02
0.66

226247_at
NM_001001974
PLEKHA1
pleckstrin homology
0.000159
0.98
0.69

domain containing,

family A

(phosphoinositide

binding specific)

member 1

201850_at
NM_001747
CAPG
capping protein (actin
0.000159
1.00
1.55

filament), gelsolin-like

225191_at
NM_001280
CIRBP
cold inducible RNA
0.000162
0.89
0.84

binding protein

201557_at
NM_014232
VAMP2
vesicle-associated
0.000163
1.01
0.81

membrane protein 2

(synaptobrevin 2)

222981_s_at
NM_016131
RAB10
RAB10, member RAS
0.000168
0.99
1.27

oncogene family

227448_at
NM_018011
FLJ10154
hypothetical protein
0.00017
0.94
0.78

FLJ10154

202821_s_at
NM_005578
LPP
LIM domain containing
0.000171
0.90
1.20

preferred translocation

partner in lipoma

212455_at
NM_001031732
YTHDC1
YTH domain containing 1
0.000182
0.96
0.92

1555037_a_at
NM_005896
IDH1
isocitrate
0.000191
1.13
1.39

dehydrogenase 1

(NADP+), soluble

204773_at
NM_004512
IL11RA
interleukin 11 receptor,
0.000192
1.11
0.74

alpha

228446_at
NM_001017969
KIAA2026
KIAA2026
0.000192
0.94
0.85

205005_s_at
NM_004808
NMT2
N-myristoyltransferase 2
0.000199
0.94
0.55

230078_at
NM_016340
RAPGEF6
Rap guanine
0.000201
0.95
0.82

nucleotide exchange

factor (GEF) 6

201007_at
NM_000183
HADHB
hydroxyacyl-Coenzyme
0.000203
0.98
1.13

A dehydrogenase/3-

ketoacyl-Coenzyme A

thiolase/enoyl-

Coenzyme A hydratase

(trifunctional protein),

beta subunit

221493_at
NM_003309
TSPYL1
TSPY-like 1
0.000212
0.93
0.80

221905_at
NM_001042355
CYLD
cylindromatosis (turban
0.000214
1.03
0.79

tumor syndrome)

1556402_at
—
FLJ46446
Hypothetical gene
0.000217
0.86
0.56

supported by

AK128305

214049_x_at
NM_006137
CD7
CD7 molecule
0.000218
0.88
0.71

214442_s_at
NM_004671
PIAS2
protein inhibitor of
0.00022
0.79
1.23

activated STAT, 2

222435_s_at
NM_016021
UBE2J1
ubiquitin-conjugating
0.00022
1.03
1.51

enzyme E2, J1 (UBC6

homolog, yeast)

220034_at
NM_007199
IRAK3
interleukin-1 receptor-
0.00022
0.66
1.13

associated kinase 3

231817_at
NM_019050
USP53
ubiquitin specific
0.000224
0.96
0.65

peptidase 53

212981_s_at
NM_014719
FAM115A
family with sequence
0.000225
0.93
0.67

similarity 115, member A

212655_at
NM_015144
ZCCHC14
zinc finger, CCHC
0.000231
0.89
0.73

domain containing 14

202419_at
NM_002035
FVT1
follicular lymphoma
0.000231
1.02
0.80

variant translocation 1

208896_at
NM_006773
DDX18
DEAD (Asp-Glu-Ala-
0.000232
1.10
0.82

Asp) box polypeptide

18

226581_at
NM_022340
ZFYVE20
zinc finger, FYVE
0.000234
0.94
0.88

domain containing 20

224833_at
NM_005238
ETS1
v-ets erythroblastosis
0.000236
0.96
0.70

virus E26 oncogene

homolog 1 (avian)

224698_at
NM_020728
FAM62B
family with sequence
0.000243
1.01
0.72

similarity 62 (C2

domain containing)

member B

213034_at
NM_025164
KIAA0999
KIAA0999 protein
0.000243
0.84
0.85

212343_at
NM_173834
YIPF6
Yip1 domain family,
0.000247
1.00
0.86

member 6

218499_at
NM_001042452
RP6-
serine/threonine
0.000248
0.91
0.82

213H19.1
protein kinase MST4

211946_s_at
NM_015172
BAT2D1
BAT2 domain
0.000255
0.96
0.87

containing 1

227988_s_at
NM_001018037
VPS13A
vacuolar protein sorting
0.000255
1.08
0.71

13 homolog A (S. cerevisiae)

222895_s_at
NM_022898
BCL11B
B-cell CLL/lymphoma
0.000257
0.89
0.59

11B (zinc finger

protein)

238614_x_at
NM_025189
ZNF430
zinc finger protein 430
0.000279
1.06
0.86

228065_at
NM_182557
BCL9L
B-cell CLL/lymphoma
0.00028
0.92
0.71

9-like

225026_at
NM_032221
CHD6
chromodomain
0.000282
1.03
0.79

helicase DNA binding

protein 6

227900_at
NM_170662
CBLB
Cas-Br-M (murine)
0.000283
0.90
0.65

ecotropic retroviral

transforming sequence b

227119_at
NM_144571
CNOT6L
CCR4-NOT
0.000283
0.87
0.77

transcription complex,

subunit 6-like

206111_at
NM_002934
RNASE2
ribonuclease, RNase A
0.000283
0.93
1.47

family, 2 (liver,

eosinophil-derived

neurotoxin)

222279_at
NM_001003807
RP3-
hypothetical protein
0.000287
0.99
0.74

377H14.5
FLJ35429

214470_at
NM_002258
KLRB1
killer cell lectin-like
0.00029
0.95
0.66

receptor subfamily B,

member 1

209674_at
NM_004075
CRY1
cryptochrome 1
0.000292
0.92
0.72

(photolyase-like)

214582_at
NM_000922
PDE3B
phosphodiesterase 3B,
0.000305
0.80
0.72

cGMP-inhibited

212660_at
NM_015288
PHF15
PHD finger protein 15
0.000306
0.86
0.78

228549_at
NM_014698
TMEM63A
Transmembrane
0.000314
0.90
0.72

protein 63A

226479_at
NM_152903
KBTBD6
kelch repeat and BTB
0.000315
0.84
0.61

(POZ) domain

containing 6

226753_at
NM_144664
FAM76B
family with sequence
0.000319
0.91
0.80

similarity 76, member B

206545_at
NM_006139
CD28
CD28 molecule
0.000321
1.01
0.58

224968_at
NM_080667
CCDC104
coiled-coil domain
0.000322
0.99
0.71

containing 104

226181_at
NM_016262
TUBE1
tubulin, epsilon 1
0.000327
1.03
0.67

204203_at
NM_001806
CEBPG
CCAAT/enhancer
0.000339
1.06
1.40

binding protein

(C/EBP), gamma

212675_s_at
NM_015147
CEP68
centrosomal protein
0.000342
0.98
0.75

68 kDa

212259_s_at
NM_020524
PBXIP1
pre-B-cell leukemia
0.000344
0.77
0.76

homeobox interacting

protein 1

219316_s_at
NM_017791
FLVCR2
feline leukemia virus
0.000344
1.15
1.75

subgroup C cellular

receptor family,

member 2

219378_at
NM_001110798
NARG1L
NMDA receptor
0.000345
0.96
0.82

regulated 1-like

209368_at
NM_001979
EPHX2
epoxide hydrolase 2,
0.00035
0.83
0.65

cytoplasmic

206237_s_at
NM_004495
NRG1
neuregulin 1
0.000353
1.20
1.71

226030_at
NM_001609
ACADSB
acyl-Coenzyme A
0.000356
0.98
0.76

dehydrogenase,

short/branched chain

228853_at
XM_001125680
LOC730432
similar to
0.000359
0.97
0.79

serine/threonine/tyrosine

interacting protein

201560_at
NM_013943
CLIC4
chloride intracellular
0.000359
1.07
1.51

channel 4

224739_at
NM_001001852
PIM3
pim-3 oncogene
0.000368
1.06
1.25

1553132_a_at
NM_152332
TC2N
tandem C2 domains,
0.00037
1.09
0.66

nuclear

1552426_a_at
NM_025141
TM2D3
TM2 domain containing 3
0.00037
0.98
0.86

212033_at
NM_021239
RBM25
RNA binding motif
0.000372
1.05
0.93

protein 25

207231_at
NM_014648
DZIP3
zinc finger DAZ
0.000374
1.05
0.74

interacting protein 3

237033_at
NM_001042693
MGC52498
hypothetical protein
0.000378
0.93
0.70

MGC52498

235125_x_at
NM_198549
FAM73A
family with sequence
0.000379
1.07
0.80

similarity 73, member A

227984_at
XM_944170
LOC650392
Hypothetical protein
0.000384
1.00
0.66

LOC650392

218473_s_at
NM_024656
GLT25D1
glycosyltransferase 25
0.000391
1.04
1.17

domain containing 1

228282_at
NM_152778
MFSD8
Major facilitator
0.000391
1.02
0.84

superfamily domain

containing 8

243492_at
NM_053055
THEM4
Thioesterase
0.000392
0.88
0.66

superfamily member 4

206761_at
NM_005816
CD96
CD96 molecule
0.000401
0.99
0.68

223592_s_at
NM_032322
RNF135
ring finger protein 135
0.000402
1.01
1.26

218723_s_at
NM_014059
C13orf15
chromosome 13 open
0.000405
0.88
0.58

reading frame 15

214195_at
NM_000391
TPP1
tripeptidyl peptidase I
0.000412
1.04
1.10

202436_s_at
NM_000104
CYP1B1
cytochrome P450,
0.000413
1.15
1.79

family 1, subfamily B,

polypeptide 1

223092_at
NM_054027
ANKH
ankylosis, progressive
0.000422
0.95
0.70

homolog (mouse)

221036_s_at
NM_031301
APH1B
anterior pharynx
0.000424
0.85
1.11

defective 1 homolog B

(C. elegans)

1553974_at
NM_173793
LOC128977
hypothetical protein
0.000427
0.97
0.85

LOC128977

231124_x_at
NM_001033667
LY9
lymphocyte antigen 9
0.00043
0.93
0.66

1556743_at
NM_018293
ZNF654
zinc finger protein 654
0.00044
0.92
0.81

209798_at
NM_002519
NPAT
nuclear protein, ataxia-
0.000444
0.93
0.79

telangiectasia locus

203234_at
NM_003364
UPP1
uridine phosphorylase 1
0.000445
0.92
1.37

205936_s_at
NM_002115
HK3
hexokinase 3 (white
0.000446
1.01
1.54

cell)

232914_s_at
NM_032379
SYTL2
synaptotagmin-like 2
0.000447
0.83
0.57

203939_at
NM_002526
NT5E
5′-nucleotidase, ecto
0.000457
0.77
0.62

(CD73)

201666_at
NM_003254
TIMP1
TIMP metallopeptidase
0.000457
1.06
1.40

inhibitor 1

202880_s_at
NM_004762
PSCD1
pleckstrin homology,
0.000458
0.95
0.89

Sec7 and coiled-coil

domains 1(cytohesin 1)

201677_at
NM_001006109
C3orf37
Chromosome 3 open
0.00046
0.90
0.77

reading frame 37

202704_at
NM_005749
TOB1
transducer of ERBB2, 1
0.000461
0.91
0.80

228760_at
NM_032102
SFRS2B
splicing factor,
0.000463
0.97
0.71

arginine/serine-rich 2B

220099_s_at
NM_016019
LUC7L2
LUC7-like 2 (S. cerevisiae)
0.000466
0.99
0.84

226680_at
NM_022466
IKZF5
IKAROS family zinc
0.000474
0.91
0.81

finger 5 (Pegasus)

224737_x_at
NM_018237
CCAR1
cell division cycle and
0.000479
1.14
0.89

apoptosis regulator 1

221221_s_at
NM_017415
KLHL3
kelch-like 3
0.000489
0.89
0.58

(Drosophila)

209657_s_at
NM_004506
HSF2
heat shock
0.00049
0.88
0.72

transcription factor 2

200965_s_at
NM_001003407
ABLIM1
actin binding LIM
0.000495
0.90
0.61

protein 1

209124_at
NM_002468
MYD88
myeloid differentiation
0.00051
1.01
1.16

primary response gene

(88)

208659_at
NM_001288
CLIC1
chloride intracellular
0.00051
0.97
1.12

channel 1

207606_s_at
NM_018287
ARHGAP12
Rho GTPase activating
0.000511
0.90
0.82

protein 12

211336_x_at
NM_001081637
LILRB1
leukocyte
0.000513
1.08
1.32

immunoglobulin-like

receptor, subfamily B

(with TM and ITIM

domains), member 1

228904_at
NM_002146
HOXB3
homeobox B3
0.000516
1.05
0.78

232262_at
NM_004278
PIGL
phosphatidylinositol
0.000517
1.06
0.83

glycan anchor

biosynthesis, class L

223625_at
NM_032581
FAM126A
family with sequence
0.000522
1.16
1.41

similarity 126, member A

211282_x_at
NM_001039664
TNFRSF25
tumor necrosis factor
0.000525
0.91
0.62

receptor superfamily,

member 25

218454_at
NM_024829
FLJ22662
hypothetical protein
0.000525
1.03
1.32

FLJ22662

206896_s_at
NM_052847
GNG7
guanine nucleotide
0.000531
0.67
0.72

binding protein (G

protein), gamma 7

217118_s_at
NM_001009880
C22orf9
chromosome 22 open
0.000532
1.10
1.44

reading frame 9

225619_at
NM_001040153
SLAIN1
SLAIN motif family,
0.000534
0.95
0.63

member 1

203450_at
NM_001002880
CBY1
chibby homolog 1
0.000536
0.95
0.77

(Drosophila)

236436_at
NM_001077241
SLC25A45
solute carrier family 25,
0.000536
0.99
0.83

member 45

209734_at
NM_005337
NCKAP1L
NCK-associated
0.000546
1.01
1.29

protein 1-like

208807_s_at
NM_001005271
CHD3
chromodomain
0.000553
1.00
0.82

helicase DNA binding

protein 3

228026_at
NM_001102396
SIKE
suppressor of IKK
0.000555
0.99
0.80

epsilon

201675_at
NM_003488
AKAP1
A kinase (PRKA)
0.000561
1.11
0.86

anchor protein 1

47560_at
NM_001008701
LPHN1
latrophilin 1
0.000563
1.03
0.78

222164_at
NM_015850
FGFR1
fibroblast growth factor
0.000566
1.11
0.84

receptor 1 (fms-related

tyrosine kinase 2,

Pfeiffer syndrome)

210031_at
NM_000734
CD247
CD247 molecule
0.000573
0.88
0.68

205603_s_at
NM_006729
DIAPH2
diaphanous homolog 2
0.000575
1.07
1.33

(Drosophila)

212593_s_at
NM_014456
PDCD4
programmed cell death
0.000576
0.90
0.80

4 (neoplastic

transformation

inhibitor)

228950_s_at
NM_001002292
GPR177
G protein-coupled
0.000577
0.62
0.73

receptor 177

209389_x_at
NM_001079862
DBI
diazepam binding
0.000603
1.07
1.31

inhibitor (GABA

receptor modulator,

acyl-Coenzyme A

binding protein)

212658_at
NM_005779
LHFPL2
lipoma HMGIC fusion
0.000608
0.77
1.22

partner-like 2

206770_s_at
NM_012243
SLC35A3
solute carrier family 35
0.000614
1.11
0.87

(UDP-N-

acetylglucosamine

(UDP-GlcNAc)

transporter), member

A3

1566448_at
NM_006725
CD6
CD6 molecule
0.000616
0.96
0.73

219298_at
NM_024693
ECHDC3
enoyl Coenzyme A
0.000622
0.50
0.89

hydratase domain

containing 3

201536_at
NM_004090
DUSP3
dual specificity
0.000623
1.12
1.48

phosphatase 3

(vaccinia virus

phosphatase VH1-

related)

213926_s_at
NM_004504
HRB
HIV-1 Rev binding
0.000625
0.76
1.22

protein

227093_at
NM_025090
USP36
Ubiquitin specific
0.000626
1.06
0.83

peptidase 36

219351_at
NM_001011658
TRAPPC2
trafficking protein
0.00063
0.95
0.83

particle complex 2

204040_at
NM_014746
RNF144A
ring finger protein 144A
0.000631
0.88
0.67

228109_at
NM_006909
RASGRF2
Ras protein-specific
0.000633
0.84
0.54

guanine nucleotide-

releasing factor 2

204099_at
NM_001003801
SMARCD3
SWI/SNF related,
0.000634
0.94
1.63

matrix associated, actin

dependent regulator of

chromatin, subfamily d,

member 3

204247_s_at
NM_004935
CDK5
cyclin-dependent
0.000637
1.00
1.28

kinase 5

218532_s_at
NM_001034850
FAM134B
family with sequence
0.000651
0.93
0.73

similarity 134, member B

230531_at
NM_004977
KCNC3
potassium voltage-
0.000661
0.97
1.14

gated channel, Shaw-

related subfamily,

member 3

232065_x_at
NM_033319
CENPL
centromere protein L
0.000661
1.09
0.82

243982_at
NM_017658
KLHL28
Kelch-like 28
0.000668
0.95
0.80

(Drosophila)

229235_at
NM_032815
NFATC2IP
nuclear factor of
0.000681
1.02
0.75

activated T-cells,

cytoplasmic,

calcineurin-dependent

2 interacting protein

203429_s_at
NM_014283
C1orf9
chromosome 1 open
0.000682
1.00
0.88

reading frame 9

1559413_at
NM_152772
TCP11L2
t-complex 11 (mouse)-
0.000685
0.88
0.74

like 2

209881_s_at
NM_001014987
LAT
linker for activation of T
0.000691
0.96
0.77

cells

204214_s_at
NM_006834
RAB32
RAB32, member RAS
0.000695
0.84
1.24

oncogene family

228359_at
NM_032873
STS-1
Cbl-interacting protein
0.000696
0.97
1.43

Sts-1

205310_at
NM_001080469
FBXO46
F-box protein 46
0.000698
0.91
0.85

227809_at
NM_198581
ZC3H6
zinc finger CCCH-type
0.000698
0.97
0.81

containing 6

210844_x_at
NM_001903
CTNNA1
catenin (cadherin-
0.000699
0.98
1.22

associated protein),

alpha 1, 102 kDa

218764_at
NM_006255
PRKCH
protein kinase C, eta
0.000701
0.94
0.75

221918_at
NM_002595
PCTK2
PCTAIRE protein
0.000703
0.88
0.84

kinase 2

226039_at
NM_012214
MGAT4A
mannosyl (alpha-1,3-)-
0.000712
0.93
0.76

glycoprotein beta-1,4-

N-

acetylglucosaminyltransferase,

isozyme A

224734_at
NM_002128
HMGB1
high-mobility group box 1
0.000719
0.98
0.85

224027_at
NM_148672
CCL28
chemokine (C-C motif)
0.000727
0.93
0.79

ligand 28

234978_at
NM_152313
SLC36A4
solute carrier family 36
0.000734
0.91
1.14

(proton/amino acid

symporter), member 4

209870_s_at
NM_005503
APBA2
amyloid beta (A4)
0.000747
0.88
0.80

precursor protein-

binding, family A,

member 2 (X11-like)

229725_at
NM_001009185
ACSL6
Acyl-CoA synthetase
0.000754
0.95
0.55

long-chain family

member 6

46665_at
NM_017789
SEMA4C
sema domain,
0.000763
0.88
0.75

immunoglobulin

domain (Ig),

transmembrane

domain (TM) and short

cytoplasmic domain,

(semaphorin) 4C

219972_s_at
NM_022495
C14orf135
chromosome 14 open
0.000763
0.97
0.78

reading frame 135

200785_s_at
NM_002332
LRP1
low density lipoprotein-
0.000764
1.12
1.35

related protein 1

(alpha-2-macroglobulin

receptor)

204520_x_at
NM_014577
BRD1
bromodomain
0.000776
0.97
0.89

containing 1

205583_s_at
NM_001039210
CXorf45
chromosome X open
0.00078
0.91
0.74

reading frame 45

212454_x_at
NM_005463
HNRPDL
Heterogeneous nuclear
0.000782
1.06
0.88

ribonucleoprotein D-

like

213587_s_at
NM_001100592
ATP6V0E2
ATPase, H+
0.000788
0.94
0.78

transporting V0 subunit

e2

225245_x_at
NM_018267
H2AFJ
H2A histone family,
0.00079
0.98
1.33

member J

227361_at
—
HS3ST3B1
heparan sulfate
0.000793
0.82
0.62

(glucosamine) 3-O-

sulfotransferase 3B1

219724_s_at
NM_001098815
KIAA0748
KIAA0748
0.000798
1.00
0.73

204614_at
NM_002575
SERPINB2
serpin peptidase
0.000801
1.47
2.17

inhibitor, clade B

(ovalbumin), member 2

210054_at
NM_024511
C4orf15
chromosome 4 open
0.000806
0.96
0.86

reading frame 15

226100_at
NM_018682
MLL5
myeloid/lymphoid or
0.000808
0.98
0.84

mixed-lineage

leukemia 5 (trithorax

homolog, Drosophila)

224842_at
NM_015092
SMG1
PI-3-kinase-related
0.000809
0.99
0.92

kinase SMG-1

228941_at
NM_001013620
ALG10B
asparagine-linked
0.000811
1.03
0.67

glycosylation 10

homolog B (yeast,

alpha-1,2-

glucosyltransferase)

203723_at
NM_002221
ITPKB
inositol 1,4,5-
0.000812
0.89
0.83

trisphosphate 3-kinase B

222688_at
NM_018367
PHCA
phytoceramidase,
0.000839
1.20
1.45

alkaline

214741_at
NM_003432
ZNF131
zinc finger protein 131
0.000842
1.04
0.84

228370_at
NM_003097
SNRPN
Small nuclear
0.000843
0.82
0.61

ribonucleoprotein

polypeptide N

208963_x_at
NM_013402
FADS1
fatty acid desaturase 1
0.000844
1.42
1.73

221510_s_at
NM_014905
GLS
glutaminase
0.000845
1.04
0.77

218428_s_at
NM_001037872
REV1
REV1 homolog (S. cerevisiae)
0.000846
1.01
0.89

218362_s_at
NM_014953
DIS3
DIS3 mitotic control
0.00085
0.92
0.86

homolog (S. cerevisiae)

242644_at
NM_152468
TMC8
Transmembrane
0.000853
0.94
0.72

channel-like 8

49452_at
NM_001093
ACACB
acetyl-Coenzyme A
0.000854
0.88
0.71

carboxylase beta

209570_s_at
NM_001040101
D4S234E
DNA segment on
0.000859
1.25
0.80

chromosome 4

(unique) 234

expressed sequence

223019_at
NM_001035534
FAM129B
family with sequence
0.00086
1.09
1.54

similarity 129, member B

230852_at
NM_145064
STAC3
SH3 and cysteine rich
0.000876
0.99
1.25

domain 3

220485_s_at
NM_001039508
SIRPG
signal-regulatory
0.000878
0.96
0.75

protein gamma

221011_s_at
NM_030915
LBH
limb bud and heart
0.000878
0.93
0.66

development homolog

(mouse)

222876_s_at
NM_018404
CENTA2
centaurin, alpha 2
0.000879
1.19
1.60

231853_at
NM_016261
TUBD1
tubulin, delta 1
0.000879
0.95
0.81

219038_at
NM_001085354
MORC4
MORC family CW-type
0.000882
0.96
0.78

zinc finger 4

201231_s_at
NM_001428
ENO1
enolase 1, (alpha)
0.000885
1.05
1.19

209504_s_at
NM_021200
PLEKHB1
pleckstrin homology
0.000886
0.89
0.71

domain containing,

family B (evectins)

member 1

215245_x_at
NM_002024
FMR1
fragile X mental
0.000888
0.94
0.84

retardation 1

47571_at
NM_007345
ZNF236
zinc finger protein 236
0.000888
1.06
0.94

205288_at
NM_003672
CDC14A
CDC14 cell division
0.000894
1.08
0.77

cycle 14 homolog A (S. cerevisiae)

218552_at
NM_018281
ECHDC2
enoyl Coenzyme A
0.000894
1.02
0.76

hydratase domain

containing 2

209149_s_at
NM_001014842
TM9SF1
transmembrane 9
0.000894
0.74
1.10

superfamily member 1

216713_at
NM_001013406
KRIT1
KRIT1, ankyrin repeat
0.000899
1.10
0.84

containing

1569652_at
NM_004529
MLLT3
myeloid/lymphoid or
0.000903
0.87
0.59

mixed-lineage

leukemia (trithorax

homolog, Drosophila);

translocated to, 3

218422_s_at
NM_022118
RBM26
RNA binding motif
0.000906
1.02
0.83

protein 26

234923_at
NM_014990
GARNL1
GTPase activating
0.000907
0.94
0.75

Rap/RanGAP domain-

like 1

222141_at
NM_032775
KLHL22
kelch-like 22
0.000907
1.02
0.83

(Drosophila)

207283_at
NR_002229
RPL23AP13
ribosomal protein L23a
0.000917
1.05
0.81

pseudogene 13

205211_s_at
NM_004292
RIN1
Ras and Rab interactor 1
0.00092
0.96
1.18

203665_at
NM_002133
HMOX1
heme oxygenase
0.000921
1.09
1.44

(decycling) 1

226465_s_at
NM_032195
SON
SON DNA binding
0.000932
1.00
0.85

protein

228594_at
NM_001085411
C5orf33
chromosome 5 open
0.000937
1.01
0.86

reading frame 33

211339_s_at
NM_005546
ITK
IL2-inducible T-cell
0.000942
1.00
0.71

kinase

AFFX-
NM_002046
GAPDH
glyceraldehyde-3-
0.000944
0.97
1.17

HUMGAPDH/

phosphate

M33197_5_at

dehydrogenase

205590_at
NM_005739
RASGRP1
RAS guanyl releasing
0.000946
0.93
0.69

protein 1 (calcium and

DAG-regulated)

224439_x_at
NM_014245
RNF7
ring finger protein 7
0.000946
1.06
1.16

219441_s_at
NM_024652
LRRK1
leucine-rich repeat
0.000954
0.81
1.11

kinase 1

1553165_at
NM_007247
AP1GBP1
AP1 gamma subunit
0.000954
1.00
0.82

binding protein 1

225942_at
NM_020726
NLN
neurolysin
0.000956
1.29
1.22

(metallopeptidase M3

family)

216202_s_at
NM_004863
SPTLC2
serine
0.000959
0.76
1.37

palmitoyltransferase,

long chain base

subunit 2

209218_at
NM_003129
SQLE
squalene epoxidase
0.000964
1.37
1.78

206965_at
NM_007249
KLF12
Kruppel-like factor 12
0.000964
0.83
0.56

218911_at
NM_006530
YEATS4
YEATS domain
0.000976
0.97
0.77

containing 4

228680_at
NM_007054
KIF3A
kinesin family member
0.000977
0.96
0.73

3A

202258_s_at
NM_014887
PFAAP5
phosphonoformate
0.000979
1.06
0.94

immuno-associated

protein 5

201486_at
NM_002902
RCN2
reticulocalbin 2, EF-
0.000983
1.07
0.84

hand calcium binding

domain

241871_at
NM_001744
CAMK4
calcium/calmodulin-
0.000985
0.90
0.53

dependent protein

kinase IV

212633_at
NM_015323
KIAA0776
KIAA0776
0.000987
1.14
0.90

218885_s_at
NM_024642
GALNT12
UDP-N-acetyl-alpha-D-
0.000989
0.85
0.70

galactosamine:polypeptide

N-

acetylgalactosaminyltransferase

12 (GalNAc-

T12)

212400_at
NM_001035254
FAM102A
family with sequence
0.00099
0.93
0.67

similarity 102, member A

222744_s_at
NM_018196
TMLHE
trimethyllysine
0.000991
0.92
1.21

hydroxylase, epsilon

206542_s_at
NM_003070
SMARCA2
SWI/SNF related,
0.000993
0.98
0.86

matrix associated, actin

dependent regulator of

chromatin, subfamily a,

member 2

202617_s_at
NM_001110792
MECP2
methyl CpG binding
0.000996
0.92
0.87

protein 2 (Rett

syndrome)

1560703_at
NM_000625
NOS2A
Nitric oxide synthase
0.001
0.89
0.72

2A (inducible,

hepatocytes)

TABLE 3

Survival Genes - Note that genes are listed in order of decreasing

statistical significance/preference.

Gene

Logrank

Probe set
RefSeq
symbol
Gene Description
test, p-value
Coefficient

218532_s_at
NM_001034850
FAM134B
family with sequence
0.0000188
0.191890372

similarity 134, member B

226039_at
NM_012214
MGAT4A
mannosyl (alpha-1,3-)-
0.0000645
0.206097886

glycoprotein beta-1,4-

N-

acetylglucosaminyltransferase,

isozyme A

212655_at
NM_015144
ZCCHC14
zinc finger, CCHC
0.0000688
0.439999867

domain containing 14

206545_at
NM_006139
CD28
CD28 molecule
0.0000863
0.451195806

203939_at
NM_002526
NT5E
5′-nucleotidase, ecto
0.0000945
0.138687171

(CD73)

228109_at
NM_006909
RASGRF2
Ras protein-specific
0.0001183
0.251103243

guanine nucleotide-

releasing factor 2

1553132_a_at
NM_152332
TC2N
tandem C2 domains,
0.0001366
0.3599156

nuclear

201656_at
NM_000210
ITGA6
integrin, alpha 6
0.0001632
0.264869311

214582_at
NM_000922
PDE3B
phosphodiesterase 3B,
0.0002216
0.300332089

cGMP-inhibited

201677_at
NM_001006109
C3orf37
Chromosome 3 open
0.0002250
0.082398123

reading frame 37

203408_s_at
NM_002971
SATB1
SATB homeobox 1
0.0003166
0.105602874

225864_at
NM_174911
FAM84B
family with sequence
0.0003358
0.386161889

similarity 84, member B

209674_at
NM_004075
CRY1
cryptochrome 1
0.0003426
0.386134127

(photolyase-like)

209657_s_at
NM_004506
HSF2
heat shock
0.0003643
0.154506655

transcription factor 2

223092_at
NM_054027
ANKH
ankylosis, progressive
0.0004883
0.264908361

homolog (mouse)

205259_at
NM_000901
NR3C2
nuclear receptor
0.0005284
0.305743767

subfamily 3, group C,

member 2

205005_s_at
NM_004808
NMT2
N-myristoyltransferase 2
0.0005364
0.465298698

211339_s_at
NM_005546
ITK
IL2-inducible T-cell
0.0005474
0.328170825

kinase

218473_s_at
NM_024656
GLT25D1
glycosyltransferase 25
0.0005685
25.88043253

domain containing 1

1559097_at
—
C14orf64
chromosome 14 open
0.0005902
0.268401623

reading frame 64

220485_s_at
NM_001039508
SIRPG
signal-regulatory
0.0006799
0.197558497

protein gamma

209881_s_at
NM_001014987
LAT
linker for activation of T
0.0008250
0.164825676

cells

224968_at
NM_080667
CCDC104
coiled-coil domain
0.0008433
0.310483663

containing 104

227361_at
—
HS3ST3B1
heparan sulfate
0.0009171
0.4056675

(glucosamine) 3-O-

sulfotransferase 3B1

209870_s_at
NM_005503
APBA2
amyloid beta (A4)
0.0009945
0.143757862

precursor protein-

binding, family A,

member 2 (X11-like)

46665_at
NM_017789
SEMA4C
sema domain,
0.0010157
0.175513963

immunoglobulin

domain (Ig),

transmembrane

domain (TM) and short

cytoplasmic domain,

(semaphorin) 4C

205288_at
NM_003672
CDC14A
CDC14 cell division
0.0012955
0.20206578

cycle 14 homolog A (S. cerevisiae)

230078_at
NM_016340
RAPGEF6
Rap guanine
0.0013072
0.12377883

nucleotide exchange

factor (GEF) 6

235048_at
NM_015566
KIAA0888
KIAA0888 protein
0.0013164
0.418246256

214049_x_at
NM_006137
CD7
CD7 molecule
0.0014992
0.217794866

219387_at
NM_018084
CCDC88A
coiled-coil domain
0.0016015
2.4251108

containing 88A

212538_at
NM_015296
DOCK9
dedicator of cytokinesis 9
0.0016303
0.3086959

204040_at
NM_014746
RNF144A
ring finger protein 144A
0.0016546
0.386430727

211282_x_at
NM_001039664
TNFRSF25
tumor necrosis factor
0.0017567
0.461061973

receptor superfamily,

member 25

222895_s_at
NM_022898
BCL11B
B-cell CLL/lymphoma
0.0019714
0.442108802

11B (zinc finger

protein)

226247_at
NM_001001974
PLEKHA1
pleckstrin homology
0.0021405
0.360753139

domain containing,

family A

(phosphoinositide

binding specific)

member 1

229725_at
NM_001009185
ACSL6
Acyl-CoA synthetase
0.0027287
0.334884067

long-chain family

member 6

1556402_at
—
FLJ46446
Hypothetical gene
0.0030901
0.327796466

supported by

AK128305

241871_at
NM_001744
CAMK4
calcium/calmodulin-
0.0032142
0.424186978

dependent protein

kinase IV

210031_at
NM_000734
CD247
CD247 molecule
0.0032530
0.400517646

214195_at
NM_000391
TPP1
tripeptidyl peptidase I
0.0036692
284.1586694

212981_s_at
NM_014719
FAM115A
family with sequence
0.0039653
0.2380392

similarity 115, member A

202664_at
NM_001077269
WIPF1
WAS/WASL interacting
0.0040573
0.032530289

protein family, member 1

205434_s_at
NM_001012987
AAK1
AP2 associated kinase 1
0.0042792
0.334485861

226682_at
—
LOC283666
hypothetical protein
0.0045761
0.507380631

LOC283666

212609_s_at
NM_005465
AKT3
V-akt murine thymoma
0.0047050
0.25152629

viral oncogene

homolog 3 (protein

kinase B, gamma)

209570_s_at
NM_001040101
D4S234E
DNA segment on
0.0047217
0.418667259

chromosome 4

(unique) 234

expressed sequence

202970_at
NM_003583
DYRK2
dual-specificity
0.0049630
0.16334198

tyrosine-(Y)-

phosphorylation

regulated kinase 2

214470_at
NM_002258
KLRB1
killer cell lectin-like
0.0049710
0.525559684

receptor subfamily B,

member 1

202704_at
NM_005749
TOB1
transducer of ERBB2, 1
0.0051841
0.162338576

212259_s_at
NM_020524
PBXIP1
pre-B-cell leukemia
0.0057089
0.158782145

homeobox interacting

protein 1

214442_s_at
NM_004671
PIAS2
protein inhibitor of
0.0057920
2.837870356

activated STAT, 2

218764_at
NM_006255
PRKCH
protein kinase C, eta
0.0062209
0.345585393

209604_s_at
NM_001002295
GATA3
GATA binding protein 3
0.0075146
0.437278076

205590_at
NM_005739
RASGRP1
RAS guanyl releasing
0.0087734
0.426211611

protein 1 (calcium and

DAG-regulated)

209884_s_at
NM_003615
SLC4A7
solute carrier family 4,
0.0090340
0.218893307

sodium bicarbonate

cotransporter, member 7

210054_at
NM_024511
C4orf15
chromosome 4 open
0.0100695
0.131111638

reading frame 15

204642_at
NM_001400
EDG1
endothelial
0.0103863
0.379777976

differentiation,

sphingolipid G-protein-

coupled receptor, 1

227626_at
NM_133367
PAQR8
progestin and adipoQ
0.0104034
0.236527598

receptor family

member VIII

218723_s_at
NM_014059
C13orf15
chromosome 13 open
0.0107424
0.458785487

reading frame 15

47560_at
NM_001008701
LPHN1
latrophilin 1
0.0110522
0.321941043

218885_s_at
NM_024642
GALNT12
UDP-N-acetyl-alpha-D-
0.0118176
0.093592166

galactosamine:polypeptide

N-

acetylgalactosaminyltransferase

12 (GalNAc-

T12)

225619_at
NM_001040153
SLAIN1
SLAIN motif family,
0.0120944
0.411766455

member 1

209368_at
NM_001979
EPHX2
epoxide hydrolase 2,
0.0121956
0.16566281

cytoplasmic

209798_at
NM_002519
NPAT
nuclear protein, ataxiatelangiectasia
0.0123399
0.21571892

locus

218454_at
NM_024829
FLJ22662
hypothetical protein
0.0125517
6.434077987

FLJ22662

228065_at
NM_182557
BCL9L
B-cell CLL/lymphoma
0.0128291
0.239389548

9-like

203665_at
NM_002133
HMOX1
heme oxygenase
0.0133210
3.640692548

(decycling) 1

243492_at
NM_053055
THEM4
Thioesterase
0.0134817
0.319867508

superfamily member 4

224833_at
NM_005238
ETS1
v-ets erythroblastosis
0.0141079
0.365618531

virus E26 oncogene

homolog 1 (avian)

213908_at
NR_003521
WHDC1L1
WAS protein homology
0.0143315
0.261281401

region 2 domain

containing 1-like 1

228950_s_at
NM_001002292
GPR177
G protein-coupled
0.0144013
0.557215355

receptor 177

204773_at
NM_004512
IL11RA
interleukin 11 receptor,
0.0162697
0.407147756

alpha

204099_at
NM_001003801
SMARCD3
SWI/SNF related,
0.0165894
2.41590844

matrix associated, actin

dependent regulator of

chromatin, subfamily d,

member 3

212400_at
NM_001035254
FAM102A
family with sequence
0.0185661
0.403641178

similarity 102, member A

205603_s_at
NM_006729
DIAPH2
diaphanous homolog 2
0.0193058
4.169717361

(Drosophila)

228853_at
XM_001125680
LOC730432
similar to
0.0200269
0.150319437

serine/threonine/tyrosine

interacting protein

237033_at
NM_001042693
MGC52498
hypothetical protein
0.0200330
0.351911723

MGC52498

221918_at
NM_002595
PCTK2
PCTAIRE protein
0.0201972
0.095861991

kinase 2

212675_s_at
NM_015147
CEP68
centrosomal protein
0.0203975
0.335424361

68 kDa

235125_x_at
NM_198549
FAM73A
family with sequence
0.0209776
0.279735767

similarity 73, member A

206761_at
NM_005816
CD96
CD96 molecule
0.0213812
0.549088648

200965_s_at
NM_001003407
ABLIM1
actin binding LIM
0.0225864
0.451561647

protein 1

231124_x_at
NM_001033667
LY9
lymphocyte antigen 9
0.0243982
0.506529706

202419_at
NM_002035
FVT1
follicular lymphoma
0.0245457
0.249130374

variant translocation 1

205936_s_at
NM_002115
HK3
hexokinase 3 (white
0.0254338
2.921378857

cell)

230490_x_at
NM_012425
RSU1
Ras suppressor protein 1
0.0258599
0.14389267

209504_s_at
NM_021200
PLEKHB1
pleckstrin homology
0.0259312
0.39679049

domain containing,

family B (evectins)

member 1

49452_at
NM_001093
ACACB
acetyl-Coenzyme A
0.0262238
0.18297323

carboxylase beta

201560_at
NM_013943
CLIC4
chloride intracellular
0.0262927
3.251909183

channel 4

215245_x_at
NM_002024
FMR1
fragile X mental
0.0263504
0.070926355

retardation 1

221221_s_at
NM_017415
KLHL3
kelch-like 3
0.0278018
0.483951344

(Drosophila)

224027_at
NM_148672
CCL28
chemokine (C-C motif)
0.0283153
0.116254904

ligand 28

222585_x_at
NM_016618
KRCC1
lysine-rich coiled-coil 1
0.0287697
13.76693584

222435_s_at
NM_016021
UBE2J1
ubiquitin-conjugating
0.0295370
3.05946191

enzyme E2, J1 (UBC6

homolog, yeast)

228423_at
NM_001039580
MAP9
microtubule-associated
0.0308351
0.490096457

protein 9

227639_at
NM_005482
PIGK
phosphatidylinositol
0.0312002
0.244295712

glycan anchor

biosynthesis, class K

208807_s_at
NM_001005271
CHD3
chromodomain
0.0312319
0.187691851

helicase DNA binding

protein 3

218911_at
NM_006530
YEATS4
YEATS domain
0.0319970
0.28642978

containing 4

224698_at
NM_020728
FAM62B
family with sequence
0.0324392
0.341961555

similarity 62 (C2

domain containing)

member B

1552426_a_at
NM_025141
TM2D3
TM2 domain containing 3
0.0348942
0.063258104

222820_at
NM_018996
TNRC6C
trinucleotide repeat
0.0372469
0.348442308

containing 6C

211336_x_at
NM_001081637
LILRB1
leukocyte
0.0418491
4.199248492

immunoglobulin-like

receptor, subfamily B

(with TM and ITIM

domains), member 1

201486_at
NM_002902
RCN2
reticulocalbin 2, EF-
0.0466717
0.235273316

hand calcium binding

domain

227900_at
NM_170662
CBLB
Cas-Br-M (murine)
0.0467392
0.586176881

ecotropic retroviral

transforming sequence b

204484_at
NM_002646
PIK3C2B
phosphoinositide-3-
0.0467784
0.450595829

kinase, class 2, beta

polypeptide

234978_at
NM_152313
SLC36A4
solute carrier family 36
0.0468999
4.702439659

(proton/amino acid

symporter), member 4

218499_at
NM_001042452
RP6-
serine/threonine
0.0475275
0.14086787

213H19.1
protein kinase MST4

1569652_at
NM_004529
MLLT3
myeloid/lymphoid or
0.0484774
0.517632562

mixed-lineage

leukemia (trithorax

homolog, Drosophila);

translocated to, 3

210102_at
NM_014622
LOH11CR2A
loss of heterozygosity,
0.0498673
2.706868224

11, chromosomal

region 2, gene A

TABLE 4

Gene Symbol
ANOVA p
NYHA I-II/Ctrl
NYHA III-IV/Ctrl

CALM1
0.000105
0.98
0.84

PIK3C2B
0.000148
0.87
0.63

CD28
0.000321
1.01
0.58

CD247
0.000573
0.88
0.68

LAT
0.000691
0.96
0.77

ITK
0.000942
1.00
0.71

RASGRP1
0.000946
0.93
0.69

CAMK4
0.000985
0.90
0.53

REFERENCES

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Cunha-Neto, E., V. J. Dzau, P. D. Allen, D. Stamatiou, L. Benvenutti, M. L. Higuchi, N. S. Koyama, J. S. Silva, J. Kalil, and C. C. Liew. 2005. Cardiac gene expression profiling provides evidence for cytokinopathy as a molecular mechanism in Chagas' disease cardiomyopathy. Am J. Pathol. 167:305-313.

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Schocken, D. D., E. J. Benjamin, G. C. Fonarow, H. M. Krumholz, D. Levy, G. A. Mensah, J. Narula, E. S. Shor, J. B. Young, and Y. Hong. 2008. Prevention of heart failure: a scientific statement from the American Heart Association Councils on Epidemiology and Prevention, Clinical Cardiology, Cardiovascular Nursing, and High Blood Pressure Research; Quality of Care and Outcomes Research Interdisciplinary Working Group; and Functional Genomics and Translational Biology Interdisciplinary Working Group. Circulation 117:2544-2565

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METHODS, KITS AND COMPOSITIONS FOR DETERMINING SEVERITY AND SURVIVAL OF HEART FAILURE IN A SUBJECT

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