METHOD FOR DIAGNOSING BLADDER CANCER BY ANALYZING DNA METHYLATION PROFILES IN URINE SEDIMENTS AND ITS KIT

FIELD OF THE INVENTION

The present invention relates to kits and methods for diagnosing bladder cancer by detecting the altered DNA methylation pattern of the specific sequences in the promoter CpG island of genes in urine sediments from individuals with bladder cancer (including pre-neoplastic stages) as compared to that from the normal individuals (or individuals without bladder cancer).

BACKGROUND OF THE INVENTION

Having the genetic blueprint for human and increasing number of model organisms available has ushered in a new era for the genetic makeup and functional elucidation in development and disease states, which chiefly concerns analysis and annotation of the epigenetic information that inheritable through cell division without changes in DNA sequence. The epigenetics consists of DNA methylation (cytosine [CpG] methylation), non-coding RNA, histone modification, and chromatin remodeling. This interface sits between the genetic blueprints stored in genomic DNA sequences and phenotypes dictated by the pattern of gene expression. It more readily responds to the changing environment than its sequence based genetic counterparts [1]. Addition of the methyl group at cytosine ring within 5′-CpG-3′ sequence (FIG. 1) was carried out by one of the three DNA methyl transferase genes (DNMT1, DNMT3a, and DNMT3b) using S-adenosyl methionine as the methyl donor. The DNA methylation pattern in the parental cells can be faithfully duplicated and distributed into daughter cells in a fashion similar to the semi-conservative replication mechanism for the genetic information. DNA methylation is the key mechanism determining the transcriptional memory. The pattern of DNA methylation changes markedly during the early embryonic development as well as germ cell maturation (the epigenetic reprogramming), and moderately throughout the life of living organisms. Abnormal epigenetic homeostatic mechanism would lead to accumulation of the epigenetic lesions, and ultimately the various diseases states, including cancer[2].

Cancers are extremely complex diseases with extensive genetic and epigenetic defects. The defects vary with both types of cancer and individual patients[3]. DNA methylation based on the enzymatic process to add the methyl group at the fifth carbon of cytosines within the palindromic dinucleotide 5′-CpG-3′ sequence (DNA methylation)(FIG. 1) is the best studied epigenetic mechanism and the focus of cancer epigenetic study.

Over 85% CpG dinucleotides are spread out in the repetitive sequences with the transcription-dependent transposition potential. They are heavily hypermethylated/transcription-silenced, a state required for the genome integrity. The extensive hypomethylated state of genome in cancer cells leads to the transcription of the repetitive sequences and enhancement of transposition activity [2,4], which, subsequently, increases genomic instability and transcription of proto-oncogenes [5,6]. The remaining CpG are clustered within the short DNA regions (approximately, 0.2 to 1 kb in length), known as “CpG island”. Approximately 40-50% of the genes have CpG island within or around the promoter, indicating that transcription of these genes can be regulated by DNA methylation-mediated mechanism. Although mostly unmethylated in normal cells, some of them are often hypermethylated and the transcriptional silencing, including the tumor suppressor genes, DNA repairing genes, cell cycle control genes, anti-apoptotic genes, and the like.

The critical role of the epigenetic abnormality at the early stage of carcinogenesis can be presented as loss of genetic imprinting (LOI). For example, overexpression of the genetic imprinting gene IGF2 can promote cell proliferation, and LOI of which was found in normal-appearing colonic epithelium of patients with colorectal cancer, and LOI of this gene in circulating leucocytes is a crucial feature of subjects susceptible to colon cancer[7]. The hypermethylation/transcription silencing of the tumor suppressor and DNA repairing genes was common at the pre-neoplastic stage[8,9]. For instance, the hypermethylated p16ink4A (tumor suppressor gene) and MGMT (DNA repairing gene) were found in the sputum DNA[8]. Abnormal epigenetic state can also result in abnormal proliferation of stem cells, promoting carcinogenesis. The association of H. pyrio infection with the aberrant DNA methylation of a given set of genes suggests detection of DNA methylation provide a pre-warning [10]. Therefore, the tumor warning value of analysis of the DNA methylation of the peripheral DNA (serum, stool, sputum, and urine sediments as the sample sources) from the population at high risk for cancer has been also seriously considered.

In terms of incidence, Bladder cancer is the fourth most common cancer in men and the eighth most common cancer in women in the United States[11]. Its incidence increases dramatically in industrializing China[12]. Although over 70% patients suffering from the superficial lesions could be cured surgically, still 50-70% of those patients will return with more severe conditions and poor prognosis. The bladder cancers at similar pathologic grades and stages have variable clinical behaviors[15], illustrating the substantial deficiency of the exsting system. The gold standard for bladder cancer diagnosis is cystoscopy along with biopsy, but the misdiagnosis rate can be up to 10-40% [16-18]. Urine cytology is a non-invasive detection method with high specificity, but suffered from the low sensitivity for Ta, G1, and T1 bladder cancers [19]. The attempt of use of genetic detection of cellular DNA in urine sediments in diagnosing bladder cancer has involved TP53 gene mutations, loss of heterozygosity, microsatellite instability, and E-cadherin promoter polymorphism (51) [20,21]. A method of seeking for chromosomatic abnormality by in situ cell hybridization in urine sediments is reported to detect 68.6% bladder cancer with 77.7% specificity (http://www.urovysion.com). Many attempts using protein marker were reported [22,23]. Although the assay for protein MNP22 in urine seems more sensitive than the urine cytology, it suffered from a substantial deficiency of the high level of the said protein in patients with benign urinogenital diseases such as hematuria, urocystitis, renal calculi, or urinary tract infections[24]. Therefore, there is still a need for developing a more sensitive and specific method for diagnosing bladder cancer and other types of urinogenital cancers, especially at the early stage thereof.

DNA methylation analysis methods generally rely on methylation modification of the original genomic DNA before any amplification step, comprising using the methylation-sensitive restriction enzyme digestion and bisulphite treatment [25]. The latter one exploited the sharp difference in the sensitivity to the bisulphite-mediated deamination (C to U conversion) between cytosine and methylated cytosine residues, which enable detection of as few as 1-10 tumor cells among 10⁴normal cells[25]. Attempts of assaying methylation patterns of genes in bodily fluids, including bronchoalveolar lavage fluid, stool, serum, or plasma and urine sediments, for in vitro detection of cancer have been intensively reported. Other methods of detecting DNA methylation pattern include methylation-specific enzyme digestion, methylation-sensitive single nucleotide primer extension (MS-SnuPE) [26], restriction landmark genomic scanning (RLGS) [27], differential methylation hybridization (DMH) [28], BeadArray platform technology (Illumina, USA)[29], and base-specific cleavage and mass spectrometry (Sequenom, USA)[30], as well as those under development or to be developed.

SUMMARY OF INVENTION

To achieve the above purpose, the present inventor has carried out extensive research and firstly discloses the difference of DNA methylation patterns between subjects with bladder cancer and those without bladder cancer, and detection of which may be used to determine bladder cancer in a subject. The method comprises the following steps:

(a) providing urine sediment sample from said subject;

(b) determining methylation pattern of one or more genes in the urine sediments, wherein said genes are selected from a group consisting of ABCC13, ABCC6, ABCC8, ALX4, APC, BCAR3, BCL2, BMP3B, BNIP3, BRCA1, BRCA2, CBR1, CBR3, CCNA1, CDH1, CDH13, CDKN1C, CFTR, COX2, DAPK1, DRG1, DRM, EDNRB, FADD, GALC, GSTP1, HNF3B, HPP1, HTERT, ICAM1, ITGA4, LAMA3, LITAF, MAGEAI, MDR1, MGMT, MINT1, MINT2, MT1GMT, MINT1, MINT2, MT1A, MTSS1, MYOD1, OCLN, p14ARF, p16INK4a RASSF1A, RPRM, RUNX3, SALL3, SERPINB5, SLC29A1, STAT1, TMS1, TNFRSF10A, TNFRSF10C, TNFRSF10D, TNFRSF21, and WVVOX;

(c) comparing methylation pattern of said genes in the urine sediment sample from said subject with that from normal subject, wherein the hypermethylation of one or more of genes indicates that said subject is suffering from bladder cancer.

The present invention further provides the procedures and standards for methylation pattern analysis and determining bladder cancer in a subject. The methods and standards will be used in diagnosing, prognosing, and monitoring the recurrence, and determining whether the tumors have been surgically removed. Other advantages and features of the present invention have been further disclosed in the following specific embodiments with reference to the accompanied figures.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 provides a flow chart of cytosine (CpG) methylation. In FIG. 1A, DNA methyltransferases (DNMT) 1, 3a, or 3b catalyzes the addition of a methyl group (the circled CH₃) at position 5 of the pyrimidine ring of the cytosine nucleotide by using S-adenosyl methionine (SAM-CH₃) as a methyl donor. In FIG. 1B, a C-to-T transition is initiated by sulfonation of cytosine (1, cytosine to cytosine sulfonate), then hydrolytic deamination occurs (2, cytosine sulfonate to uracil sulfonate), with the process concluded by alkali desulfonation (3, uracil sulfonate to uracil). Methylated cytosine resists this chemical treatment; thus, methylated versus unmethylated CpG can be detected by a subsequent polymerase chain reaction (PCR), including methylation-specific PCR.

FIG. 2 shows the analysis results of methylation specific PCR of 20 genes and sequencing verification.

This figure shows the electrophoretogram of MSP data of the representative methylation state and its sequencing verification. The number above each lane is the Identification Number of patient, cell lines (5637, T24, and SCaBER). M Sss1 indicates the result of normal liver tissue DNA modified by methylation by M Sss1 methyl transferase in a tube used as positive control. Gene names are listed above each panel. The wild-type sequences and the sequences of representative PCR products cloned from T vectors are aligned.

FIG. 3 shows the MSP analysis results of 11 valuable genes in 15 tumor tissue samples and 9 urine sediment samples. FIG. 3A illustrates the electrophoretogram of the MSP results, the involved gene is indicated on the top right corner of each panel. As a loading reference, the electrophoretogram of non-methylated MSP product of CFTR gene (marked as CFTRu) is shown.

Note: Ur: urine sediment, T: tumor tissue, G XX: No. of clinical samples, BJ, bisulphate-treated DNA derived from a normal fibroblast cell line, used as control of non-methylated DNA template. H₂O: control without DNA template. M. Sss I: positive control of methylated template of methylated DNA derived from normal liver tissue in a tube.

FIG. 3B summarizes the results from analysis of 9 pairs of the matched tumor tissues and urine sediments. The filled boxes indicate the methylated targets, and the empty boxes indicate the unmethylated targets.

FIG. 3C shows a histogram of the matching profile of the DNA methylation patterns in the matched tumor tissues and urine sediments.

Y axis: the percentage of methylation targets in a subgroup. T/Ur: commonly methylated in both tumor tissues and urine sediments; T; only methylated in tissues, and Ur: only methylated in urine sediments. The number of events and (percentage) are shown at the top of each column.

FIG. 4 shows the gene methylation state in urine sediments from patients with bladder cancer and patients with non-cancerous urinogenital lesions. The lower panel describes the methylation frequency (y axis, %) of each gene (x axis) in the urine sediments from patients with bladder cancer (column 2) and patients with non-cancerous urinogenital lesions (column 3, FIG. 4A). CI (Confidence Index): The values of each gene within 95% confidence interval are presented as a perpendicular line on the panel. The positions of p values of <0.01 and <0.05 are indicated as their methylation states can be used as a marker for bladder cancer.

FIG. 5 shows the ROC (RECEIVER OPERATING CHARACTERISTICS) values of the sensitivities and specificities of the informative gene sets for bladder cancer detection. Both the sensitivity (%, Column 4, in FIG. 5A) and specificity (%, Column 5, FIG. 5A) of each gene set were calculated and plotted.

DETAILED DESCRIPTION OF EMBODIMENTS

In one aspect, the present invention provides a method for detecting bladder cancer in a subject, comprising the following steps:

(a) providing a urine sediment sample from said subject;

(b) determining the methylation pattern of one or more genes in the urine sediments, wherein said genes are selected from a group consisting of ABCC13, ABCC6, ABCC8, ALX4, APC, BCAR3, BCL2, BMP3B, BNIP3, BRCA1, BRCA2, CBR1, CBR3, CCNA1, CDH1, CDH13, CDKNIC, CFTR, COX2, DAPK1, DRG1, DRM, EDNRB, FADD, GALC, GSTP1, HNF3B, HPP1, HTERT, ICAM1, ITGA4, LAMA3, LITAF, MAGEA1, MDR1, MGMT, MINT1, MINT2, MT1GMT, MINT1, MINT2, MT1A, MTSS1, MYOD1, OCLN, p14ARF, p16INK4a, PTCHD2, RASSF1A, RPRM, RUNX3, SALL3, SERPINB5, SLC29A1, STAT1, TIMP3, TMS1, TNFRSF10A, TNFRSF10C, TNFRSF10D, TNFRSF21, and WWOX;

(c) comparing the methylation pattern of one or more genes in the sample from said subject with that in the sample from normal subject, wherein the hypermethylated state in one or more genes indicates that said subject suffered from bladder cancer.

As used herein, the term “sample” in the context of the present invention is defined to include any sample obtained from any individual which is proper to test for DNA methylation, for example, those samples taken from the subjects with urinogenital symptoms. The term “urine sediment” has the meaning well known by a person skilled in the art, which includes the epithelial cells exfoliated from urethra, and etc. The cytological analysis of urine sediment has been used in clinical diagnosis of bladder cancer, since cells from bladder tumors are often exfoliated into urine sediment.

The sample being used in the present invention may also be the established bladder cancer cell lines, such as T24 (ATCC number: HTB-4), SCaBER (HTB-3), and 5637(HTB-9).

The present method is applicable to determine the urinogenital cancer. Said urinogenital cancer may include, for example, bladder cancer, prostate cancer, and kidney cancer. (Other types of cancer whose cells can be present in urine may also be detected by the present method. As a result, the “urinogenital cancers” are also included in the scope of the present invention.

The term “subject” as used herein includes, but not limited to, mammal, such as human.

The term “methylation” and “hypermethylation”, used interchangeably herein, are defined as the presence or high methylation of CpG loci within a gene sequence, most often within the promoter of a gene. When MSP is used, the tested DNA (gene) region can be considered to be hypermethylated if a positive PCR result is obtained from a PCR reaction using methylation-specific primers. Using Real-time Quantitative Methylation-Specific PCR, the hypermethylated state can be determined according to the statistically significant difference in comparison with the relative value of the methylation state of the control sample.

The basis of the present invention lies in that the methylation profiling of CpG sequence (for example, the region within the promoter CpG island of a tumor related gene, known as gene infra) from individuals suffering from bladder cancer is different from normal individuals or those whithout bladder cancer. As a result, the methylation state of one or more of the following genes may be used as an indicator of presence of bladder cancer in the subject. These genes may be selected from a group consisting of ABCC13, ABCC6, ABCC8, ALX4, APC, BCAR3, BCL2, BMP3B, BNIP3, BRCA1, BRCA2, CBR1, CBR3, CCNA1, CDH1, CDH13, CDKNIC, CFTR, COX2, DAPK1, DRG1, DRM, EDNRB, FADD, GALC, GSTP1, HNF3B, HPP1, HTERT, ICAM1, ITGA4, PTCHD2, LAMA3, LITAF, MAGEA1, MDR1, MGMT, MINT1, MINT2, MT1A, MTSS1, MYOD1, OCLN, p14ARF, p16INK4a, PTCHD2, RASSF1A, RPRM, RUNX3, SALL3, SERPINB5, SLC29A1, STAT1, TIMP3, TMS1, TNFRSF10A, TNFRSF10C, TNFRSF10D, TNFRSF21, and WWOX.

More particularly, the hypermethylation state of any gene selected from a group consisting of SALL3, CFTR, ABCC6, HPR1, RASSF1A, MT1A, RUNX3, ITGA4, BCL2, ALX4, MYOD1, DRM, CDH13, BMP3B, CCNA1, RPRM, MINT1, and BRCA1, in the urine sediment indicates that said subject is suffering from bladder cancer.

The methylation pattern of cellular DNA in the urine sediments may be determined by any techniques that are known (e.g. methylation-specific PCR(MSP) and Real-time Quantitative Methylation-Specific PCR, Metylite) or are under developing and to be developed. After bisulfite treatment, the unmethylated cytosines are converted to uracils, while the methylated cytosines remain unconverted. Subsequently, the DNA methylation state in the subject DNA is determined by amplifying the DNA after bisulfite treatment using primers capable of distinguishing methylated DNA from unmethylated DNA (30). This PCR approach, known as MSP can be used to detect small amount of tumor cells from a clinical sample with many normal cells with the proviso that the methylation state of the indicated DNA region (gene) in normal cells is opposite to that in tumor cells. It is possible to identify 1 tumor cells from 10,000 normal cells by using MSP.

It is preferred to use quantitative methylation-specific PCR (QMSP) in detection of methylation level. This method is based on the continuous optical monitoring of a fluorogenic PCR, which is more sensitive than the MSP method (31). It is a high-throughput technique and avoids analyzing its result by electrophoresis. The methods for designing primers and probes are known to the skilled in the art.

Additional useful techniques include methylation-specific enzyme digestion, bisulfite DNA sequencing, methylation-sensitive single nucleotide primer extension (MS-SnuPE) [26], restriction landmark genomic scanning (RLGS) [27], differential methylation hybridization (DMH) [28], BeadArray platform technology (Illumina, USA) [29], and a base-specific cleavage/mass spectrometry (Sequenom, USA)[30], and etc.

For a large sample analysis (comprising being compared with normal and/or non-cancerous subject), the methylation patterns of multiple tumor related genes are obtained, that is, it is possible to detect bladder cancer or other urinogenital cancer (prostate cancer or kidney cancer) in a subject by measuring methylation state of the gene sets.

The present invention also provides a kit for bladder cancer detection, comprising:

(a) means for measuring methylation pattern of one or more genes in the urine sediments, wherein said genes are selected from a group consisting of ABCC13, ABCC6, ABCC8, ALX4, APC, BCAR3, BCL2, BMP3B, BNIP3, BRCA1, BRCA2, CBR1, CBR3, CCNA1, CDH1, CDH13, CDKNIC, CFTR, COX2, DAPK1, DRG1, DRM, EDNRB, FADD, GALC, GSTP1, HNF3B, HPP1, HTERT, ICAM1, ITGA4, LAMA3, LITAF, MAGEA1, MDR1, MGMT, MINT1, MINT2, MT1GMT, MINT1, MINT2, MT1A, MTSS1, MYOD1, OCLN, p14ARF, p16INK4a, PTCHD2, RASSF1A, RPRM, RUNX3, SALL3, SERPINB5, SLC29A1, STAT1, TIMP3, TMS1, TNFRSF10A, TNFRSFIOC, TNFRSFIOD, TNFRSF21, and WWOX;

(b) providing a criteria for determining the methylation state of one or more genes to detect urinogenital cancer (e.g. bladder cancer) in the subject (specifically and sensitively).

The term “means for measuring methylation pattern of one or more genes in the urine sediments” includes any substantial technical measures, instruments, devices, and reagents that may be useful to measuring methylation pattern of one or more genes in the urine sediments. The specific means depend on the method used.

Since one preferred method of detecting the methylation state of a panel of genes is MSP and/or QMSP. The reagents included in the MSP and/or QMSP kits of this invention are apparent to the skilled in the art: reagents and materials for DNA isolation, polymerase for PCR reaction (such as Taq polymerase), sodium bisulfite, MSP/QMSP specific buffers and the corresponding primers, etc. All the related reagents (primers, among others) are included in the scope of the present invention. Primers comprise DNA, RNA, and synthetic equivalents thereof, depending on the amplification technique employed. For example, a pair of short single-stranded primers are used in standard PCR, and the two primers are localized to both sides of the target gene to be amplified (including CpG sequence, the complementation to CpG is directed to methylated region, and the complementation to TpG is directed to unmethylated gene region). The nucleic acid amplification techniques are well-known to the skilled in the art.

The present invention provided, for example, a list of verified gene primers (Table 2). However, the scope of the invention is not limited to these examples.

The present invention may also comprises methylation information of corresponding genes in urine sediments (or tissues) obtained from normal and/or non-cancerous subject.

The invention will be further understood with reference to the following examples. It should be noted that all these examples are for purpose of illustration only rather than for limitation of the scope of the invention. Unless otherwise indicated, all the techniques therein are obvious to those having basic knowledge in molecular biochemistry and relevant fields.

EXAMPLES
Methods

Collection of Tissues and Urine Sediments, and DNA Isolation.

With the informed consent of all patients and approval of the ethics committee, 15 samples of bladder cancer tissues were collected in Guangxi Province, China. Three normal bladder tissues were obtained from healthy organ donator. The void morning urine samples were also collected from the bladder cancer patients, diagnosed by the existing methods and standards, known in the clinical arena, at Guangxi Hospital (40) and Zhongshan Hospital, Shanghai, China (92). 79 post-surgical urine samples were also obtained at Zhongshan Hospital, Shanghai, China. The control group included 23 patients with non-cancerous urinogenital diseases (cystitis glandularis: 8, prostatic hyperplasia: 4, vesical calculus: 3, renal calculus: 5, and adrenal nodule: 3), 6 with neurological disease, and 7 healthy volunteers. The urine cytological analysis, and the tumor-node-metastasis (TNM) staging and classification are indicators according to the WHO classification and American Joint Committee on Cancer guidelines.

Bisulfite Treatment and Methylation-Specific PCR Analysis

Primer pairs for PCR detection of 59 methylated and unmethylated alleles were 1, directly from the published information, or 2. designed with software for identification of the CpG islands (http://www.ebi.ac.uk/emboss/cpgplot/index.html) and the primer design software (http://micro-gen.ouhsc.edu/cgi-bin/primer3_www.cgi) (Table 2).

Desalting the DNA samples treated by bisulfite was carried out by a home-made agarose based gel filtration system[31, 32]. The PCR products were cloned and verified by sequencing (FIG. 2 shows 20 genes as examples). The DNA, in vitro methylated by M.Sss I, from normal liver tissues were used as a positive control.

Statistics

The significance analysis of the relation between methylation state of genes and each clinical pathological parameter was carried out by z relevant software (http://www.Rproject.org). The significance of methylation state of each gene as a bladder cancer specific marker is presented as 95% confidence interval (R package Hmisc http://cran.r-project.org/src/contrib/Descriptions/Hmisc.html). The significance of the methylation frequency of each gene in urine sediments from patients with bladder cancer (132 cases) in comparison with that from patients with non-cancerous urinogenital diseases (23 cases) is determined by 2×2 fisher exact test. The receiver operating characteristics (ROC) of both specificity and sensitivity of the gene sets useful in bladder cancer detection were calculated and plotted.

Results
Identification of Genes in a Bladder Cancer-Specific Methylation State

The 59 test genes (table 2) include: 1, those having been investigated in bladder cancer or other types of urinogenital tumors previously, such as CDKN2A, ARF, MGMT, GSTP1, BCL2, DAPK, and HTERT, 2, those being hypermethylated in other types of tumors according to our work [31-43], and 3, those being suggested functionally relate to carcinogenesis by bioinformatics analysis. FIG. 2A shows the methylation states of 11 diagnostically valuable genes in three established bladder cancer cell lines, and the verification of sequence analysis of the methylated and unmethylated target sequences thereof. FIG. 2B shows the MSP data of 20 diagnostically valuable genes and typical results from sequencing confirmation.

Given that the established bladder cancer cell lines are likely to contain deficiencies of clinical bladder cancer at the genetic and epigenetic level, we initially carried out MSP profiling of 59 genes on 3 bladder cancer cell lines: T24 (ATCC number: HTB-4), SCaBER (HTB-3), and 5637 (9). 41 genes were found hypermethylated, at least, in one allele of one cell line (Table 3). Although FADD, LITAF, MGMT and TNFRSF21 are homozygously unmethylated, their hypermethylation states are reported to relate to bladder cancer [44,45]. The following 14 genes have been eliminated in the initial screening: APC, BCAR3, BNIP3, CBR1, CBR3, COX2, DRG1, HNF3B, MDR1, MTSS1, SLC29A1, TIMP3, TNFRFIOA, and VVWOX. In the urine sediments of 11 patients, 21 genes were hypermethylated in 1 to 10 patients (9% to 90%), but not in 3 patients with cystitis glandularis. It is implicated that the hypermethylation states of these genes relate to various degrees of bladder cancer-specificity. The characteristic promoter unmethylation of the MAGEA1 gene and concomitant activation of transcription are frequently found in cancer. However, in the present study of bladder cancer, this phenomenon occurs scarcely (Table 3), the releant study is terminated thereby. This was also the reason to exclude LAMA3, ICAM1, and GALC. We further analyzed 15 cancer tissues and 3 normal bladder tissues for the DNA methylation state of 32 genes. Although 28 genes were unmethylated in the 3 normal bladder tissues, 19 genes among which were hypermethylated in 1-12/15(6.7% to 73.3%) bladder cancer tissues, indicating various degrees of bladder cancer specificity. The other genes: PTCHD2, BRCA1, CDH13, TMS1, CDH1, p14ARF, p16INK4a, FADD, LITAF, MGMT, and TNFRSF2, are also unmethylated. To determine the association of DNA methylation patterns between tumor tissues and cells from urine sediments, we have carried out MSP-profiling of 9 pairs of samples (FIG. 3). Among 99 methylation events, 86 (87%) were shared by the tumor tissues and corresponding urine sediments, 11 (11%) were unique to tumor tissues, and 2 (2%) were unique to urine sediments. The inconsistency is low, but is still 13%. Therefore, the genes only methylated in one kind of samples were included for a further study: BRCA1 and CDH13 (only hypermethylated in tumor tissues), and PTCHD2 (only hypermethylated in urine sediments). TMS1 was also included for the further analysis as it was reported as one of the most informative markers for prostate cancer in USA[44], however, it is not reported to date that its methylation state relates to bladder cancer.

Methylation States of 21 Genes in DNA of Urine Sediments from Bladder Cancer Patients and Non-Bladder Cancer Control Group

The test samples are from bladder cancer cohort (132) and 3 control groups, namely, 1), neurological disease (6), 2), healthy volunteers (7), and 3), non-cancerous urinogenital disease (23), including cystitis glandularis: 8, prostatic hyperplasia: 4, vesical calculus: 3, renal calculus: 5 and adrenal nodule: 3. The average age of the bladder cancer cohort was 63.4 (34-88), which matched well to that for the non-cancerous urinogenital disease cohort, i.e. 55.7 (16-83) and the neurological diseases cohort, i.e. 64.1 (46-78).

The 21 genes were unmethylated in the urine sediments from healthy volunteers and patients with the neurological disease. However, 6 hypermethylation events were recorded in four genes: RASSF1a (2/23), MT1A (2/23), RUNX3 (1/23) and ITGA4 (1/23) (FIG. 4A), which involved 3 patients in non-cancerous urinogenital disease cohort (including 2 patients with prostatic hyperplasia (84, and 64 years old) and 1 patient with vesical calculus (54 years old)). The influence of the “false positive” results on the criteria for bladder cancer detection was taken into consideration by corresponding statistic analysis (FIGS. 4A and 4B). Four relevant genes, with the highest frequency of DNA hypermethylation in urine sediments from bladder cancer patients and in unmethylated states in control cohorts, were identified: SALL3 (58.3%, CI (Confidence Interval): 95%: 49.8%-66.4%), CFTR (55.3% CI: 95%: 46.8%-63.4%), ABCC6 (36.4% CI 95%: 28.7%-44.8%), and HPP1 (34.8% CI 95%: 27.3%-43.3%). The rest 6 genes with a p value of <0.01 were BCL2 (27.3% CI 95%: 20.4%-35.4%), ALX4 (25% CI 95%: 18.4%-33%), RUNX3 (32.6% CI 95%: 25.2%-41%), ITGA4 (31.1%, CI 95%: 23.8%-39.4%), RASSF1A (35.6% CI 95%: 28%-44.1%), and MYOD1 (22% CI 95%: 15.8%-29.8%). The genes with a p value of <0.05 were MT1A (34.8% CI 95%: 27.3%-43.3%), DRM (18.9% CI 95%: 13.2%-26.5%), BMP3B (15.9% CI 95%: 10.6%-23.1%), CCNA1 (15.9% CI 95%: 10.6%-23.1%), and CDH13 (16.7%, CI 95%: 11.3-23.9%). The genes hypermethylated in more than 12.1% of bladder cancer cases are RPRM, MINT1, and BRCA1. These genes may have certain_values in diagnosing bladder cancer. This observation contradicts the previous report [44], both TMS1 (P=1) and GSTP1 (p=1) were found hypermethylated only in 2 bladder cancer patients (5.3% (2/132)). By taking the hypermethylated state of any gene in the 11 genes as an indicator for bladder cancer, 121 of the 132 bladder cancer patients were positive (92%), wherein 6 of 8 are in stage 0a (sensitivity: 75%), 60 of 68 are in stage I (88.2%), 49 of 50 are in stage II (98.2%), 4 of 4 are in stage III (100%), and 2 of 2 are in stage IV (100%)(Table 5). As compared to the results from the urine cytological analysis (detected 1 case in stage I, and 2 cases in stage II, but missed 17 cases, including 4 cases in stage 0a), 19 of 20 cases, except for one case (among four) in stage 0a, were detected by the present analysis, indicating the much higher sensitivity of the present method than the urine cytological analysis.

We failed to find the substantial association of the DNA methylation of genes with cancer staging (Table 5) by the statistic test. Comparing with the DNA methylation state in the urine sediments from 79 post-surgical patients, we found that the methylation incidence of MYOD1 and MINT1 turned from 22.2% and 12.9% before surgery to 0% after surgery, respectively, the incidence of methylation of other genes are also substantially reduced (P<0.005)(Table 6). The methylated genes remained in urine sediment were likely caused by the incomplete removal of tumor by the surgical procedure. Therefore, analysis of the DNA methylation pattern in urine sediments from pre- or post-surgical patients can be effective to assess the surgical quality. Additionally, no significance difference was found in the DNA methylation patterns between the primary and recurrent cases of bladder cancer (p>0.05) (Table 7). The methylation of a single gene (SALL3) can be used to detect at most 58.3% of the bladder cancer cases, and detection of multiple genes may improve the detection rate and specificity for bladder cancer. Hypermethylation of 10 genes results in extremely high tumor-specificity (p<0.01), and hypermethylation of 5 additional genes also results in substantial tumor-specificity (p<0.05 (FIGS. 4A and 4B)). The low frequency of methylation was found in 3 genes in the non-cancerous urinogenital disease control cohort, which has influence on the specificity of these genes as indicator of bladder cancer. “True positive” (TP) was defined as a bladder cancer sample having at least one gene methylated, while “False negative” (FN) was defined as a bladder cancer sample having no gene methylated. “False positive” (FP) was defined as the non-cancerous urinogenital disease sample having at least one gene methylated, while “True negative” (TN) was defined as the non-cancerous urinogenital disease sample having no gene methylated. Both “Sensitivity”=TP/(TP+FN) (%, Column 4 in FIG. 5A) and “specificity”=TN/(TN+FP) (%, Column 5, Table 5A) of each gene were calculated. The receiver operating characteristics (ROC) of both specificity and sensitivity for sets of 2-11 genes were shown in FIG. 5.

None of the following four genes: SALL4, CFTR, ABCC6, and HPP1 were false positive in three control groups, the specificity for them, alone or in combination, to detect bladder cancer should be 100% (FIG. 4). The sensitivity was: 58% (77/132) for SALL3 alone, 74.2% (98/132) for SALL3 and CFTR, 80.3% (106/132) for SALL3, CFTR, and ABCC6, and 82.6% (109/132) for SALL3, CFTR, ABCC6, and HPR1(Column 4 and 5, FIG. 5A).

Bladder
Non-cancerous

cancer (123)
control (23)

Methylated
TP(121)
FP(3)

Unmethylated
FN(12)
TP(20)

The first column indicates the gene sets. The genes in bracket were considered redundant as inclusion thereof did not improve the sensitivity of the set. The second column indicates the number of the true positive (TP=the bladder cancer sample having at least one gene methylated) and false negative (FN=the bladder cancer sample having no gene methylated) events. The third column indicates the number of the false positive (FP=the non-cancerous urinogenital disease sample having at least one gene methylated) and true negative (TN=the non-cancerous urinogenital disease sample having no gene methylated) events. Both Sensitivity=TP/(TP+FN) (%, Column 4) and specificity=TN/(TN+FP) (%, Column 5) of each gene sets were calculated and plotted in FIG. 5A.

The hypermethylated RASSFIA gene was found in 2 of 23 cases in the non-cancerous urinogenital disease group (2 false positive events and 21 true negative events, Column 3, FIG. 4A). Therefore, its inclusion in a 5 gene set improved the sensitivity to 85.6%, with a compromised specificity: 91.3% (Column 4 and 5, FIG. 5A). The six gene set with MT1A had an improved sensitivity: 86.4% and a moderately reduced specificity: 87%, as MT1A was also methylated in another sample of the non-cancerous urinogenital disease group (the accumulated false positive events: 3, and true negative events: 20, column 3, FIG. 5A). Given that further addition of gene RUNX, ITGA4, or BCL2 did not improve the sensitivity of the detection, they were not taken as valuable markers. The sensitivity of a 7 gene set with additional ALX4 is 87.1%, that of a 8 gene set with additional CDH13 is 88.6%, that of a 9 gene set with additional RPRM is 90.2%, that of a 10 gene set with additional MINT is 90.9%, and that of a 11 gene set with additional BRCA1 is 91.7, however, the specificity remained 87%.

Although the aforementioned description relates to particular examples, the spirit and scope of the present invention, and modifications of these information and practical forms according to the established principles are apparent to those skilled in the art. Therefore, such possible modifications should be within the scope of the following claims.

TABLE 1

Molecular Biomarkers for Cancer Detection

Genetic
Epigenetic

Mutation,
DNA
Expressional

SNP, LOH
methylation
mRNA
Protein

Stability
High
High
Low
Low

PCRable
Yes
Yes
Yes
No

Target/gene
Multiple
Single
NA
NA

Nature
Quantita-
Qualita-
Quantita-
Quantita-

tive
tive
tive
tive

Sample purity
Essential
Non-
Essential
Essential

essential

Fluctuation
No
No
Yes
Yes

Tumor type
Low
High
Low
Low

specificity

NA, not applicable; multiple/single: one (single) or more than one (multiple) targets need to be analyzed; fluctuation, whether the amout of the biomarker changes with the fluctuation of non-cancerous factors (biological clock, physiological, or pathological factors); SNP: single nucleotide polymorphism; LOH: loss of heterozygosity.

TABLE 2

Primer list for the MSP-profiling of the promoter CpG islands of the genes

Location of

product

fragment

relative to

Or-

transcription

der

GenBank

initiation
Size

No.
Gene Name
No.
Sense 5′-3′
Antisense 5′-3′
site
(bp)

1
ABCC13M
NT_011512
GCGGGCGGTTTTTATTAG
CAAAAACTCGTCCGTCCA
+314~+478
165

ABCC13U

TGGGTTTGTGGGGTGTT
ACAAAAACTCATCCATCCACAT
+332~+479
148

2
ABCC6M
NT_010393
GGCGTTCGGGGAGTT
CGACCTCGACCCGATAAT
−436~−190
247

ABCC6U

AGGTGTTTGGGGAGTTGG
TCTCAACCTCAACCCAATAATC
−437~−194
244

3
ABCC8M
NT_009237
GACGTGCGGTATTACGTTG
ACAAAAACGCGACAAACG
+72~+254
183

ABCC8U

AGGATGGGGAAGGTGATG
AAAACAAAAACACAACAAACACAC
+75~+282
208

4
ALX4M
NT_009237
GAGTTTGAGGTTGTCGTTCG
AACCCGTTACGACGCTAAAC
+311~+539
229

ALX4U

TTGTTTGGGGGTGTTTTG
AAACCAAACCCATTACAACACT
+307~+527
221

5
APCM
NT_034772
TATTGCGGAGTGCGGGTC
TCGACGAACTCCCGACGA
−163~−66
98

APCU

GTGTTTTATTGTGGAGTGTGG
CCAATCAACAAACTCCCAACAA
−169~−62
108

GTT

6
BCAR3M
NT_028050
GCGTTTCGGGAGGAATAG
ACTACGAAACGCACCGACT
−137~+103
241

BCAR3U

TGGGTGTGTGGTGGAGAT
CTACAAAACACACCAACTAAACACA
−136~+71
208

7
BCL2M
NT_025028
GAAGTCGTCGTCGGTTTG
CCCGCACCGAACATC
+276~+458
183

BCL2U

TTGTTGTTGGTTTGGTGGA
CCCACACCAAACATCTTCTC
+276~+454
179

8
BMP3BM
NT_030772
GCGGTAAAGGGTCGAAGT
AACTCGAACCGCCGATA
+65~+460
196

BMP3BU

TGAGGGTGGTAAAGGGTTG
AAAAACTCAAACCACCAATACC
+267~+460
194

9
BNIP3M
NT_024040
TCGTTCGGTTTCGTTTTG
ACGCTCCGTTCTACGACA
−49~+144
194

BNIP3U

GTTGTAGATTTGTTTGGTTTTG
ACATCCCAAACACTCCATTCT
−58~+153
212

TTT

10
BRCA1M
L78833
GGTTAATTTAGAGTTTCGAGAG
TCAACGAACTCACGCCGCGCAATCG
−320~−138
183

ACG

BRCA1U

GGTTAATTTAGAGTTTTGAGAG
TCAACAAACTCACACCACACAATCA
−320~−138
183

ATG

11
BRCA2M
NT_024524
GCGGAGATTGCGTTATTG
CCGAACCCGTTTCCTTAC
−682~−519
164

BRCA2U

TGGAGGTGGAAGTTGTGG
CTCCAAACCCATTTCCTTACT
−703~−517
187

12
CBR1M
NT_086913
TCGTATTTGGCGAGGT
AAACCCCGCAACGTATTC
−126~+36
163

CBR1U

TTGGTGGGGAGGGGTA
AAACCCCACAACATATTC
−108~+36
145

13
CBR3M
NT_086913
CGTAGATTATTTCGCGGTTTAG
GAACCGAACTTCGAACCAC
−260~−14
247

CBR3U

GGGTGTAGTGTGGGTAGGG
AAACCAAACTTCAAACCACCT
−223~−14
210

14
CCNA1M
AF124143
TCGTCGCGTTTTAGTCGT
ACCCGTTCTCCCAACAAC
−755~−550
206

CCNA1U

GGGTAGTTTTGTTGTGTTTTAG
AACCACTAACAACCCCCTCT
−762~−565
198

TTG

15
CDH1M
L34545
GTGGGCGGGTCGTTAGTTTC
CTCACAAATACTTTACAATTCCGACG
−265 to −93
172

CDH1U

GGTGGGTGGGTTGTTAGTTTTGT
AACTCACAAATCTTTACAATTCCAAC
−266 to −93
172

16
CDH13M
AB001090
TCGCGGGGTTCGTTTTTGC
GACGTTTTCATTCATACACGCG
−267~−24
244

CDH13U

TTGTGGGGTTTGTTTTTTGT
AACTTTTCATTCATACACACA
−267~−24
244

17
CDKN1CM
NT_009237
GGTTCGGTTTTCGCGTAT
AAAACGAACGTCGCGATA
−354~−159
196

CDKN1CU

TTTGTTGTGGTTTGGTTTTTG
AACAAACATCACAATATCACATTACC
−344~−148
197

18
CFTRM
N7_007933
AGAGGTCGCGATTGTCGTT
CGACTTTCTCCACCCACTACG
−316~−114
203

CFTRU

TTAAAGAGAGGTTGTGATTGTT
TCCTTCACTCCCTCACCA
−322~−174
149

GTT

19
COX2M
NT_004487
GTTCGTCGTTGCGATGTT
CCAAACTCTTTCCCAAATCA
+122~+324
203

COX2U

TTGTTTGTTGTTGTGATGTTTG
TCCAAACTCTTTCCCAAATC
+120~+325
206

20
DAPK1M
NT_023935
TCGGTAATTCGTAGCGGTAG
TACTCACCCGAACGCCTA
+57~+234
178

DAPK1U

GGGATTTGGTAATTTGTAGTGG
CCTAACTACTCACCCAAACACCT
+52~+240
189

21
DRG1M
NT_011520
GGTGCGGAGTATGAGTCG
CCGCGAACCAATACGATA
−335~−132
204

DRG1U

GTGAGGAATAGGGGTGTGG
CCCACAAACCAATACAATATCAT
−347~−131
217

22
DRMM
NT_010194
TCGGTTTCGTTGATTTCG
AAACTACCGCGCGTAAAAC
−42~+155
198

DRMU

TTGAGTTTTGGTGGTTTTGG
AAACTACCACACATAAAAC
−22~+155
178

23
ENDRBM
NT_024524
TAGGGCGCGTTCGTATAG
CCACTAACGCGCAAACTT
−119~+103
223

ENDRBU

TGTGTTTGTATAGATTTGGAG
TTCCCACTAACACACAAACTTAAA
−116~+104
221

GTG

24
FADDM
NT_033927
CGTGACGTTCGGGTTG
CCTACGCCCGACGTATC
−169~+19
189

FADDU

TGGATTTGGTAGAGGTGTGATT
TACACCTACACCCAACATATCATC
−96~+24
121

25
GALCM
NT_026437
GGTGACGTCGGAAGAGAAG
CCGCCACGATAAATACGA
+93~+289
197

GALCU

TTATTAGGTGATGTTGGAAGAG
AAAAACAAATCCCATCACCA
+67~+306
220

AAG

26
GSTP1M
NT_033903
GCGATTTCGGGGATTTTA
ACGACGACGAAACTCCAA
−183~+15
199

GSTP1U

GTTGGGGATTTGGGAAAG
TATAAAAATAATCCCACCCCACT
−230~−28
203

27
HNF3BM
NT_011387
CGTTCGTTGTTGTTTTTGC
AACCGTCGACCGCTACTAA
+13~+199
187

HNF3BU

GGGAGAAGTGTGGGGTGT
CCCAACCATCAACCACTACTAA
+13~+139
127

28
HPP1M
AF242221
AAGAGGGGCGTTAGTTCG
CGCTCGCAAACGCTAA
−320~−163
158

HPP1U

ATGTGTGGAAGAGGGGTGT
CACTCACAAACACTAACCCAAA
−328~−163
166

29
HTERTNM
NT_006576
GCGTCGCGAGGAGAG
AATTCGCGAACACAAACG
−205~+4
210

HTERTNU

GGGGTTGTGGAAAGGAAG
AACCACACTTCCCACATAACA
−179~−11
169

30
ICAM1M
NT_011295
TAGCGCGGTGTAGATCGT
CGAACTAACAAAATACCCGAAC
−284~−101
184

ICAM1U

TTGGGAAATGGGAGGTG
TCCAAACTAACAAAATACCCAAAC
−248~−99
150

31
ITGA4M
NT_005403
GACGCGAGTTTTGCGTAG
TAAAATACCGCGCACTCG
+779~+978
200

ITGA4U

GGGAGGTTTGGGTTAGGAT
CAACCTAAAATACCACACACTCAC
+763~+983
221

32
PTCHD2M
NT_021937
TTTCGCGGTCGTTTTAGA
CCGCCCACGTACGTATAA
+1037~+1237
201

PTCHD2U

TGGATAGTGTTTTGTGGTTGTTT
CCACCCACATACATATAAACCAT
+1028~+1237
210

33
LAM3M
NT_010966
TTCGTTCGCGAAGTTTGT
TAAACGACGCCGAAACC
−217~−29
189

LAM3U

TGTGTTTTGTGTGGGAGAGA
AAACAACACCAAAACCACTCC
−197~−30
168

34
LITAFM
NT_010393
CGGTCGGGTTTTTACGTT
ACCTCCCGACTCGACAA
−528~−314
215

LITAFU

GGGAGGTTGGATTTTGTTTT
CAAACCTCCCAACTCAACAA
−528~−293
236

35
MAGEA1M
NT_011726
GTTCGGTCGAAGGAATTTGA
CCACAACCCTCCCTCTTAAA
+7~+328
322

MAGEA1U

GTTTGGTTGAAGGAATTTGA
ACCCACAACCCTCCCTCTTA
+7~+330
324

36
MDR1M
NT_007933
TTGGGGGTTTGGTAGCGC
CTCTCTAAACCCGCGAACGAT
+112864~+112749
115

MDR1U

GTTGGGGGTTTGGTAGTGT
ACTCTCTAAACCCACAAACAAT
+112864~+112748
117

37
MGMTM
NT_008818
AGCGTCGTTGTTTTGTGC
CGCTTTCAAAACCACTCG
−439~−254
186

MGMTU

TTGGTAGTGTTGTTGTTTTGTGT
CATCCTACAACCCCCACA
−457~−249
209

38
MINT2M
AF135502
TGTTGGTGGATTTTGGATTT
AACAACAATTCCATACACCTTTCT
+446~+551
106

MINT2U

AGTTCGTTGGCGGATTTT
CCCGAAATAATAACGACGATT
+442~+562
121

39
MINT1M
AF135501
TTCGAAGCGTTTGTTTGG
CGCCTAACCTAACGCACA
+169~+328
160

MINT1U

TATTTTTGAAGTGTTTGTTTGG
TCCCTCTCCCCTCTAAACTTC
+165~+366
202

TGT

40
MT1AM
K01383
TAAGGTTGGGTTTTCGGAAC
AAATACGAACCACGAAACCA
−421~−258
164

MT1AU

TAAGGTTGGGTTTTTGGAAT
CTCCCCTAAATACAAACCACA
−421~−251
171

41
MTSS1M
NT_008046
TGATTTCGGTCGGGAGT
AAATACAACGCGCTCGAA
+501~+697
197

MTSS1U

GGTGATATTTTGGTTGGGAGT
AAATACAACACACTCAAAAACCTCT
+508~+701
194

42
MYOD1M
AF027148
GACGGTTTTCGACGGTTT
GCCCGAAACCGAATACAC
+210~+393
184

MTOD1U

ATTTGATGGTTTTTGATGGTTT
CACACACATACTCATCCTCACA
+206~+418
213

43
OCLNM
NT—006713
TGCGTTCGTTAGGTGAGC
CGAATCCCAACTCGAAAACG
+537~+762
216

OCLNU

GTTAGGTGTGTTTGTTAGGTG
CACACCTCTCTAATTCCCACA
+531~+771
241

AGT

44
p14^ARFM
L41934
GTCGAGTTCGGTTTTGGAGG
AAAACCACAACGACGAACG
95 TO 255
160

p14^ARFU

TGAGTTTGGTTTTGGAGGTGG
AACCACAACAACAAACACCCCT
97 TO 262
165

45
p61^INK4aM
NM_000077
TTATTAGAGGGTGGGGCGGAT
ACCCCGAACCGCGACCGTAA
−80 to 69
149

CGC

p61^INK4aU

TTATTAGAGGGTGGGGTGGAT
CAACCCCAAACCACAACCATAA
−80 to 71
151

TGT

46
RASSF1AM
XM_040961
GTGTTAACGCGTTGCGTATC
AACCCCGCGAACTAAAAACGA
+82~+176
95

RASSF1AU

TTTGGTTGGAGTGTGTTAATGTG
CAAACCCCACAAACTAAAAACAA
+70~+178
109

47
RPRMM
NT_005403
TGAGCGTTTATTCGTAGATTAGC
GAACGAACGCCGAAAAC
+14~+184
171

RPRMU

GTGGTGGTGTTGGAGGAA
TCAAACAAACACCAAAAACAAAC
+18~+209
192

48
RUNX3M
NT_004610
GAGGGGCGGTCGTACGCGGG
AAAACGACCGACGCGAACGCCTCC
−259~−44
216

RUNX3U

GAGGGGTGGTTGTATGTGGG
AAAACAACCAACACAAACACCTCC
−259~−44
216

49
SALL3M
NT_010879
GTTCGCGTAGTCGTCGTC
TACTCGAAAACCCCGTCA
−123~+79
203

SALL3U

GTGGTTTGTGTAGTTGTTGTT
CCCAACCCTCACCATACTC
−126~+93
220

GTT

50
SERPINB5M
NT_025028
TTTGCGTGGGTCGAGA
GCCTCGACGACACTCC
−219~−29
191

SERPINB5U

TTTTGTGTGGGTTGAGAGG
CACCCCACCCCACCT
−220~−18
203

51
SLC29A1M
NT_007592
AAGGCGTCGGTCGTTAGT
TATAAACCGCCGAACGAA
−178~−18
161

SLC29A1U

TGGGTGTTTAAAGGTGTTGG
ACCAATATAAACCACCAAACAAA
−188~−13
176

52
STAT1M
NT_005403
GTCGTTCGGTGATTGGTG
AACGAAAACGCGACGATA
−28~+166
195

STAT1U

TGTTTAATTGGTTGAGTGTGGA
AAACTAAACAAAAACACAACAATACAA
−50~+172
223

53
TMS1M
NT_010393
TTGTAGCGGGGTGAGCGGC
AACGTCCATAAACAACAACGCG
+197~+387
191

TMS1U

GGTTGTAGTGGGGTGAGTGGT
CAAAACATCCATAAACAACAACACA
+195~+390
196

54
TNFRSF10AM
NT_023666
GTTTTTCGGTCGGGAGTT
ACTCGCCCGATAATAACGA
−321~−160
162

TNFRSF10AU

TGTTTGGTGGATGGATGG
ACTAAATCACTCACCCAATAATAACAA
−321~−220
102

55
TNFRSF10CM
NT_023666
AGCGTTTCGGTCGTTTG
TACCGTATCCCCGTCTCC
+131~+338
208

TNFRSF10CU

TGGTTGAGGTAGGGTGTGAT
TACCATATCCCCATCTCCCTA
+149~+338
190

56
TNFRSF10DM
NT_023666
GAATCGCGACGATGAAGA
CACGCGCACAAACTACG
+38~+250
213

TNFRSF10DU

AGAATTGTGATGATGAAGATG
AACCTTTACACACACACAAACTACA
+38~+257
220

ATG

57
TNFRSF21M
NT_007592
TTGTTTAGCGTCGTATTTATCGT
TCCTCAACCGCTATCGAA
+169~+390
222

TNFRSF21U

TTTTTGGGTTGGGAGTTTATT
TAATTCTCCTCAACCACTATCAAAA
+170~+362
193

58
WWOXM
NT_0140498
GCGATATTGCGGAGATTG
CCCTATCGCCCGCTAC
−58~+99
158

WWOXU

TTGTGGAGATTGGATTTTAGT
CCCTATCACCCACTACCAAAT
−52~+99
152

TTT

(SEQ ID NOS 1-236, respectively, in order of appearance.)

TABLE 3

Methylation states of the tested genes

N.B., 1, the homozygously unmethylated; 2, in grey background: heterozygously methylated; and 3, in dark background: homozygously methylated. The number of tested genes is shown and the number of clinical samples is shown in brackets. The urine sediments derived from patients with cystitis glandularis are used as non-bladder cancer control. The following genes are homozygously methylated in tumor cells, thereby not shown.

TABLE 4

Clinical profile of the bladder cancer patients and controls

Non-cancerous
Neuro-

Bladder
urinogenital
logical
Healthy

cancer
diseases
diseases
control

(n = 132)
(n = 23)
(n = 6)
(n = 7)

Gender
F
25
6
2
4

M
107
17
4
3

Age
19-30
0
2

6

31-40
5
2

1

41-50
22
4
1

51-60
24
7

61-
81
8
5

Range
34-88
16-83
46-78
23-34

Average
63.4
55.7
64.1
25.7

Stage
0a
8

I
68

II
50

III
4

IV
2

Primary

99

cases

Recurrent

33

cases

TABLE 5

DNA methylation profiles in urine sediments from bladder cancer patients and TMN staging

Stage

0a
I
II
III
IV
Total

case(s)^/
case(s)/
case(s)/
case(s)/
case(s)/
case(s)/

Gene
frequency(%)
frequency(%)
frequency(%)
frequency(%)
frequency(%)
frequency(%)

Symbol
(n = 8)
(n = 68)
(n = 50)
(n = 4)
(n = 2)
(n = 132)

SALL3
4/50.0
31/45.6
36/72.0
4/100.0
2/100.0
77/58.3

CFTR
5/62.5
36/52.9
26/52.0
4/100.0
2/100.0
73/55.3

ABCC6
1/12.5
19/27.9
25/50.0
2/50.0
1/50.0
48/36.4

HPP1
2/25.0
22/32.4
21/42.0
0/0.0
1/50.0
46/34.8

BCL2
3/37.5
15/22.1
17/34.0
0/0.0
1/50.0
36/27.3

ALX4
4/50.0
15/22.1
12/24.0
2/50.0
0/0.0
33/25.0

RUNX3
3/37.5
17/25.0
22/44.0
1/25.0
0/0.0
43/32.6

ITGA4
1/12.5
16/23.5
21/42.0
2/50.0
1/50.0
41/31.1

RASSF1A
0/0.0
19/27.9
25/50.0
1/25.0
2/100.0
47/35.6

MYOD1
1/12.5
12/17.6
15/30.0
0/0.0
1/50.0
29/22.0

MT1A
1/12.5
22/32.4
21/42.0
1/25.0
1/50.0
46/34.8

DRM
0/0.0
15/22.1
9/18.0
1/25.0
0/0.0
25/18.9

BMP3B
0/0.0
9/13.2
11/22.0
1/25.0
0/0.0
21/15.9

CCNA1
1/12.5
7/10.3
12/24.0
1/25.0
0/0.0
21/15.9

CDH13
0/0.0
12/17.6
9/18.0
1/25.0
0/0.0
22/16.7

RPRM
1/12.5
9/13.2
7/14.0
2/50.0
0/0.0
19/14.4

MINT1
2/25.0
6/8.8
7/14.0
1/25.0
1/50.0
17/12.9

BRCA1
0/0.0
7/10.3
8/16.0
1/25.0
0/0.0
16/12.1

PTCHD2
0/0.0
4/5.9
2/4.0
1/25.0
0/0.0
7/5.3

TMS1
0/0.0
2/2.9
2/4.0
0/0.0
0/0.0
4/3.0

GSTP1
0/0.0
2/2.9
1/2.0
0/0.0
0/0.0
3/2.3

TABLE 6

Methylation profiles in urine sediments from bladder

cancer patients before and after surgery

Pre-surgery
Post-surgery

case(s)/
case(s)/

Gene
frequency(%)
frequency(%)

Symbol
(n = 132)
(n = 79)
p value

SALL3
77/58.3
6/7.6
1.543E−14

CFTR
73/55.3
6/7.6
3.163E−13

ABCC6
48/36.4
2/2.5
1.110E−09

HPP1
46/34.8
4/5.1
2.293E−07

BCL2
36/27.3
2/2.5
1.457E−06

ALX4
33/25.0
2/2.5
5.595E−06

RUNX3
43/32.6
1/1.3
3.203E−09

ITGA4
41/31.1
5/6.3
1.175E−05

RASSF1A
47/35.6
1/1.3
1.576E−10

MYOD1
29/22.0
0/0.0
4.352E−07

MT1A
46/34.8
3/3.8
2.878E−08

DRM
25/18.9
2/2.5
4.354E−04

BMP3B
21/15.9
1/1.3
3.405E−04

CCNA1
21/15.9
2/2.5
2.344E−03

CDH13
22/16.7
1/1.3
1.940E−04

RPRM
19/14.4
1/1.3
1.098E−03

MINT1
17/12.9
0/0.0
3.368E−04

BRCA1
16/12.1
1/1.3
3.647E−03

PTCHD2
7/5.3
1/1.3
2.630E−01

TMS1
4/3.0
1/1.3
6.526E−01

GSTP1
3/2.3
0/0.0
2.940E−01

TABLE 7

Methylation profiles of tested genes

in the primary and recurrent cases

Primary
Recurrent

case(s)/
case(s)/

Gene
frequency(%)
frequency(%)

Symbol
(n = 99)
(n = 33)
p value

SALL3
57/57.6
20/60.6
8.398E−01

CFTR
50/50.5
23/69.7
6.929E−02

ABCC6
35/35.4
13/39.4
6.814E−01

HPP1
34/34.3
12/36.4
8.358E−01

BCL2
23/23.2
13/39.4
1.126E−01

ALX4
23/23.2
10/30.3
4.873E−01

RUNX3
29/29.3
14/42.4
1.992E−01

ITGA4
31/31.3
10/30.3
1.000E+00

RASSF1A
34/34.3
13/39.4
6.759E−01

MYOD1
22/22.2
7/21.2
1.000E+00

MT1A
34/34.3
12/36.4
8.358E−01

DRM
21/21.2
4/12.1
3.117E−01

BMP3B
17/17.2
4/12.1
5.918E−01

CCNA1
18/18.2
3/9.1
2.791E−01

CDH13
17/17.2
5/15.2
1.000E+00

RPRM
14/14.1
5/15.2
1.000E+00

MINT1
11/11.1
6/18.2
3.675E−01

BRCA1
13/13.1
3/9.1
7.599E−01

PTCHD2
6/6.1
1/3.0
6.796E−01

TMS1
4/4.0
0/0.0
5.716E−01

GSTP1
2/2.0
1/3.0
1.000E+00

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METHOD FOR DIAGNOSING BLADDER CANCER BY ANALYZING DNA METHYLATION PROFILES IN URINE SEDIMENTS AND ITS KIT

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