SALIVARY BIOMARKERS FOR CANCERS, METHODS AND DEVICES FOR ASSAYING THE SAME, AND METHODS FOR DETERMINING SALIVARY BIOMARKERS FOR CANCERS

TECHNICAL FIELD

The present invention relates to salivary biomarkers for cancers, methods and devices for assaying the same, and methods for determining the salivary biomarkers for cancer. In particular, the present invention relates to salivary biomarkers to differentiate pancreatic cancer, intraductal papillary mucinous neoplasm (IPMN), breast cancer, and oral cancers from healthy controls, and methods and devices for assaying these biomarkers, and methods for determining these salivary biomarkers.

BACKGROUND ART

A treatment of pancreatic cancer patients, one of the most maglicant cancers showing a poor prognosis, is still difficult. The median survival year is less than one year for pancreatic cancer patients who do not undego adjuvant therapies, such as chemotherapy and radiotherapy. Thus, detection of pancreatic cancer at the early stages is the only way available to prove the prognosis, indicating the needs of development of novel methods to detect the cancer using a biological sample (body fluid, etc.) minimally or non-invasively.

One of the present inventors previously proposed serum biomarkers to detect liver diseases in Patent Literatures 2 and 3.

Large molecule biomarkers for early detection of pancreatic cancers using blood, serum and plasma samples have been intensively developed (Patent Literatures 4 and 5). For example, carbohydrate antigen 19-9 (CA19-9) is already commonly used as a tumor marker to detect pancreatic cancers and biliary tract cancers as well as to evaluate the effects of chemotherapy. However, early detection of pancreatic cancer using this marker is difficult, and the accuracy of screening cancer is insufficient (Non-Patent Literature 1). In addition, CA19-9 levels do not increase in Lewis negative patients even in the advanced stage. Detection of a pancreatic cancer associated antigen (DUPAN-2 antigen) and a carcinoembryonic antigen (CEA) are also used. However, DUPAN-2 shows low specificity because this marker increases not only for pancreatic cancer but also for biliary tract and liver cancers. CEA also shows low specificity and shows positive for cancers of the digestive system, e.g. esophageal cancer and gastric cancer. Therefore, these markers are not specific to pancreatic cancer. Further, these two markers have not been widely used due to costs.

Polyamines, such as spermine (spermine), and acetylated polyamines, such as N8-acetylspermidine (N8-Acetylspermidine), N1-acetylspermidine (N1-Acetylspermidine), and N1-acetylspermine (N1-Acetylspermine) were known as metabolite biomarkers for various cancers in blood and urine (Non-Patent Literature 2). In a metabolic pathway, arginine is metabolized to ornithine, and then metabolized through putrescine to polyamines. The synthesis of polyamines is usually relatively activated in close to the surface of tumor tissues where oxygen is available compared to the center of the tumor tissue, while synthesis is less activated under a hypoxic condition in the center of the tumor. Despite their hetelogenious conditions in the tumor tissues, overall, the concentration of the polyamines in total tumor tissue increases and a part of these metabolites is transferred to the blood vessel. For example, an increase in the concentration of spermidine in blood is known in patients with breast cancers, prostate cancers and testis tumors (Non-Patent Literature 1). Decreasing the concentrations of spermine and spermidine in blood is reported in patients with acute pancreatitis by experiments on animals (Non-Patent Literature 3).

CITATION LIST
Patent Literature

Patent Literature 1: Japanese Patent Application Laid-Open No. 2011-58863

Patent Literature 2: Japanese Patent Application Laid-Open No. 2011-232164

Patent Literature 3: WO2011/158590A1

Patent Literature 4: Japanese Patent Application Laid-Open No. 2011-247869

Patent Literature 5: Japanese Translation of PCT International Application No. 2009-508493

Patent Literature 6: Japanese Patent Application Laid-Open No. 2013-521763

Non-Patent Literature

Non-Patent Literature 1: Hamada S, Shimosegawa T., Biomarkers of pancreatic cancer, Pancreatology. 2011; 11: 14-9

Non-Patent Literature 2: Soda K (2011), The mechanisms by which polyamines accelerate tumor spread. Journal of Experimental & Clinical Cancer Research. 30(1): 95

Non-Patent Literature 3: Jin H T, Lamsa T, Merentie M, Hyvonen M T, Sand J, Raty S, Herzig K H, Alhonen L, Nordback I (2008). Polyamine levels in the pancreas and the blood change according to the severity of pancreatitis. Pancreatology. 8(1), 15-24

Non-Patent Literature 4: Zhang L, Farrell J J, Zhou H, Elashoff D, Akin D, Park N H, Chia D, Wong D T. (2010), Salivary transcriptomic biomarkers for detection of resectable pancreatic cancer. Gastroenterology. 138(3): 949-57

Non-Patent Literature 5: Sugimoto M, Wong D T, Hirayama A, Soga T, Tomita M, (2010), Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles, Metabolomics, 6, 78-95

Non-Patent Literature 6: Soga, T., Baran, R., Suematsu M., Ueno, Y., Ikeda, S., Sakurakawa T., Kakazu, Y., Ishikawa, T., Robert, M., Nishioka, T., Tomita, M. (2006), Differential methabolomics reveals ophthalmic acid as an oxidative stress biomarker indicating hepatic glutathione sonsumption., Journal of Biological Chemistry, 281 (24): 16768-16776

Non-Patent Literature 7: Sugimoto et al. Capillary electrophoresis mass spectrometry-based saliva metabolomics identified oral, breast and pancreatic cancer-specific profiles, Metabolomics, 2010, 6, 78-95

Non-Patent Literature 8: Tsutsui et al, High-throughput LC-MS/MS based simultaneous determination of polyamines including N-acetylated forms in human saliva and the diagnostic approach to breast cancer patients, Anal Chem, 2013, 85, 11835-42

Non-Patent Literature 9: Wang Q et al. Investigation and identification of potential biomarkers in human saliva for the early diagnosis of oral squamous cell carcinoma., Clin Chim Acta. 2014; 427: 79-85

SUMMARY OF INVENTION
Technical Problem

Conventional screening for protein markers in blood, serum, or plasma is insufficient for early detection of pancreatic cancer. Although blood-based tests are minimally invasive, professionals, such as medical doctors and nurses are required to handle the syringe. Thus, frequency of the test is limited. In contrast, the use of saliva provides definite advantages, i.e. completely non-invasive collection anywhere, which make it possible for frequent and self-sampling tests. For example, salivary biomarkers for lung cancer detection was proposed in Patent Literature 6. As mentioned, because early detection of pancreatic cancer using currently known biomarkers is difficult, frequent salivary testing is the only method to increase the possibility of detecting this cancer in earlier stages.

Detection of pancreatic cancer using mRNA profiles in saliva was proposed in Non-Patent Literature 4.

However, quantification of mRNA requires complex sample processing and addition of RNase inhibitor to saliva just after saliva collection to prevent mRNA degradation. Because of the low reproducibility of microarray-based quantification of mRNA, quantitative PCR (qPCR) is usually used for validating a marker's quantified values. However, each qPCR can profile only one marker, which limits simultaneous quantification of multiple markers. For example, only 35 substances are quantitatively determined in Non-Patent Literature 4. Thus, the use of qPCR limits simultaneous quantification of multiple markers, which cannot capture the holistic view of salivary molecular characteristics, e.g. the overall variation of salivary concentration cannot be determined. Therefore, highly accurate prediction using only a few markers becomes difficult. In the case of qPCR-based quantification, complexity of the sample processing for qPCR may increase artificial noise levels. Thefore, simple methods for quantifying salivary moleculers to minimize possible artificial noise are preferable. Taken together, not only exploring novel biomarkers but also the development of combination techniques for accurately detecting subjects with various cancers, such as pancreatic, breast and oral cancers, and begin diseases including intraductal papillary mucinous neoplasm (IPMN), are required.

The present invention addresses these problems. An object of the present invention is the early detection of cancer such as pancreatic cancer, breast cancer, and oral cancer using saliva.

Solution to the Problems

The present inventors identified multiple metabolite biomarkers in saliva to discriminate patients with pancreatic cancers from healthy controls. Capillary electrophoresis-mass spectrometry (CE-MS) may be used to simultaneously quantify these metabolite markers. The inventors also developed combinations of these biomarkers to realize accurate discrimination. Although saliva samples should be collected carefully to eliminate diurnal variation, there are difficulties to completely eliminate these variations. Therefore, the inventors also found normalization metabolites for estimating the total concentration of the metabolites in saliva, and developed algorithms to combine metabolite markers and normalization metabolites for more accurate detection of subjects with pancreatic cancers.

Further, the inventors have found markers for breast cancer and oral cancer following similar procedures.

The present invention is based on the aforementioned research results. Salivary biomarkers and their combinations have the potential to solve the aforementioned problems.

Herein, salivary metabolite biomarkers and their combinations were developed to detect certain diseases, including pancreatic cancer, intraductal papillary mucinous neoplasm (IPMN), breast cancer, and oral cancer.

Absolute concentration and the combination of the following salivary metabolite biomarkers can be used for detecting patients with pancreatic disease: N-acetylputrescine (N-Acetylputrescine), adenosine (Adenosine), 3-phospho-D-glyceric acid (3PG), urea (Urea), o-acetylcarnitine (o-Acetylcarnitine), citric acid (Citrate), glycyl-glycine (Gly-Gly), 5-aminovaleric acid (5-Aminovalerate), 4-methyl 2-oxopentanoate (2-Oxoisopentanoate), malic acid (Malate), benzoate ester (Benzoate), fumaric acid (Fumarate), N-acetylaspartic acid (N-Acetylaspartate), inosine (Inosine), 3-methylhistidine (3-Methylhistidine), N1-acetylspermine (N1-Acetylspermine), creatine (Creatine), α-aminoadipic acid (alpha-Aminoadipate), phosphorylcholine (Phosphorylcholine), 2-hydroxypentanoate (2-Hydroxypentanoate), xanthine (Xanthine), succinic acid (Succinate), 6-phosphogluconic acid (6-Phosphogluconate), butanoic acid (Butanoate), homovanillic acid (Homovanillate), O-phosphoserine (O-Phosphoserine), trimethylamine-N-oxide (Trimethylamine N-oxide), piperidine (Piperidine), cystine (Cys), 2-isopropylmalic acid (2-Isopropylmalate), N8-acetylspermidine (N8-Acetylspermidine), N1-acetylspermidine (N1-Acetylspermidine), N-acetylneuraminic acid (N-Acetylneuraminate), glucosamine (Glucosamine), spermine (Spermine), agmatine (Agmatine), N-acetylhistamine (N-Acetylhistamine), methionine (Met), p-4-hydroxyphenylacetic acid (p-4-Hydroxyphenylacetate), N,N-dimethylglycine (N,N-Dimethylglycine), hypotaurine (Hypotaurine), glutamyl-glutamic acid (Glu-Glu), and N1,N12-diacetylspermine (N1,N12-Diacetylspermine).

Relative concentration, i.e. the absolute concentration divided by the concentration of the normalization metabolite, of the following salivary metabolite biomarkers can be used for detecting patients with pancreatic cancer or IPMN: N8-acetylspermidine (N8-Acetylspermidine), creatinine (Creatinine), spermine (Spermine), aspartic acid (Asp), N1-acetylspermidine (N1-Acetylspermidine), N1-acetylspermine (N1-Acetylspermine), cytidine (Cytidine), α-aminoadipic acid (alpha-Aminoadipate), cytosine (Cytosine), betaine (Betaine), urea (Urea), homovanillic acid (Homovanillate), N-acetylneuraminic acid (N-Acetylneuraminate), cystine (Cys), urocanic acid (Urocanate), fumaric acid (Fumarate), 1,3-diaminopropane (1,3-Diaminopropane), hypotaurine (Hypotaurine), nicotinic acid (Nicotinate), agmatine (Agmatine), valine (Val), 2-hydroxy-4-methylpentanoic acid (2-Hydroxy-4-methylpentanoate), alanyl-alanine (Ala-Ala), citric acid (Citrate), glucosamine (Glucosamine), carnosine (Carnosine), glycyl-glycine (Gly-Gly), 2-aminobutyric acid (2AB), arginine (Arg), N-acylglutamic acid (N-Acetylglutamate), glycerophosphoric acid (Glycerophosphate), phosphoenolpyruvic acid (PEP), isoleucine (Ile), adenosine (Adenosine), guanine (Guanine), dihydroxyacetonephosphoric acid (DHAP), and cadaverine (Cadaverine).

As an example combination, the absolute concentration of creatinine, N1-acetylspermidine, α-aminoadipic acid, N-acetylneuraminic acid, and 1,3-diaminopropane in saliva can be used for accurate pancreatic cancer detection. The prediction can be made by using another combination or changing the methodology of combination.

As the salivary biomarker for cancer used to detect breast cancer, the absolute concentration of the following substances or a combination thereof in saliva can be used: choline (Choline), 2-hydroxybutyric acid (2-Hydroxybutyrate), β-alanine (beta-Ala), 3-methylhisdine (3-Methylhistidine), α-aminobutyric acid (2AB), N-acetyl-β-alanine (N-Acetyl-beta-alanine), isethionic acid (Isethionate), N-acetylphenylalanine (N-Acetylphenylalanine), trimethyllysine (N6,N6,N6-Trimethyllysine), α-aminoadipic acid (alpha-Aminoadipate), creatine (Creatine), γ-butyrobetaine (gamma-Butyrobetaine), sarcosine (Sarcosine), pyruvic acid (Pyruvate), urocanic acid (Urocanate), piperidine (Piperidine), serine (Ser), homovanillic acid (Homovanillate), 5-oxoproline (5-Oxoproline), GABA (GABA), 5-aminovaleric acid (5-Aminovalerate), trimethylamine-N-oxide (Trimethylamine N-oxide), 2-hydroxyvaleric acid (2-Hydroxyl)pentanoate), carnitine (Carnitine), isopropanolamine (Isopropanolamine), hypotaurine (Hypotaurine), lactic acid (Lactate), 2-hydroxy-4-methylpentanoic acid (2-Hydroxy-4-methylpentanoate), hydroxyproline (Hydroxyproline), butyric acid (Butanoate), adenine (Adenine), N6-acetyllysine (N-epsilon-Acetyllysine), 6-hydroxyhexanoic acid (6-Hydroxyhexanoate), propionic acid (Propionate), betaine (Betaine), N-acetylputrescine (N-Acetylputrescine), hypoxanthine (hypoxanthine), crotonic acid (Crotonate), tryptophan (Trp), citrulline (Citrulline), glutamine (Gln), proline (Pro), 2-oxoisopentanoic acid (2-Oxoisopentanoate), 4-methylbenzoate (4-Methylbenzoate), 3-(4-hydroxyphenyl)propionic acid (3-(4-Hydroxyphenyl)propionate), cysteic acid (Cysteate), azelaic acid (Azelate), ribulose-5-phosphoric acid (Ru5P), pipecolinic acid (Pipecolate), phenylalanine (Phe), O-phosphoserine (O-Phosphoserine), malonic acid (Malonate), hexanoic acid (Hexanoate), and p-hydroxyphenylacetic acid (p-Hydroxyphenylacetate).

The aforementioned substances that are significant substances and that are not publicly known are indicated in Table 7 below.

A combination of β-alanine, N-acetylphenylalanine, and citrulline can be used as one example of a combination of salivary biomarkers for cancer to detect breast cancer. The prediction can be performed by using a different combination or changing the methodology of combination.

When a value is used in which the concentration of saliva was corrected, the following substances or a combination thereof may be used as a marker: choline (Choline), β-alanine (beta-Ala), 3-methylhisdine (3-Methylhistidine), α-aminobutyric acid (2AB), N-acetyl-β-alanine (N-Acetyl-beta-alanine), isethionic acid (Isethionate), N-acetylphenylalanine (N-Acetylphenylalanine), trimethyllysine (N6,N6,N6-Trimethyllysine), urocanic acid (Urocanate), piperidine (Piperidine), 5-aminovaleric acid (5-Aminovalerate), trimethylamine-N-oxide (Trimethylamine N-oxide), isopropanolamine (Isopropanolamine), hypotaurine (Hypotaurine), hydroxyproline (Hydroxyproline), N6-acetyllysine (N-epsilon-Acetyllysine), 6-hydroxyhexanoic acid (6-Hydroxyhexanoate), N-acetylputrescine (N-Acetylputrescine), azelaic acid (Azelate), dihydroxyacetonephosphoric acid (DHAP), glycolic acid (Glycolate), 4-methyl-2-oxopentanoic acid (4-Methyl-2-oxopentanoate), N-acetylaspartic acid (N-Acetylaspartate), glycerophosphoric acid (Glycerophosphate), 3-hydroxybutyric acid (3-Hydroxybutyrate), benzoic acid (Benzoate), adipic acid(Adipate), 2-isopropylmalate (2-Isopropylmalate), phosphorylchlorine (Phosphorylcholine), N-acetylneuraminic acid (N-Acetylneuraminate), histamine (His), o-acetylcarnitine (o-Acetylcarnitine), N-acetylglucosamine 1-phosphate (N-Acetylglucosamine 1-phosphate), creatinine (Creatinine), arginine (Arg), and syringic acid (Syringate).

The aforementioned substances that are significant substances and that are not publicly known are indicated in Table 8 below.

A combination of N-acetylphenylalanine, N-acetylspermidine, and creatine can be used as one example of a combination of salivary biomarkers for cancer used to detect breast cancer. The prediction can be performed by using a different combination or changing the methodology of combination.

As the salivary biomarker for cancer used to detect oral cancer, the concentration of the following substances or a combination thereof in saliva can be used: Glycyl-glycine (Gly-Gly), citrulline (Citrulline), γ-butyrobetaine (gamma-Butyrobetaine), 3-phenyllactate (3-Phenyllactate), butyric acid (Butanoate), hexanoic acid (Hexanoate), methionine (Met), hypoxanthine (Hypoxanthine), spermidine (Spermidine), tryptophan (Trp), aspartic acid (Asp), isopropanolamine (Isopropanolamine), alanyl-alanine (Ala-Ala), N,N-dimethylglycine (N,N-Dimethylglycine), N1-acetylspermidine (N1-Acetylspermidine), N1-,N8-diacetylspermidine (N1,N8-Diacetylspermidine), N8-acetylspermidine (N8-Acetylspermidine), α-aminobutyric acid (2AB), trimethylamine-N-oxide (Trimethylamine N-oxide), N-acetylaspartic acid (N-Acetylaspartate), adenine (Adenine), 2-hydroxyvaleric acid (2-Hydroxyl)pentanoate), putrescine (Putrescine (1,4-Butanediamine)), 3-phosphoglycerate (3PG), 3-phenylpropionic acid (3-Phenylpropionate), serine (Ser), 1-methylnicotinamide (1-Methylnicotineamide), 3-hydroxy-3-methylglutaric acid (3-Hydroxy-3-methylglutarate), guanine (guanine), 3-(4-hydroxyphenyl)propionic acid (3-(4-Hydroxyphenyl)propionate), 4-methylbenzoate (4-Methylbenzoate), ribulose-5-phosphoric acid (Ru5P), α-aminoadipic acid (alpha-Aminoadipate), N6-acetyllysine (N-epsilon-Acetyllysine), glucosamine (Glucosamine), cystine (Cys), carnosine (Carnosine), urocanic acid (Urocanate), phenylalanine (Phe), 2-deoxyribose-1-phosphoric acid (2-Deoxyribose 1-phosphate), cytidine disodium 5′-monophosphate (CMP), p-hydroxyphenylacetic acid (p-Hydroxyphenylacetate), 3-hydroxybutyric acid (3-Hydroxybutyrate), N-acetylputrescine (N-Acetylputrescine), 7-methylguanine (7-Methylguanine), inosine (Inosine), lysine (Lys), dihydroxyacetonephosphoric acid (DHAP), 3-methylhisdine (3-Methylhistidine), carbamoylaspartic acid (Carbamoylaspartate), creatinine (Creatinine), N-methyl-2-pyrrolidone (1-Methyl-2-pyrrolidinone), pyruvic acid (Pyruvate), propionic acid (Propionate), 5-aminovaleric acid (5-Aminovalerate), N-acetylornithine (o-Acetylornithine), 5-oxoproline (5-Oxoproline), creatine (Creatine), homoserine (Homoserine), fumaric acid (Fumarate), glycine (Gly), and N1,N12-diacetylspermine (N1,N12-Diacetylspermine).

The aforementioned substances that are not publicly known are indicated in Table 9 below.

The present invention provides a method for assaying a salivary biomarker for cancer including the steps of: collecting a saliva sample; and detecting the aforementioned salivary biomarker for cancer in the collected saliva sample.

The present invention provides a device for assaying a salivary biomarker for cancer including means for collecting a saliva sample, and means for detecting the aforementioned salivary biomarker for cancer in the collected saliva sample.

The present invention further provides a method for determining a salivary biomarker for cancer including a procedure of performing ultrafiltration of a saliva sample, means for cyclopedically measuring ionic metabolites in the saliva sample after the ultrafiltration, and a procedure of selecting a substance having high ability of distinguishing a patient with a pancreatic disease from a healthy subject according to concentrations of the measured metabolites.

Correlation of absolute concentration among multiple metabolites can be used for identifying a normalizing metabolite that can eliminate variation of overall concentrations in saliva.

A combination of the salivary biomarkers for cancer can be determined using a mathematical model.

Advantageous Effects of Invention

According to the present invention, not only pancreatic cancer but also a pancreatic disease including IPMN and chronic pancreatitis, breast cancer, and oral cancer can be detected early using saliva that can be collected non-invasively and simply. In particular, a combination of polyamine with novel metabolite biomarkers makes a highly accurate prediction possible.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart illustrating the procedure for determining the biomarkers used in the Examples of the present invention.

FIG. 2 is a diagram illustrating a correlation network between metabolites in saliva used in the Examples.

FIG. 3 is a flowchart illustrating a procedure of developing a mathematical model used in the Examples.

FIG. 4 is a diagram illustrating a model of a decision tree that distinguishes a subject with pancreatic cancer from a healthy subject.

FIG. 5 is a diagram illustrating a receiver operating characteristic (ROC) curve of a mathematical model that distinguishes a subject with pancreatic cancer from a healthy subject using a metabolite concentration normalized with a concentration marker used in the Examples.

FIG. 6 is a diagram in which a risk of pancreatic cancer (PC) for a healthy subject (C), and subjects with pancreatic cancer (PC), chronic pancreatitis (CP), and IPMN is plotted in a model of classifying the healthy subject and the subject with pancreatic cancer in the Examples.

FIG. 7 is a diagram illustrating a stepwise forward selection method used for variable selection in an MLR model that distinguishes a subject with pancreatic cancer from a healthy subject when the absolute concentration of the concentration marker as used in the Examples.

FIG. 8 is a diagram illustrating a forward selection method used for variable selection in the MLR model that distinguishes a subject with pancreatic cancer from a healthy subject when the absolute concentration of the concentration marker as used in the Examples.

FIG. 9 is a diagram illustrating an example of a total concentration of amino acids in saliva used in the Examples.

FIG. 10 is a diagram illustrating an ROC curve in which variables in an MLR model that distinguishes a patient with breast cancer from a healthy subject are β-alanine, N-acetylphenylalanine, and citrulline.

FIG. 11 is a diagram illustrating a ROC curve in which the variables in the same MLR model are N-acetylphenylalanine, N1-acetylspermidine, and creatine.

FIG. 12 is a diagram of a network between metabolites for determination of a concentration-correcting substance for a biomarker for breast cancer.

FIG. 13 includes diagrams illustrating substances belonging to polyamines among substances that give a significant difference between the healthy subject and the patient with breast cancer.

FIG. 14 includes diagrams illustrating examples of substances other than polyamines among the substances that give a significant difference between the healthy subject and the patient with breast cancer.

FIG. 15 includes diagrams illustrating the top five substances that give a significant difference between the healthy subject and the patient with breast cancer and has a smaller p value regardless of the presence or absence of concentration correction, and an ROC curve thereof.

FIG. 16 is a correlation network diagram illustrating a reason for determining Gly to be a correction marker.

FIG. 17 includes diagrams illustrating the concentrations of metabolites in a cancer tissue sample obtained during surgery of oral cancer and a healthy tissue sample near the cancer tissue sample.

FIG. 18 includes diagrams illustrating a difference in the concentration of saliva of a patient with oral cancer from that of the healthy subject when a method of collecting saliva in the patient is changed.

DESCRIPTION OF EMBODIMENTS

Hereinafter, an embodiment suitably implementing the present invention (hereinafter referred to as the embodiment) will be described in detail. The present invention is not limited to the following embodiments and Examples. In addition, constituents in the following embodiments and Examples include those that can be easily assumed by those skilled in the art, those that are substantially equivalent, and those falling within the scope of the so-called doctrine of equivalents. Further, the constituents disclosed in the following embodiments and Examples may be used in appropriate combination or by appropriate selection.

A procedure of determining a biomarker for a pancreatic disease will be described with reference to FIG. 1.

1. Saliva Donor

A total of 199 salivary samples were collected from patients with pancreatic cancer with various stages, healthy subjects, and patients with intraductal papillary mucinous neoplasm (IPMN) and chronic pancreatitis. Table 1 lists subject characteristics, such as sex and age. Of these, no patients had undergone chemotherapy.

TABLE 1

THE
SEX
AGE

NUMBER

THE NUMBER

THE NUMBER

DISEASE
STAGE
OF CASES
male
female
OF DEFECTS
MINIMUM
MEDIAN
MAXIMUM
OF DEFECTS

HEALTHY
—
63
50
13

19
43
73

CHRONIC
—
14
12
2

32
49
79

PANCREATITIS

TPMN
—
7
6
1

63
64
69
2

PANCREATIC
I
3
1
2

61
68
76

CANCER
II
3
1
1
1
67
67.5
68
1

III
18
10
8

51
69.5
95

Iva
37
14
16
7
45
69
85
7

Ivb
54
30
22
2
43
72
86
3

SUBTOTAL OF
115
56
49
10
43
70
95
11

PANCREATIC

CANCER

2. Method for Collecting Saliva (Step 100 in FIG. 1)

With respect to collection date

Collection is performed on a day other than a surgery day as much as possible.

With respect to diet

After 21:00 of the day before the collection, do not drink anything but water.

On the day of collection, do not eat breakfast.

Notes before collection of saliva on the day

Collect saliva from AM 8:30 to 11:00 before breakfast.

Brush teeth without use of toothpaste 1 hour or more before the collection of saliva.

Do not strenuously exercise 1 hour before the collection of saliva.

Do not clean the inside of oral cavity (with a toothpick, etc.).

Do not smoke.

Do not drink anything but water.

Method for collecting saliva

The mouth is rinsed with water before the collection of saliva, and non-irritant mixed saliva is collected.

Only saliva that runs spontaneously but is not volitionally generated is collected (sialemesis method). Alternatively, a straw is placed in the mouth when saliva is retained to some extent in the mouth (the time is about 3 minutes), and the saliva runs into a tube (passive drool method). When the face is turned down and saliva in the mouth is pushed into the straw that is vertically set, the saliva is likely to run spontaneously. However, when the saliva adheres to a middle of the straw and does not fall down, the saliva is sent out by the breath (in this case, saliva is easily collected by retaining saliva in the mouth to some extent and then pushing the saliva into the tube at one time as compared with opening of the mouth to the tube).

200 μL or more of saliva (as much as possible) is collected.

During the collection of saliva, the tube is placed on ice and kept at a low temperature as much as possible, and the collection is finished within 15 minutes (even when 200 μL of saliva is not collected, the collection is finished in 15 minutes).

Within 5 minutes, the saliva is cryopreserved on ice at −80° C. or with dry ice for storage. The tube and the straw of collecting saliva is a tube and a straw made of a polypropylene material.

A method for collecting saliva is not limited to the aforementioned method, and another method may be used.

3. Pretreatment Method for Measurement of Metabolites in Saliva (Step 110 in FIG. 1)

400 μL of saliva sample is taken, placed in an ultrafiltration filter (molecular weight cutoff: 5,000 Da), and centrifuged at 4° C. and 9,100 g for 3.5 hours. 45 μL of the filtrate and 5 μL of an aqueous solution in which the concentration of each of methionine sulfone (Methionine sulfone), 2-morpholinoethanesulfonic acid (2-Morpholinoethanesulfonic acid), CSA (D-Camphor-10-sulfonic acid), 3-aminopyrrolidine (3-Aminopyrrolidine), and trimesic acid (Trimesate) is 2 mM are mixed to prepare 50 μL of a sample. Measurement was performed by the following method.

4. Measurement of Absolute Concentrations of Metabolites in Saliva by Capillary Electrophoresis-Time-of-Flight Mass Spectrometry (CE-TOFMS) (Step 120 in FIG. 1)

Ionic metabolites were identified and quantitatively determined from saliva by metabolome analysis using CE-MS.

A measurement method was performed in accordance with the method described in Non-Patent Literature 6. Hereinafter, the parameters will be described.

1) Cationic Metabolite Measurement Mode

HPCE

Capillary: fused silica, 50 μm in inner diameter×100 cm in length

Buffer: 1 M formic acid (formate)

Voltage: positive, 30 kV

Temperature: 20° C.

Injection: injection under a pressure of 50 mbar for 5 seconds (about 3 nL)

Washing before measurement: with 30 mM ammonium formate (Ammonium Formate) at a pH of 9.0 for 5 minutes, ultrapure water for 5 minutes, and buffer for 5 minutes

TOFMS

Polarity: positive

Capillary voltage: 4,000 V

Fragmentor voltage: 75 V

Skimmer voltage: 50 V

OCT RFV: 125 V

Drying gas: nitrogen (N₂), 10 L/min

Drying gas temperature: 300° C.

Nebulizer gas pressure: 7 psig

Sheath liquid: 50% methanol/0.1 μM Hexakis (2,2-difluoroethoxy) phosphazene-containing water

Flow rate: 10 mL/min

Reference m/z: 2 methanol ¹³C isotope [M+H]+m/z 66.063061,

Hexakis(2,2-difluoroethoxy)phosphazene [M+H]+m/z 622.028963

2) Anionic Metabolite Measurement Mode

HPCE

Capillary: COSMO (+), 50 μm in inner diameter×10.6 cm in length

Buffer: 50 mM ammonium acetate, pH: 8.5

Voltage: negative, 30 kV

Temperature: 20° C.

Injection: injection under a pressure of 50 mbar for 30 seconds (about 30 nL)

Washing before measurement: with 50 mM ammonium acetate at a pH of 3.4 for 2 minutes, and 50 mM ammonium acetate at a pH of 8.5 for 5 minutes

TOFMS

Polarity: negative

Capillary voltage: 3,500 V

Fragmentor voltage: 100 V

Skimmer voltage: 50 V

OCT RFV: 200 V

Drying gas: nitrogen (N₂), 10 L/min

Drying gas temperature: 300° C.

Nebulizer gas pressure: 7 psig

Sheath liquid: 5 mM ammonium acetate and 50% methanol/0.1 μM Hexakis (2,2-difluoroethoxy) phosphazene-containing water

Flow rate: 10 mL/min

Reference m/z: 2 acetic acid ¹³C isotope [M−H]−m/z 120.038339, Hexakis(2,2-difluoroethoxy) phosphazene+acetic acid [M−H]− 680.035541

ESI needle: platinum

The anionic metabolite measurement may be performed before the cationic metabolite measurement.

5. Removal of Noise (Step 130 in FIG. 1)

Signals of a substance in which a value largely varies depending on a measurement day and a substance not derived from a metabolite are removed.

From measurement data, all peaks in which a signal noise ratio was 1.5 or more were first detected. A commercially available standard substance was measured before measurement of the saliva samples. A peak in which a value of mass to charge ratio (m/z) obtained by a mass spectrometer and a corresponding migration time were assigned to a substance name. Thus, identification was performed. In quantitative determination, the peak area of each peak was divided by the area of the peak of the internal standard substance, a fluctation of measurement sensitivity of the mass spectrometer was corrected, and the specific peak area ratio was calculated. The absolute concentration was calculated from a ratio of the specific peak area in the saliva samples to the specific peak area of the standard substance.

6. Selection of Substance Detected Highly Frequently in Each Group (Step 140 in FIG. 1)

Only a substance in which the peak can be detected in 30% or more cases (for example, three out of ten) of each group was selected.

7. Selection of a Substance Having a Statistically Significant Difference Between Groups (Step 150 in FIG. 1)

After a typical test (in this case, Mann-Whitney test) was performed, a P value was corrected using a false discovery rate (FDR) and a Q value was calculated. A substance having a significant difference of Q<0.05 was selected.

The substance selected by this procedure is a substance selected from N-acetylputrescine (N-Acetylputrescine), adenosine (Adenosine), 3-phospho-D-glyceric acid (3PG), urea (Urea), o-acetylcarnitine (o-Acetylcarnitine), citric acid (Citrate), glycyl-glycine (Gly-Gly), 5-aminovaleric acid (5-Aminovalerate), methyl 2-oxopentanoate (2-Oxoisopentanoate), malic acid (Malate), benzoate ester (Benzoate), fumaric acid (Fumarate), N-acetylaspartic acid (N-Acetylaspartate), inosine (Inosine), 3-methylhistidine (3-Methylhistidine), N1-acetylspermine (N1-Acetylspermine), creatine (Creatine), α-aminoadipic acid (alpha-Aminoadipate), phosphorylcholine (Phosphorylcholine), 2-hydroxypentanoate (2-Hydroxypentanoate), xanthine (Xanthine), succinic acid (Succinate), 6-phosphogluconic acid (6-Phosphogluconate), butanoic acid (Butanoate), homovanillic acid (Homovanillate), O-phosphoserine (O-Phosphoserine), trimethylamine-N-oxide (Trimethylamine N-oxide), piperidine (Piperidine), cystine (Cys), 2-isopropylmalic acid (2-Isopropylmalate), N8-acetylspermidine (N8-Acetylspermidine), N1-acetylspermidine (N1-Acetylspermidine), N-acetylneuraminic acid (N-Acetylneuraminate), glucosamine (Glucosamine), spermine (Spermine), agmatine (Agmatine), N-acetylhistamine (N-Acetylhistamine), methionine (Met), p-4-hydroxyphenylacetic acid (p-4-Hydroxyphenylacetate), N,N-dimethylglycine (N,N-Dimethylglycine), hypotaurine (Hypotaurine), glutamyl-glutamic acid (Glu-Glu), N1,N12-diacetylspermine (N1,N12-Diacetylspermine), and combinations thereof.

8. Selection of Substance for Presuming Concentrations of all Metabolites in Saliva and Performing Concentration Correction (Step 142 in FIG. 1)

In all the samples measured (including healthy, breast cancer, oral cancer, IPMN, and pancreatic cancer), correlation values between the metabolites were exhaustively calculated using the determined quantitative values of the metabolites. Combinations of substances satisfying a Pearson correlation coefficient (R) of R≧0.8 were listed. Of a metabolite group in which the most substances correlated with each other, a substance that correlated with the most substances was selected.

FIG. 2 shows one example of a correlation network diagram of the metabolites in saliva.

9. Selection of a Substance Having a Statistically Significant Difference Among the Substances after Concentration Correction (Step 152 in FIG. 1)

After the typical test (in this case, Mann-Whitney test) was performed using a value in which the concentration of each substance was corrected with the concentration of the substance selected at Step 142, a P value was corrected using the false discovery rate (FDR), and a Q value was calculated. A substance having a significant difference of Q<0.05 was selected.

A procedure of developing a mathematical model of distinguishing the subjects with pancreatic cancer from the healthy subjects will be then described with reference to FIG. 3.

Using the marker selected at Step 150 or 152 in FIG. 1, a multiple logistic regression model (MLR model) that is a mathematical model was developed from a state in which a variable did not exist at Step 200. In the analysis of multiple logistic regression (MLR), a regression equation of P that is

ln(P/1−P)=b₀+b₁x₁+b₂x₂+b₃x₃+ . . . +b_kx_k (1)

is determined using k description variables x₁, x₂, x₃, . . . , and x_kfor a ratio P as a target variable.

Specifically, a combination of the smallest independent variables that did not correlate with each other was selected at Step 210, for example, using a stepwise forward selection method of stepwise variable selection. A P value at which the variable was added was 0.05, a P value at which the variable was eliminated was 0.05, and a variable x_iwas selected.

At Step 220, the data were divided into learning data and evaluation data, and at Step 230, a model was formed from the learning data and evaluated using the evaluation data. In cross validation of Loop 1 in FIG. 3, Steps 220 and 230 were repeated.

At Step 240, receiver operating characteristic (ROC) analysis was performed using the selected model. An area under the ROC curve (AUC) and a 95% confidential interval (CI) were calculated, and the model was evaluated. In accordance with the ROC curve, a curve of Y=X+α (α is a constant) was drawn. When the value of α was decreased from 1 to 0, the value of α that first touched the ROC curve was determined. Thus, an optimal cut-off value was determined.

Next, the process proceeded to Step 250, and a model having the best accuracy as the result of cross validation was selected.

Herein, a stepwise method is used. The stepwise method includes three kinds of a forward selection method, a stepwise forward selection method, and a backward selection method. The threshold value may be adjusted to a threshold value of P<0.05, and variable may be added. Therefore, the model having the best accuracy can be selected by forming a model many times at a larger loop 2 in FIG. 3.

Specifically, for evaluation of the MLR model, values of risk of pancreatic cancer (PC) with respect to saliva of breast cancer, oral cancer (CP), and IPMN were calculated. A group of the healthy subjects (C), and the subjects with CP and IPMN was formed. An AUC value that could identify pancreatic cancer from this group was calculated. The data were randomly divided into 10, a model was formed using 90% of the data, and the model was evaluated by the rest values of 10%. This operation was repeated 10 times. All the cases were selected once for evaluation, and cross validation (CV) of collecting the evaluation data and calculating the AUC value was performed.

10. Results of Model of Distinguishing Pancreatic Cancer from Searched Substance

FIG. 2 shows substances that exhibited high correlation values with the metabolites quantitatively determined at Step 120 in FIG. 1 at Step 142. In FIG. 2, a line is drawn between substances having R≧0.8. Eight clusters (groups of metabolites) are confirmed, but a cluster on the far left upper side in the drawing contains the most substances. In the cluster, alanine (Ala) forms the most networks with other substances. Therefore, alanine is determined as a metabolite for normalizing the concentration of the whole saliva. The metabolite used for normalization is not limited to the substance forming the most networks with other substances. For example, the total concentration of the metabolites, the sum of signals obtained during measurement of saliva by CE-MS (total ion electropherogram), or the area of a peak that is at a central order when all detected signals are sorted according to size may be used for normalization. The variable selection and the mathematical model are not limited to the stepwise method and the MLR model, respectively.

For example, for the variable selection, a correlation-based feature subset method (see M. A. Hall (1998). Correlation-based Feature Subset Selection for Machine Learning. Hamilton, New Zealand.), a relief method (see Marko Robnik-Sikonja, Igor Kononenko (1997). An adaptation of Relief for attribute estimation in regression. In: Fourteenth International Conference on Machine Learning, 296-304.), an SVM valiable selection method (see I. Guyon, J. Weston, S. Barnhill, V. Vapnik (2002). Gene selection for cancer classification using support vector machines. Machine Learning. 46(1-3): 389-422.), or the like may be applied.

For the mathematical model, a mechanical learning method of dividing two groups may be applied. For example, Bayesian estimate (see Berger, James 0 (1985). Statistical Decision Theory and Bayesian Analysis. Springer Series in Statistics (Second ed.). Springer-Verlag. ISBN 0-387-96098-8.), neural network (ANN) (see D. E. Rumelhart, G. E. Hinton, and R. J. Williams, (1986): Learning representations by back-propagating errors, Nature, 323-9, 533-536.), support vector machine (SVM) (see J. Platt (1998) Fast Training of Support Vector Machines using Sequential Minimal Optimization. In B. Schoelkopf and C. Burges and A. Smola, editors, Advances in Kernel Methods-Support Vector Learning), Alternative decision tree (ADTree) (see Yoav Freund and Llew Mason (1999) The Alternating Decision Tree Algorithm. Proceedings of the 16th International Conference on Machine Learning, 124-133, and Freund, Y., Mason, L. (1999) The alternating decision tree learning algorithm. In: Proceeding of the Sixteenth International Conference on Machine Learning, Bled, Slovenia, 124-133), decision tree (see Ross Quinlan (1993). C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers, San Mateo, Calif.), PART model (see Eibe Frank, Ian H. Witten (1998) Generating Accurate Rule Sets Without Global Optimization. In: Fifteenth International Conference on Machine Learning, 144-151), Random forest, PLS discriminant analysis (see Partial least squares-discriminant analysis; PLS-DA)(Lindgren, F; Geladi, P; Wold, S (1993). The kernel algorithm for PLS. J. Chemometrics 7: 45-59. doi: 10.1002/cem.1180070104.), Orthogonal PLS discriminant analysis (OPLS-DA) (see Trygg, J., & Wold, S. (2002). Orthogonal projections to latent structures (O-PLS). Journal of Chemometrics, 16(3), 119-128, and Breiman, Leo (2001). Random Forests. Machine Learning 45 (1): 5-32. doi: 10.1023/A: 1010933404324.), or the like may be applied. Bootstrap method and Bagging method (see Breiman, Leo (1996) Bagging predictors. Machine, Learning24 (2): 123-140) in which prediction is performed using average and majority of predictive values of a plurality of mathematical models that are obtained by forming a plurality of mechanical learning methods of dividing two groups may be used. Further, separation may be performed using a principal component in principal component analysis (Principal Component Analysis; PCA) (see Hotelling, H. (1933). Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, 24, 417-441) that is unsupervised learning. FIG. 4 is a model of decision tree that distinguishes the subjects with pancreatic cancer from the healthy subjects. The concentrations of metabolites that were normalized with a concentration marker (in this case, Ala) were used. The area under the ROC curve was 0.856 and the area under the ROC curve during 10-fold cross validation was 0.653.

Substances having a high ability of distinguishing the subjects with pancreatic cancer from the healthy subjects at Step 152 of the above section 9 are shown in Table 2.

TABLE 2

CONCENTRATION

NORMALIZED WITH ALA (NO UNIT)

HEALTHY SUBJECT
PANCREATIC CANCER
DETECTION RATIO (%)

NAME OF

STANDARD

STANDARD
HEALTHY
PANCREATIC
MANN-WHITNEY TEST
ROC CURVE

SUBSTANCE
AVERAGE
DEVIATION
AVERAGE
DEVIATION
SUBJECT
CANCER
P VALUE
Q VALUE
AREA
95% CI
P VALUE

N8-ACETYLSPERMIDINE
0.0015
0.0015
0.0049
0.0071
70
80
1.55773E−06
0.000190043
0.7175
0.6414 to 0.7936
0.0001

CREATININE
0.1793
0.1349
0.1026
0.0945
100
99
1.74565E−05
0.001064849
0.695
0.6140 to 0.7759
0.0001

SPERMINE
0.0129
0.0222
0.0318
0.0477
63
83
3.99507E−05
0.001624661
0.6852
0.6036 to 0.7669
0.0001

ASPARTIC ACID
0.5559
0.2454
0.4290
0.2803
98
100
9.5103E−05
0.002320512
0.6772
0.5965 to 0.7578
0.0001

N1-ACETYLSPERMIDINE
0.0237
0.0147
0.0407
0.0351
97
99
0.000141561
0.002878407
0.6727
0.5929 to 0.7526
0.0001424

N1-ACETYLSPERMINE
0.0019
0.0045
0.0046
0.0060
30
60
7.37102E−05
0.002243162
0.6579
0.5868 to 0.7490
0.0002175

CYTIDINE
0.0232
0.0320
0.0107
0.0198
79
56
0.000175296
0.00305516
0.6663
0.5828 to 0.7499
0.0002494

α-AMINOADTPIC ACID
0.0381
0.0234
0.0689
0.0811
97
99
0.000784335
0.010632096
0.6525
0.5712 to 0.7337
0.0007859

CYTOSINE
0.0042
0.0094
0.0105
0.0180
35
61
0.000495928
0.007562907
0.6489
0.5662 to 0.7316
0.001037

BETAINE
0.2236
0.3431
0.1548
0.4208
98
98
0.001209678
0.013416426
0.5469
0.5614 to 0.7325
0.001211

UREA
39.1920
43.7834
23.5791
42.6852
98
98
0.001345148
0.013675675
0.6455
0.5595 to 0.7316
0.001347

HOMOVANILLIC ACID
0.0653
0.1133
0.0999
0.1260
46
75
0.00108606
0.013249934
0.845
0.5573 to 0.7327
0.001404

N-ACETYLNEURAMINIC ACID
1.6138
1.8792
0.9916
1.3150
92
95
0.00158931
0.014559781
0.6433
0.5560 to 0.7307
0.001592

CYSTINE
0.0040
0.0062
0.0068
0.0071
43
70
0.001670795
0.014559781
0.0381
0.5526 to 0.7236
0.002351

UROCANIC ACID
0.0965
0.0780
0.0696
0.0626
98
97
0.003836014
0.029249607
0.5313
0.5458 to 0.7167
0.003834

FUMARIC ACID
0.0104
0.0209
0.0206
0.0294
35
59
0.00304094
0.024732975
0.6262
0.5418 to 0.7105
0.005451

1,3-DIAMINOPROPANE
0.0516
0.0554
0.0304
0.0397
78
73
0.00511035
0.034103773
0.8261
0.6368 to 0.7154
0.005477

HYPOTAURINE
0.0322
0.0508
0.0498
0.0739
51
78
0.004991032
0.034103773
0.6255
0.5352 to 0.7157
0.005712

NICOTINIC ACID
0.2649
1.1623
0.1163
0.5318
75
65
0.005311243
0.034103773
0.6246
0.5365 to 0.7127
0.006067

AGMATINE
0.0050
0.0050
0.0033
0.0039
81
59
0.005639892
0.034403344
0.6244
0.5377 to 0.7112
0.006123

VALINE
0.3794
0.2213
0.4894
0.3424
100
99
0.006972284
0.040119018
0.6225
0.5380 to 0.7070
0.006964

2-HYDROXY-4-METHYLPENTANOIC ACID
0.0645
0.0686
0.0949
0.0948
75
94
0.007234577
0.040119018
0.6218
0.5333 to 0.7103
0.007288

ALANYL-ALANINE
0.0301
0.0175
0.0247
0.0139
89
86
0.010929763
0.055559628
0.6155
0.5261 to 0.7049
0.01092

CITRIC ACID
0.3689
0.5593
0.2466
0.5154
95
85
0.014695008
0.067709323
0.6107
0.5265 to 0.6949
0.01474

GLUCOSAMINE
0.0124
0.0117
0.0078
0.0104
85
55
0.01143552
0.055805337
0.6108
0.5209 to 07004
0.0148

CARNOSINE
0.0029
0.0046
0.0016
0.0040
54
38
0.008103017
0.042981222
0.609
0.5195 to 0.8985
0.01629

GLYCINE-GLYCINE
0.0154
0.0158
0.0229
0.0202
56
80
0.015589845
0.067709323
0.6086
0.5203 to 0.6968
0.01677

2-AMINOBUTYRIC ACID
0.0758
0.1971
0.0723
0.0719
95
100
0.017659224
0.074290531
0.6077
0.5190 to 0.6965
0.01762

ARGININE
0.5163
0.3841
0.4082
0.4195
100
99
0.019323999
0.078584264
0.6062
0.5197 to 0.6927
0.01928

N-ACETYLGLUTAMIC ACID
0.0035
0.0057
0.0053
0.0060
40
63
0.015062166
0.067709323
0.6052
0.5172 to 0.6933
0.02041

GLYCEROPHOSPHORIC ACID
0.3663
0.2880
0.3039
0.3335
100
99
0.020954571
0.082466378
0.6048
0.5208 to 0.6889
0.02091

PHOSPHOENOLPYRUVIC ACID
0.0130
0.0194
0.0222
0.0398
44
67
0.026459721
0.100877686
0.5972
0.5093 to 0.6852
0.03216

ISOLEUCINE
0.1286
0.0693
0.1629
0.1046
100
100
0.037465083
0.138507278
0.5945
0.5089 to 0.6800
0.003738

ADENOSINE
0.0126
0.0131
0.0093
0.0141
81
78
0.039739367
0.142594198
0.593
0.5040 to 0.6820
0.04054

GUANINE
0.0520
0.0403
0.0449
0.0543
94
91
0.04241365
0.147344023
0.5921
0.5085 to 0.6778
0.04236

DIHYDROXYACETONEPHOSPHORIC ACID
0.2462
0.1806
0.2041
0.1814
94
97
0.047321948
0.154153689
0.5901
0.5043 to 0.6758
0.04722

CADAVERINE
0.5943
0.7614
0.9336
1.2717
100
99
0.048015084
0.154153689
0.5898
0.5039 to 0.5757
0.0479

In Table 2, a detection ratio shows a ratio of cases in which a peak can be detected relative to all the cases in each group of the healthy subjects and the subjects with pancreatic cancer. A 95% confidential interval (CI) represents a value of 95% confidential interval.

A Mann-Whitney test that is a non-parametric two-group test for the healthy subject group and the pancreatic cancer group was performed between the healthy subjects and the subjects with pancreatic cancer, the p value of each of the metabolites was calculated, and the p value was corrected with the false discovery rate (FDR). A test of q value was performed.

For evaluation of sensitivity and specificity of the substances that distinguish two groups of the subjects with pancreatic cancer (PC) and the healthy subjects (C), receiver operating characteristic (ROC) analysis was performed. The results are shown in FIG. 5. The concentrations of metabolites that were normalized with the concentration marker were used. The substances contained in the MLR model and a parameter and an odds ratio thereof are shown in Table 3.

TABLE 3

ITEM
PARAMETER
P VALUE
95% CI
ODDS RATIO
95% CI

(INTERCEPT)
−0.0375549
0.9179
−0.7551751
0.67987596
—
—
—

CREATININE
8.81954122
<.0001
5.11051632
13.1970776
6765.16
165.7559
538788.1

N1-ACETYLSPERMIDINE
−36.266806
0.0017
−80.649929
−14.849848
1.78E−16
4.57E−27
3.56E−07

α-AMINOADIPIC ACID
−25.266236
0.0039
−43.368968
−9.1982815
1.04E−11
1.48E−19
0.000101

N-ACETYLNEURAMINIC
0.30754159
0.0219
0.05238651
0.58469864
1.360077
1.053783
1.79445

ACID

1,3-DIAMINOPROPANE
11.309134
0.004
4.02635127
19.6651022
81563.25
56.058
3.47E+08

The area under the ROC curve in FIG. 5 was 0.8763 (95% CI: 0.8209 to 0.9317, p<0.0001). The sensitivity of optimal cut-off value was 0.8348, and (1-specificity) was 0.2169.

Using the MLR model that can distinguish the healthy subjects and the subjects with pancreatic cancer, values calculated as risk of pancreatic cancer (PC) of the healthy subjects (C) and the subjects with pancreatic cancer (PC) as well as the patients with breast cancer, oral cancer (CP), and IPMN are shown in FIG. 6.

FIG. 6 is a diagram in which the risk of pancreatic cancer (PC) of C, PC, breast cancer, oral cancer (CP), and IPMN is plotted by the model of classifying the healthy subjects (C) and the subjects with pancreatic cancer (PC). A boxplot represents values of 10%, 25%, 50%, 75%, and 90% from the top, and values under 10% and values beyond 90% are expressed as plots.

Table 4 shows an AUC value in which the specificity and general-purpose properties of the MLR model were evaluated.

TABLE 4

DISTINGUISH

DISTINGUISH
PC FROM C +

PC FROM C
CP + IPMN

WHEN ALL THE
0.88
0.85

DATA ARE USED

IN CASE OF CV
0.86
0.83

Herein, CV represents a case of cross validation.

In FIG. 7, an ROC curve at which the MLR model was formed using the absolute concentration without concentration correction is shown. Table 5 shows selected markers and coefficients. The area under the ROC curve was 0.8264 (95% CI: 0.7619 to 0.8874, p<0.0001). The accuracy was slightly decreased as compared with a case with concentration correction, but highly accurate prediction was possible. For formation of this model, a P value at which the variable was added was 0.05, and a P value at which the variable was eliminated was 0.05 using a stepwise forward selection method of stepwise variable selection. FIG. 8 shows an ROC curve at which the P value at which the variable was added was 0.05 using a forward selection method as a method of variable selection. Table 6 shows selected markers and coefficients. The area under the ROC curve was 0.8373 (95% CI: 0.7792 to 0.8954, p<0.0001). Regardless of use of the markers and coefficients that were different from the model of FIG. 7, prediction accuracy of the same level could be achieved.

TABLE 5

ITEM
PARAMETER
P VALUE
95% CI
ODDS RATIO
95% CI

(INTERCEPT)
0.40019
0.162
−0.15107
0.977355
—
—
—

α-AMINOADIPIC
−0.64356
<.0001
−0.95539
−0.39782
0.525421
0.384661
0.671782

ACID

PHOSPHORYLCHOLINE
−0.04641
0.0427
−0.09647
−0.00235
0.954646
0.90804
0.997649

N-ACETYLNEURAMINIC
0.014466
<.0001
0.007874
0.02224
1.014571
1.007905
1.022489

ACID

TABLE 6

ITEM
PARAMETER
P VALUE
95% CI
ODDS RATIO
95% CI

(INTERCEPT)
0.277503
0.3624
−0.31567
0.884388
—
—
—

3PG
0.166107
0.0028
0.063545
0.282987
1.1807
1.065608
1.327088

N1-ACETYLSPERMINE
−2.26566
0.0127
−4.28489
−0.65321
0.103762
0.013775
0.520371

α-AMINOADIPIC
−0.9899
<.0001
−1.47201
−0.58351
0.371613
0.229465
0.557935

ACID

N-ACETYLNEURAMINIC
0.012425
0.0007
0.005686
0.020249
1.012503
1.005702
1.020456

ACID

4-(β-
4.469352
0.0041
1.397495
7.786111
87.30013
4.045055
2406.938

ACETYLAMINOETHYL)IMIDAZOLE

11. Consideration of Searched Substance

In the present invention, the concentrations of ionic metabolites contained in saliva were simultaneously measured, and markers having a high ability of distinguishing the subjects with pancreatic cancer from the healthy subjects were selected. Further, a model having higher accuracy (sensitivity and specificity) as compared with a single substance could be developed by combining the markers.

A problem involved in using saliva is that there is a greater variation of concentrations present in saliva as compared with blood. In this method, saliva was collected under unified conditions depending on the collection time and dietary restriction before the collection. Some of the samples had trends in which the concentrations of all the substances were clearly high or low. FIG. 9 shows a difference in the total concentration of amino acids in saliva of each disease. A boxplot has the same meanings as that of FIG. 6. A Kruskal-Wallis test that is a non-parametric multiplex test was performed, and the P value was 0.0138. After that, a Dunn's post test was performed. Only between C and PC, the P value was less than 0.05.

The concentration in the subjects with pancreatic cancer (PC) is significantly higher than that in the healthy subjects (C), and as an indication exhibiting a risk of pancreatic cancer, a high total concentration in saliva itself may be used. However, some of the samples of C have a concentration higher than PC, and in contrast, some of the samples of PC have a concentration lower than C. Therefore, when the samples are simply considered for a risk, the accuracy is low (from results of ROC analysis between C and PC in data of FIG. 8, AUC=0.6282, and p=0.004756).

When only a sample having a concentration falling within a certain range except for the samples (the samples of C having higher concentration, and the samples of PC having lower concentration) is a target of a test, based on the whole concentration, examination should be omitted. Therefore, the fluctuation of the entire concentration was offset by performing normalization using a substance that has a high correlation with the entire metabolite concentration of the saliva and can be detected in all the samples by the method shown in FIG. 2. The normalization may be omitted.

Among the substances as marker candidates in Table 2, polyamines such as spermine, and acetylated polyamines such as N8-acetylspermidine, N1-acetylspermidine, and N1-acetylspermine are each a substance that reflects on a state of the pancreatic tissues according to various changes in cancer. However, for example, in a case of spermine in urine, the concentration correction with creatinine is only considered. Therefore, spermine cannot achieve the accuracy that a tumor marker measured in a blood test can achieve. Because polyamines in blood are taken up by erythrocytes (see Fu N N, Zhang H S, MaM, Wang H. (2007) Quantification of polyamines in human erythrocytes using a new near-infrared cyanine 1-(epsilon-succinimidyl-hexanoate)-1′-methyl-3,3,3′,3′-tetramethyl-indocarbocyanine-5,5′-disulfonate potassium with CE-LIF detection. Electrophoresis. 28(5): 822-9), the amount of polyamines in a free state is extremely small, and the concentration thereof in urine is extremely low. Even when polyamines in blood and urine are measured in breast cancer, the highest concentration of spermidine is about 140 nM (nanomol), and the concentration of N-acetylspermidine is about 64 nM (nanomol). Thus, concentrations that are much lower than the concentrations in saliva are reported (see Byun J A, Lee S H, Jung B H, Choi M H, Moon M H, Chung B C. (2008) Analysis of polyamines as carbamoyl derivatives in urine and serum by liquid chromatography-tandem mass spectrometry. Biomed Chromatogr. 22(1): 73-80). The quantitative determination of polyamines in erythrocytes requires a complicated step. Therefore, a diagnosis method found in the present invention has characteristics in which a highly accurate prediction can be achieved due to the contribution of the following three points, including (i) use of saliva capable of detecting the marker substances at high concentration, (ii) a decrease in dispersion generated at each measurement due to a simple treatment process for measurement, and (iii) use of the mathematical model in combination with the markers. A difference in mRNA in saliva between the patients with pancreatic cancer and the healthy subjects is already known (Non-Patent Literature 4). However, mRNA is completely different because a molecular group to which the present invention is directed is a metabolite. The variation of metabolites by themselves in saliva depending on pancreatic cancer is already known (Non-Patent Literature 5). However, substances that are not disclosed in known documents are used as a marker in the present invention, and a mathematical model for eliminating the effect of a specific concentration variation in saliva and identifying pancreatic cancer with high sensitivity and specificity can be developed.

With respect to four groups of healthy subjects, chronic pancreatitis, IPMN, and pancreatic cancer, a distribution of risk of pancreatic cancer that is predicted by the MLR model shows that the model exhibits high specificity for pancreatic cancer (FIG. 6). The results of cross validation (Table 4) and the results of a test for distinguishing the pancreatic cancer group from the groups other than the pancreatic cancer group also show that this model has high sensitivity and specificity that cannot be achieved by the conventional method.

In Examples, capillary electrophoresis-mass spectroscopy (CE-MS) is used to measure the concentrations of metabolites in saliva. However, high speed liquid chromatography (LC), gas chromatography (GC), chip LC, or chip CE, or GC-MS, LC-MS, and CE-MS methods in which they are combined with a mass spectrometer (MS), a measurement method for each MS alone, an NMR method, a measurement method for a metabolite substance that is derivatized into a fluorescent substance or a UV absorptive material, or an enzyme method in which an antibody is produced and measured by an ELISA method, may be used. Regardless of the measurement method, measurement may be performed by any analysis.

Next, a biomarker for breast cancer will be described.

Cases included healthy subjects (20 cases), and patients with breast cancer (90 cases) including patients with breast cancer before initiation of treatment (37 cases), patients with breast cancer that were treated with chemotherapy, hormonotherapy, or the like. In the breast cancer cases, one patient was male and the rest were female. In the patients with breast cancer before initiation of treatment, eight cases were DCIS, and 29 cases were invasive ductal carcinoma.

A method for collecting saliva, a method for measuring metabolites, and the like were the same as those used in the biomarker for pancreatic cancer.

In a variable selection method performed during formation of multiple logistic regression model (MLR mode), only a substance of Q 0.05 was used. In FIG. 10, β-alanine (Beta-Ala), N-acetylphenylalanine (N-Acetylphenylalanine), and citrulline (Citrulline) were used for addition of variable at P≦0.05 or elimination of variable at P≧0.05 by a stepwise forward selection method. In FIG. 11, N-acetylphenylalanine, N1-acetylspermidine, and creatine (Creatine), which include N1-acetylspermidine (N1-Acetylspermidine) that is a known marker, were used in a method for adding (increasing) a variable at P≦0.05 by a stepwise forward selection method. In the model of FIG. 10, as the ROC value of each substance, the ROC value of β-alanine is 0.8373, the ROC value of N-acetylphenylalanine is 0.7122, and the ROC value of citrulline is 0.698. By conversion of the substances into the MLR model, the ROC values are increased to 0.9622. Also in FIG. 12 including a known marker, the ROC value of N-acetylphenylalanine is 0.7122, the ROC value of N-acetylspermidine is 0.7811, and the ROC value of creatine is 0.7824. It was confirmed that the ROC values were increased to 0.9365 by combination of the substances using the MLR model.

Substances in which the absolute concentration exhibits a statistic significant difference (p<0.05 in the Mann-Whitney test) between the patients with breast cancer and the healthy subjects are shown in Tables 7-1, 7-2, 7-3, and 7-4. Comparison was performed in 20 cases of the healthy subjects and all the cases (90 cases) including the patients with breast cancer before treatment without chemotherapy or hormonotherapy (37 cases). A Q value was calculated by the false discovery rate (FDR).

TABLE 7-1

CONCENTRATION (μM)

BREAST CANCER

DETECTION RATIO (%)

ONLY BEFORE
INCLUDING DURING

BREAST CANCER

HEALTHY SUBJECT
TREATMENT
TREATMENT

INCLUDING

STANDARD

STANDARD

STANDARD
HEALTHY
BEFORE
DURING

NAME OF SUBSTANCE
AVERAGE
DEVIATION
AVERAGE
DEVIATION
AVERAGE
DEVIATION
SUBJECT
TREATMENT
TREATMENT

CHOLINE
Choline
5.378
3.347
20.534
26.7069
18.249
21.29059
20
37
90

2-HYDROXYBUTYRIC ACID
2-Hydroxybutyrate
1.027
0.713
2.6841
4.1045
2.5884
3.078189
18
37
90

β-ALANINE
beta-Ala
1.233
0.919
5.4375
9.96858
4.5107
7.349677
17
36
87

3-METHYLHISDINE
3-Methylhistidine
0.085
0.180
1.2255
2.20901
1.0247
1.925432
6
28
70

α-AMINOBUTYRIC ACID
2AB
0.791
0.703
6.2284
20.2287
5.3851
16.0949
16
35
86

CADAVERINE
Cadaverine
9.083
13.402
35.46
41.1071
39.765
55.12648
19
37
89

N-ACETYL-β-ALANINE
N-Acetyl-beta-alanine
0.282
0.374
1.4267
2.0143
1.2751
1.666246
9
32
76

ISETHIONIC ACID
Isethionate
0.148
0.109
0.3581
0.27681
0.4044
0.804686
14
32
77

N-ACETYLPHENYLALANINE
N-Acetylphenylalanine
0.086
0.116
0.0172
0.07346
0.0229
0.074225
10
3
10

TRIMETHYLLYSINE
N6,N6,N6-Trimethyllysine
0.035
0.089
0.2699
0.36582
0.2515
0.342463
3
25
57

α-AMINOADIPIC ACID
alpha-Aminoadipate
0.883
0.814
3.4689
5.42307
3.0669
6.266668
17
34
83

SPERMINE
Spermine
0.065
0.119
1.3403
3.39481
1.0308
2.855626
6
28
60

N1-ACETYLSPERMIDINE
N1-Acetylspermidine
0.327
0.345
1.289
1.74192
1.1973
1.595473
15
35
87

CREATINE
Creatine
10.968
6.522
34.274
63.8828
28.365
50.60692
20
37
90

γ-BUTYROBETAINE
gamma-Butyrobetaine
1.914
1.542
7.3809
11.9304
6.1926
8.983804
20
36
89

SARCOSINE
Sarcosine
5.133
4.294
13.808
18.2185
10.827
13.26566
20
37
90

PYRUVIC ACID
Pyruvate
40.919
27.286
98.681
93.8728
105.45
133.2851
20
37
90

UROCANIC ACID
Urocanate
0.995
1.082
5.054
13.3985
3.9576
8.875522
13
32
82

PIPERIDINE
Piperidine
0.073
0.152
0.3755
0.53702
0.7133
2.011631
7
25
67

SERINE
Ser
10.300
9.622
34.007
67.4699
28.451
47.13016
20
37
90

HOMOVANILLIC ACID
Homovanillate
1.721
0.906
3.8279
3.74161
4.2265
5.204739
20
37
88

5-OXOPROLINE
5-Oxoproline
6.788
12.685
18.15
44.9634
14.684
30.06842
20
37
90

GABA
GABA
1.286
1.299
2.5587
2.18752
4.4657
16.60331
20
36
89

5-AMINOVALERIC ACID
5-Aminovalerate
195.795
209.963
827.22
1489.58
679.53
1052.043
20
37
89

TRIMETHYLAMINE-N-OXIDE
Trimethylamine N-oxide
0.091
0.195
0.4613
0.61489
0.4739
1.005668
4
24
61

2-HYDROXYVALERIC ACID
2-Hydroxypentanoate
5.843
4.793
16.188
25.3406
15.137
19.67004
20
37
90

CARNITINE
Carnitine
0.757
0.633
1.8914
2.68246
1.9392
4.54229
20
37
90

ISOPROPANOLAMINE
Isopropanolamine
0.530
0.806
2.2374
4.13934
2.047
3.62564
12
31
77

THREONINE
Thr
4.163
3.602
12.408
19.5441
12.486
19.3648
20
37
90

HYPOTAURINE
Hypotaurine
0.318
1.106
1.9495
2.79625
1.6802
2.564848
2
19
42

LACTIC ACID
Lactate
128.842
80.253
348.15
495.025
394.62
807.0465
20
37
90

2-HYDROXY-4-
2-Hydroxy-4-
2.068
2.293
6.7127
12.7565
5.9735
9.612041
20
36
89

METHYLPENTANOIC ACID
methylpentanoate

HYDROXYPROLINE
Hydroxyproline
0.437
0.731
1.6485
3.26692
1.4398
2.683583
9
31
75

MANN-WHITNEY TEST

INCLUDING

ONLY BEFORE
DURING

TREATMENT
TREATMENT

vs HEALTHY
vs HEALTHY

SUBJECT
SUBJECT
PUBLICLY

NAME OF SUBSTANCE
P VALUE
Q VALUE
P VALUE
Q VALUE
KNOWN
SIGNIFICANT

CHOLINE
Choline
1.18E−05
0.001
3.07E−06
0.0002

1

2-HYDROXYBUTYRIC ACID
2-Hydroxybutyrate
4.11E−05
0.002
4.39E−06
0.0002

1

β-ALANINE
beta-Ala
3.34E−05
0.002
1.44E−05
0.0004

1

3-METHYLHISDINE
3-Methylhistidine
6.88E−05
0.002
5.69E−06
0.0002

1

α-AMINOBUTYRIC ACID
2AB
8.39E−05
0.002
2.38E−05
0.0005

1

CADAVERINE
Cadaverine
8.81E−05
0.002
2.13E−05
0.0005
78
1

N-ACETYL-β-ALANINE
N-Acetyl-beta-alanine
9.62E−05
0.002
4.60E−05
0.0008

1

ISETHIONIC ACID
Isethionate
0.0001192
0.002
5.75E−05
0.0008

1

N-ACETYLPHENYLALANINE
N-Acetylphenylalanine
0.0003692
0.004
7.54E−05
0.0009

1

TRIMETHYLLYSINE
N6,N6,N6-Trimethyllysine
0.0003921
0.004
0.000271
0.0026

1

α-AMINOADIPIC ACID
alpha-Aminoadipate
0.0004268
0.004
0.000311
0.0027

1

SPERMINE
Spermine
0.0003262
0.004
0.001251
0.005
8
1

N1-ACETYLSPERMIDINE
N1-Acetylspermidine
0.0005128
0.005
0.00023
0.0024
8
1

CREATINE
Creatine
0.0004883
0.005
0.000933
0.0043

1

γ-BUTYROBETAINE
gamma-Butyrobetaine
0.0006073
0.005
0.000385
0.0028

1

SARCOSINE
Sarcosine
0.0006649
0.005
0.002504
0.0087

1

PYRUVIC ACID
Pyruvate
0.0008749
0.006
0.000421
0.0029

1

UROCANIC ACID
Urocanate
0.001326
0.009
0.000101
0.0011

1

PIPERIDINE
Piperidine
0.0015033
0.01
5.83E−05
0.0008

1

SERINE
Ser
0.0016657
0.01
0.000509
0.0033

1

HOMOVANILLIC ACID
Homovanillate
0.0016108
0.01
0.000735
0.0041

1

5-OXOPROLINE
5-Oxoproline
0.0018661
0.01
0.000369
0.0028

1

GABA
GABA
0.0019086
0.01
0.000844
0.0042

1

5-AMINOVALERIC ACID
5-Aminovalerate
0.002093
0.01
0.000323
0.0027

1

TRIMETHYLAMINE-N-OXIDE
Trimethylamine N-oxide
0.0020797
0.01
0.000762
0.0041

1

2-HYDROXYVALERIC ACID
2-Hydroxypentanoate
0.0023391
0.011
0.00086
0.0042

1

CARNITINE
Carnitine
0.0025095
0.012
0.004876
0.0133

1

ISOPROPANOLAMINE
Isopropanolamine
0.0030194
0.012
0.000882
0.0042

1

THREONINE
Thr
0.0029044
0.012
0.001147
0.0048
7
1

HYPOTAURINE
Hypotaurine
0.0028193
0.012
0.003926
0.0116

1

LACTIC ACID
Lactate
0.0029968
0.012
0.004292
0.0122

1

2-HYDROXY-4-
2-Hydroxy-4-
0.0033304
0.013
0.001146
0.0048

1

METHYLPENTANOIC ACID
methylpentanoate

HYDROXYPROLINE
Hydroxyproline
0.0035492
0.013
0.000991
0.0044

1

TABLE 7-2

CONCENTRATION (μM)

BREAST CANCER

DETECTION RATIO (%)

ONLY BEFORE
INCLUDING DURING

BREAST CANCER

HEALTHY SUBJECT
TREATMENT
TREATMENT

INCLUDING

STANDARD

STANDARD

STANDARD
HEALTHY
BEFORE
DURING

NAME OF SUBSTANCE
AVERAGE
DEVIATION
AVERAGE
DEVIATION
AVERAGE
DEVIATION
SUBJECT
TREATMENT
TREATMENT

ALANINE
Ala
21.390
19.430
67.526
117.162
69.232
120.485
20
37
90

VALINE
Val
11.610
15.718
49.326
129.44
41.908
95.743
20
37
90

BUTYRIC ACID
Butanoate
65.627
64.301
204.46
276.832
216.69
280.267
20
37
89

SPERMIDINE
Spermidine
1.339
1.342
3.874
5.26322
3.3277
4.61563
20
36
88

N8-ACETYLSPERMIDINE
N8-Acetylspermidine
0.013
0.031
0.0715
0.0938
0.0737
0.11937
3
20
49

ADENINE
Adenine
0.673
0.482
1.4111
1.21709
1.2015
1.05126
16
34
81

PUTRESCINE
Putrescine(1,4-
39.277
40.268
182.54
417.293
137.74
287.799
20
37
89

Butanediamine)

N6-ACETYLLYSINE
N-epsilon-Acetyllysine
0.077
0.173
0.346
0.42637
0.3251
0.41918
4
22
51

6-HYDROXYHEXANOIC ACID
6-Hydroxyhexanoate
0.949
1.156
0.2497
0.66804
0.4526
0.93174
9
5
19

PROPIONIC ACID
Propionate
212.587
228.193
546.67
646.293
494.44
565.74
20
37
89

BETAINE
Betaine
3.025
3.596
5.3352
5.90053
8.0909
19.3663
18
36
88

N-ACETYLPUTRESCINE
N-Acetylputrescine
2.272
2.472
8.8389
21.6884
7.1087
15.0297
20
36
89

GLYCINE
Gly
65.011
74.106
244.6
523.14
184.11
365.957
20
37
90

HYPOXANTHINE
Hypoxanthine
3.410
3.105
8.2703
9.59661
7.6871
9.55741
19
35
85

LEUCINE
Leu
10.154
12.642
40.029
111.084
32.465
79.3207
20
37
90

CROTONIC ACID
Crotonate
3.972
4.571
13.987
16.6822
13.974
18.9801
13
32
75

ORNITHINE
Ornithine
14.858
14.943
49.144
89.3732
38.014
61.7178
20
37
90

ISOLEUCINE
Ile
3.739
5.159
16.472
49.1296
13.323
34.8535
20
37
90

TRYPTOPHAN
Trp
1.021
1.114
2.3621
3.28089
2.3166
4.00336
16
36
88

CITRULLINE
Citrulline
12.637
20.416
23.047
32.1049
19.889
24.4814
20
37
90

GLUTAMINE
Gln
17.582
20.941
52.563
125.806
48.734
118.722
20
37
90

N1-,N8-DIACETYLSPERMIDINE
N1,N8-Diacetylspermidine
0.097
0.090
0.2579
0.35117
0.2501
0.38753
15
32
76

PROLINE
Pro
41.381
58.124
172.84
450.311
117.48
298.65
20
37
90

2-OXOISOPENTANOIC ACID
2-Oxoisopentanoate
0.783
0.465
1.3519
1.12646
1.3467
1.33154
17
34
81

GLUTAMIC ACID
Glu
34.682
40.278
73.856
138.564
63.928
110.975
20
37
90

4-METHYLBENZOATE
4-Methylbenzoate
13.815
15.388
34.85
50.4018
30.807
39.5322
20
35
87

3-(4-HYDROXYPHENYL)
3-(4-
5.764
6.256
18.082
23.9319
17.157
24.1955
19
35
86

PROPIONIC ACID
Hydroxyphenyl)propionate

CYSTEIC ACID
Cysteate
0.140
0.129
0.4086
0.67652
0.3194
0.5148
13
27
60

AZELAIC ACID
Azelate
0.037
0.129
0.0975
0.19163
0.0913
0.15074
2
15
39

RIBULOSE-5-PHOSPHORIC
Ru5P
3.152
2.063
4.9381
3.74484
4.7181
3.89669
20
36
88

ACID

PICOLINIC ACID
Pipecolate
0.622
0.813
0.9077
0.96916
0.8528
0.87247
17
34
83

PHENYLALANINE
Phe
14.694
12.454
28.133
35.1399
25.384
29.0587
20
37
90

MANN-WHITNEY TEST

INCLUDING

ONLY BEFORE
DURING

TREATMENT
TREATMENT

vs HEALTHY
vs HEALTHY

SUBJECT
SUBJECT
PUBLICLY

NAME OF SUBSTANCE
P VALUE
Q VALUE
P VALUE
Q VALUE
KNOWN
SIGNIFICANT

ALANINE
Ala
0.00362
0.013
0.00148
0.006
7
1

VALINE
Val
0.00381
0.014
0.00064
0.004
7
1

BUTYRIC ACID
Butanoate
0.00424
0.015
0.00065
0.004

1

SPERMIDINE
Spermidine
0.00457
0.015
0.00821
0.02
8
1

N8-ACETYLSPERMIDINE
N8-Acetylspermidine
0.00497
0.016
0.00323
0.01
8
1

ADENINE
Adenine
0.00514
0.016
0.01372
0.03

1

PUTRESCINE
Putrescine(1,4-Butanediamine)
0.00536
0.017
0.00201
0.007
78
1

N6-ACETYLLYSINE
N-epsilon-Acetyllysine
0.00552
0.017
0.00453
0.013

1

6-HYDROXYHEXANOIC ACID
6-Hydroxyhexanoate
0.00624
0.019
0.02908
0.058

1

PROPIONIC ACID
Propionate
0.00642
0.019
0.00394
0.012

1

BETAINE
Betaine
0.00688
0.02
0.00347
0.011

1

N-ACETYLPUTRESCINE
N-Acetylputrescine
0.00709
0.02
0.00315
0.01

1

GLYCINE
Gly
0.00727
0.02
0.00281
0.009
7
1

HYPOXANTHINE
Hypoxanthine
0.00905
0.024
0.01064
0.025

1

LEUCINE
Leu
0.00953
0.024
0.00198
0.007
7
1

CROTONIC ACID
Crotonate
0.00944
0.024
0.01306
0.03

1

ORNITHINE
Ornithine
0.01051
0.026
0.00399
0.012
78
1

ISOLEUCINE
Ile
0.01076
0.026
0.00159
0.006
7
1

TRYPTOPHAN
Trp
0.01237
0.03
0.00849
0.02

1

CITRULLINE
Citrulline
0.01463
0.034
0.00566
0.015

1

GLUTAMINE
Gln
0.0164
0.038
0.0207
0.043

1

N1-,N8-DIACETYLSPERMIDINE
N1,N8-Diacetylspermidine
0.01899
0.043
0.0671
0.118
8
1

PROLINE
Pro
0.0201
0.045
0.0066
0.017

1

2-OXOISOPENTANOIC ACID
2-Oxoisopentanoate
0.02087
0.046
0.0153
0.033

1

GLUTAMIC ACID
Glu
0.02196
0.047
0.02156
0.043
7
1

4-METHYLBENZOATE
4-Methylbenzoate
0.02558
0.054
0.00974
0.023

1

3-(4-HYDROXYPHENYL)
3-(4-Hydroxyphenyl)propionate
0.02669
0.056
0.00776
0.019

1

PROPIONIC ACID

CYSTEIC ACID
Cysteate
0.02938
0.06
0.13727
0.209

1

AZELAIC ACID
Azelate
0.03075
0.062
0.01386
0.03

1

RIBULOSE-5-PHOSPHORIC
Ru5P
0.03961
0.079
0.04804
0.09

1

ACID

PICOLINIC ACID
Pipecolate
0.0403
0.079
0.0329
0.063

1

PHENYLALANINE
Phe
0.04301
0.083
0.02133
0.043

1

TABLE 7-3

CONCENTRATION (μM)

DETECTION

BREAST CANCER

RATIO (%)

ONLY
INCLUDING DURING

BREAST

HEALTHY SUBJECT
BEFORE TREATMENT
TREATMENT

CANCER

STANDARD

STANDARD

STANDARD
HEALTHY
BEFORE

NAME OF SUBSTANCE
AVERAGE
DEVIATION
AVERAGE
DEVIATION
AVERAGE
DEVIATION
SUBJECT
TREATMENT

O-PHOSPHOSERINE
O-Phosphoserine
0.708
0.930
1.5125
2.1099
41.317
1.73873
14
35

MALONIC ACID
Malonate
0.63945
0.329939
1.0177
0.7514
1.055
1.06444
18
36

HEXANOIC ACID
Hexanoate
13.3189
15.28551
28.902
34.775
31.21
37.8054
20
37

3-PHOSPHOGLYCERIC ACID
3PG
3.04736
3.338531
4.1156
4.6142
3.806
3.88737
20
36

N-ACETYLGLUTAMIC ACID
N-Acetylglutamate
0.13999
0.127875
0.3581
0.5293
0.304
0.42572
15
30

N-ACETYLGLUCOSAMINE-6-
N-Acetylglucosamine
0.17916
0.254872
0.3779
0.5735
0.355
0.66918
10
25

PHOSPHORIC ACID
6-phosphate

2-OXOBUTYRIC ACID
2-Oxobutyrate
3.12964
2.243161
5.9299
5.5128
6.043
6.8824
17
33

GLYCYL-GLYCINE
Glu-Glu
0.22668
0.421861
0.3931
0.5354
0.391
0.64314
6
23

LYSINE
Lys
36.0936
38.50907
120.17
311.97
86.9
213.649
20
36

ASPARTIC ACID
Asp
17.2927
16.49612
25.784
30.243
23.1
23.215
20
37

METHIONINE
Met
1.335
1.93562
2.2982
3.2471
2.556
4.53023
12
31

P-HYDROXYPHENYLACETIC ACID
p-Hydroxyphenylacetate
12.5798
24.01714
31.185
64.355
24
45.5421
12
29

AGMATINE
Agmatine
0.09511
0.084621
0.1607
0.1443
0.148
0.1435
13
28

2-DEOXYRIBOSE 1-PHOSPHORIC ACID
2-Deoxyribose 1-phosphate
0.39332
0.743072
0.8457
1.6264
0.682
1.22119
10
26

PEPLOMYCIN
PEP
0.60591
0.643731
0.9077
1.0752
0.801
0.88615
18
36

DIHYDROXYACETONEPHOSPHORIC ACID
DHAP
7.36598
3.854232
9.1677
5.3083
8.998
5.96742
20
37

GLYCOLIC ACID
Glycolate
9.57279
3.591728
12.61
8.9417
11.9
7.76016
20
36

HISTAMINE
Histamine
0.30073
0.439731
0.8814
1.9794
0.669
1.55616
13
30

N-ACETYLLEUCINE
N-Acetylleucine
0.03062
0.060836
0.1832
0.5739
0.099
0.38111
6
16

CYTIDINE DISODIUM 5′-MONOPHOSPHATE
CMP
0.25869
0.443067
1.0785
2.6912
0.973
2.38666
10
21

GUANINE
Guanine
0.71569
0.608172
1.492
1.7957
1.271
1.4044
14
32

4-METHYL-2-OXOPENTANOIC ACID
4-Methyl-2-oxopentanoate
1.58389
0.943891
2.3178
2.2787
2.289
2.07591
20
37

N-ACETYLASPARTIC ACID
N-Acetylaspartate
0.68824
0.381095
1.1339
1.3923
1.025
1.04935
20
37

TYROSINE
Tyr
21.4067
18.11086
35.63
43.578
31.4
38.7827
20
37

SUCCINIC ACID
Succinate
19.5264
17.89444
35.029
69.034
42.57
100.728
20
37

GLYCEROPHOSPHORIC ACID
Glycerophosphate
8.39508
4.672054
9.4613
4.481
10.02
7.03863
20
37

ALANYL-ALANIHE
Ala-Ala
0.77481
0.970735
1.2475
1.6193
1.115
1.47621
13
27

1,3-DIAMINOPROPANE
1,3-Diaminopropane
1.50535
1.791791
2.2101
2.494
1.983
2.11129
12
29

3-PHENYLPROPIONIC ACID
3-Phenylpropionate
9.9887
10.32626
17.117
20.316
18.22
21.885
20
31

CIS-ACONITATE
cis-Aconitate
0.12953
0.117343
0.2169
0.3154
0.35
0.94761
12
30

DETECTION
MANN-WHITNEY TEST

RATIO (%)
ONLY
INCLUDING DURING

BREAST CANCER
BEFORE TREATMENT
TREATMENT

INCLUDING DURING
vs HEALTHY SUBJECT
vs HEALTHY SUBJECT
PUBLICLY

NAME OF SUBSTANCE
TREATMENT
P VALUE
Q VALUE
P VALUE
Q VALUE
KNOWN
SIGNIFICANT

O-PHOSPHOSERINE
O-Phosphoserine
84
0.054
0.1023
0.047
0.08962

1

MALONIC ACID
Malonate
88
0.056
0.10535
0.017
0.03548

1

HEXANOIC ACID
Hexanoate
89
0.062
0.11442
0.007
0.01869

1

3-PHOSPHOGLYCERIC ACID
3PG
89
0.066
0.1192
0.082
0.14011

0

N-ACETYLGLUTAMIC ACID
N-Acetylglutamate
78
0.071
0.12654
0.055
0.10058

0

N-ACETYLGLUCOSAMINE-6-
N-Acetylglucosamine
58
0.072
0.12654
0.145
0.21651

0

PHOSPHORIC ACID
6-phosphate

2-OXOBUTYRIC ACID
2-Oxobutyrate
76
0.077
0.13429
0.102
0.17022

0

GLYCYL-GLYCINE
Glu-Glu
52
0.084
0.1439
0.094
0.15826

0

LYSINE
Lys
89
0.088
0.14876
0.074
0.12852

0

ASPARTIC ACID
Asp
90
0.094
0.15744
0.056
0.10156
7
0

METHIONINE
Met
75
0.097
0.15951
0.058
0.10439

0

P-HYDROXYPHENYLACETIC ACID
p-Hydroxyphenylacetate
75
0.101
0.1639
0.032
0.06241

1

AGMATINE
Agmatine
66
0.106
0.16947
0.185
0.26216

0

2-DEOXYRIBOSE 1-PHOSPHORIC ACID
2-Deoxyribose 1-phosphate
63
0.138
0.21673
0.107
0.17574

0

PEPLOMYCIN
PEP
85
0.139
0.21673
0.162
0.23485

0

DIHYDROXYACETONEPHOSPHORIC ACID
DHAP
90
0.146
0.22459
0.286
0.37699

0

GLYCOLIC ACID
Glycolate
85
0.16
0.24375
0.115
0.18355

0

HISTAMINE
Histamine
77
0.169
0.25525
0.129
0.20202

0

N-ACETYLLEUCINE
N-Acetylleucine
28
0.179
0.26598
0.67
0.73379

0

CYTIDINE DISODIUM 5′-MONOPHOSPHATE
CMP
54
0.19
0.27997
0.202
0.28365

0

GUANINE
Guanine
78
0.217
0.31541
0.144
0.21651

0

4-METHYL-2-OXOPENTANOIC ACID
4-Methyl-2-oxopentanoate
90
0.222
0.31859
0.113
0.18302

0

N-ACETYLASPARTIC ACID
N-Acetylaspartate
90
0.225
0.31979
0.162
0.23485

0

TYROSINE
Tyr
90
0.229
0.32104
0.227
0.31474
7
0

SUCCINIC ACID
Succinate
90
0.238
0.32735
0.24
0.32643

0

GLYCEROPHOSPHORIC ACID
Glycerophosphate
90
0.238
0.32735
0.287
0.37699

0

ALANYL-ALANIHE
Ala-Ala
62
0.253
0.34319
0.378
0.47192

0

1,3-DIAMINOPROPANE
1,3-Diaminopropane
71
0.275
0.3699
0.278
0.37322
8
0

3-PHENYLPROPIONIC ACID
3-Phenylpropionate
80
0.296
0.38057
0.126
0.19886

0

CIS-ACONITATE
cis-Aconitate
68
0.298
0.38057
0.134
0.20719

0

TABLE 7-4

CONCENTRATION (μM)

DETECTION

BREAST CANCER

RATIO (%)

ONLY
INCLUDING DURING

BREAST

HEALTHY SUBJECT
BEFORE TREATMENT
TREATMENT

CANCER

STANDARD

STANDARD

STANDARD
HEALTHY
BEFORE

NAME OF SUBSTANCE
AVERAGE
DEVIATION
AVERAGE
DEVIATION
AVERAGE
DEVIATION
SUBJECT
TREATMENT

3-HYDROXYBUTYRIC ACID
3-Hydroxybutyrate
6.65264
4.679435
8.1673
5.6145
9.119
8.0353
20
37

BENZOIC ACID
Benzoate
11.5158
4.957046
9.6971
6.7622
10.98
8.65846
18
27

GUANOSINE
Guanosinc
0.18071
0.208922
0.3316
0.4262
0.313
0.52441
10
19

6-PHOSPHOGLUCONIC ACID
6-Phosphogluconate
0.45188
0.420291
0.6064
0.5832
0.547
0.59845
16
33

URIDYLIC ACID
UMP
0.12161
0.168081
0.2179
0.2884
0.313
0.63248
8
18

ADENOSINE
Adenosine
0.14107
0.164335
0.1919
0.2021
0.163
0.18723
11
22

MUCIC ACID
Mucate
0.24406
0.271429
0.1807
0.2273
0.283
0.54345
11
18

ETHANOLAMINEPHOSPHORIC ACID
Ethanolamine phosphate
32.346
26.12692
60.343
94.201
54.75
82.4473
18
34

ADIPATE
Adipate
0.49755
0.32343
0.5903
0.531
0.562
0.39362
19
35

2-ISOPROPYLMALATE
2-Isopropylmalate
0.25413
0.194751
0.2663
0.1619
0.261
0.18967
20
36

PHOSPHORYLCHLORINE
Phosphorylcholine
5.9658
5.079958
5.9868
6.9568
6.471
7.37476
20
31

NICOTINATE
Nicotinate
1.84972
1.398171
2.2547
2.2648
2.606
2.66505
16
23

1-METHYL-2-PYRROLIDINONE
1-Methyl-2-pyrrolidinone
23.0783
14.05832
30.672
33.782
27.93
28.4191
20
36

MALIC ACID
Malate
1.63046
0.501086
2.4789
2.9025
2.689
2.85035
20
37

PANTOTHENIC ACID
Pantothenate
0.15636
0.284826
0.2627
0.5432
0.276
0.49056
7
14

N-ACETYLNEURAMINATE
N-Acetylneuraminate
49.3584
36.98463
43.496
30.441
42.27
34.3608
20
37

HISTAMINE
His
11.4764
8.07934
16.58
20.947
15.95
19.4737
20
37

FUMARIC ACID
Fumarate
0.35582
0.233544
0.4677
0.5926
0.57
0.68895
15
30

2-DEOXYGLUCOSE-6-PHOSPHORIC ACID
2-Deoxyglucose 6-phosphate
0.35743
0.74316
0.4996
1.0832
0.37
0.83301
5
11

CITRIC ACID
Citrate
4.60257
3.675132
4.8562
6.0137
8.051
19.8864
20
35

4-ACETYLBUTYRIC ACID
4-Acetylbutyrate
0.09595
0.160288
0.0775
0.1312
0.072
0.12937
6
11

O-ACETYLCARNITINE
o-Acetylcarnitine
0.93204
0.530378
1.3934
1.8255
1.283
1.90322
20
37

N-ACETYLGLUCOSAMINE-1-
N-Acetylglucosamine
0.28012
0.263447
0.265
0.3282
0.239
0.25934
17
26

PHOSPHORIC ACID
1-phosphate

SEDOHEPTULOSE-7-PHOSPHORIC ACID
S7P
0.49477
0.412779
0.5007
0.3721
0.564
0.45267
16
29

HEPTANOIC ACID
Heptanoate
0.23138
0.200911
0.2286
0.1788
0.215
0.22231
15
25

CREATININE
Creatinine
3.47024
1.223945
4.3163
3.7725
4.805
8.00241
20
37

2,5-DIHYDROXYBENZONATE
2,5-Dihydroxybenzoate
0.67706
1.220304
0.6312
1.125
0.563
0.97834
9
17

CYTIDINE
Cytidine
0.2924
0.261356
0.4567
0.6785
0.386
0.62379
14
22

ARGININE
Arg
12.0359
6.289379
15.501
15.934
14.71
14.7857
20
37

SYRINGIC ACID
Syringate
1.13065
0.376083
1.2108
0.7313
1.106
0.67811
20
34

DETECTION
MANN-WHITNEY TEST

RATIO (%)
ONLY
INCLUDING DURING

BREAST CANCER
BEFORE TREATMENT
TREATMENT

INCLUDING DURING
vs HEALTHY SUBJECT
vs HEALTHY SUBJECT
PUBLICLY

NAME OF SUBSTANCE
TREATMENT
P VALUE
Q VALUE
P VALUE
Q VALUE
KNOWN
SIGNIFICANT

3-HYDROXYBUTYRIC ACID
3-Hydroxybutyrate
90
0.288
0.38057
0.179
0.2567

0

BENZOIC ACID
Benzoate
67
0.289
0.38057
0.54
0.64014

0

GUANOSINE
Guanosinc
42
0.297
0.38057
0.675
0.73379

0

6-PHOSPHOGLUCONIC ACID
6-Phosphogluconate
77
0.319
0.40265
0.655
0.73117

0

URIDYLIC ACID
UMP
42
0.324
0.40524
0.341
0.43454

0

ADENOSINE
Adenosine
49
0.356
0.44105
0.613
0.70354

0

MUCIC ACID
Mucate
49
0.465
0.56994
0.687
0.74006

0

ETHANOLAMINEPHOSPHORIC ACID
Ethanolamine phosphate
84
0.558
0.67738
0.543
0.64014

0

ADIPATE
Adipate
87
0.581
0.69792
0.319
0.41063

0

2-ISOPROPYLMALATE
2-Isopropylmalate
87
0.586
0.69803
0.913
0.95163

0

PHOSPHORYLCHLORINE
Phosphorylcholine
81
0.604
0.71215
0.914
0.95163

0

NICOTINATE
Nicotinate
63
0.628
0.73378
0.386
0.47816

0

1-METHYL-2-PYRROLIDINONE
1-Methyl-2-pyrrolidinone
87
0.64
0.74026
0.978
0.98625

0

MALIC ACID
Malate
90
0.651
0.74644
0.23
0.31652

0

PANTOTHENIC ACID
Pantothenate
42
0.678
0.76994
0.313
0.40777

0

N-ACETYLNEURAMINATE
N-Acetylneuraminate
90
0.744
0.80908
0.464
0.56853

0

HISTAMINE
His
90
0.738
0.80908
0.551
0.64092

0

FUMARIC ACID
Fumarate
72
0.743
0.80908
0.554
0.64092

0

2-DEOXYGLUCOSE-6-PHOSPHORIC ACID
2-Deoxyglucose 6-phosphate
24
0.744
0.80908
0.972
0.98625

0

CITRIC ACID
Citrate
86
0.732
0.80908
1
1

0

4-ACETYLBUTYRIC ACID
4-Acetylbutyrate
25
0.772
0.82508
0.625
0.71055

0

O-ACETYLCARNITINE
o-Acetylcarnitine
89
0.77
0.82508
0.96
0.98625

0

N-ACETYLGLUCOSAMINE-1-
N-Acetylglucosamine
63
0.781
0.82726
0.675
0.73379

0

PHOSPHORIC ACID
1-phosphate

SEDOHEPTULOSE-7-PHOSPHORIC ACID
S7P
72
0.814
0.85505
0.518
0.62283

0

HEPTANOIC ACID
Heptanoate
56
0.826
0.85996
0.654
0.73117

0

CREATININE
Creatinine
90
0.867
0.89576
0.877
0.92877

0

2,5-DIHYDROXYBENZONATE
2,5-Dihydroxybenzoate
38
0.898
0.92041
0.767
0.81912

0

CYTIDINE
Cytidine
49
0.959
0.96668
0.508
0.6165

0

ARGININE
Arg
90
0.953
0.96668
0.972
0.98625

0

SYRINGIC ACID
Syringate
82
0.993
0.99331
0.374
0.47183

0

A substance for which “7” or “8” is indicated in the column labeled “Publicly Known” is a known substance disclosed in Non-Patent Literature 7 or 8.

Next, a network diagram in which a line is drawn between metabolites exhibiting a correlation between metabolites in the patients with breast cancer before initiation of treatment (37 cases) and metabolites in the healthy subjects (20 cases) of R²>0.92 shown in FIG. 10 is shown. Among the substances, a substance that formed bonding lines to many substances and could be detected in all of the samples, or glutamine (Gln), was selected as a concentration-correcting substance. In the drawing, the substance is circled.

Substances in which a relative concentration exhibits a statistical significant difference (p<0.05 in the Mann-Whitney test) between the patients with breast cancer and the healthy subjects are shown in Tables 8-1, 8-2, 8-3, and 8-4. In calculating the relative concentration, the concentration of each substance was divided by the concentration of glutamine, and the value was expressed with no units.

At that time, many of the metabolites included in saliva were measured. In order to calculate the significant difference of each substance, independent statistics (for example, Mann-Whitney test) needs to be repeated. When the test is repeated at a level of significance a of 0.05, null hypothesis that is accidentally dismissed is increased. Therefore, the P value was corrected by the false discovery rate (FDR) method (Storey, J. D., & Tibshirani, R. (2003). Statistical significance for genomewide studies. Proceedings of the National academy of Sciences of the United States of America, 100, 9440-9445), and a Q value was calculated. For example, when the Q value is 0.5, true null hypothesis occupies a half of the null hypothesis that is dismissed at P<0.05.

TABLE 8-1

RELATIVE CONCENTRATION (NO UNIT)

DETECTION

BREAST CANCER

RATIO (%)

ONLY BEFORE
INCLUDING DURING

BREAST CANCER

HEALTHY SUBJECT
TREATMENT
TREATMENT

INCLUDING

STANDARD

STANDARD

STANDARD
HEALTHY
BEFORE
DURING

NAME OF SUBSTANCE
AVERAGE
DEVIATION
AVERAGE
DEVIATION
AVERAGE
DEVIATION
SUBJECT
TREATMENT
TREATMENT

CHOLINE
Choline
0.535
0.365
0.6695
0.39647
0.7124
0.392
20
37
90

2-HYDROXYBUTYRIC ACID
2-Hydroxybutyrate
0.126
0.110
0.1339
0.14048
0.1438
0.132294
18
37
90

β-ALANINE
beta-Ala
0.102
0.081
0.1503
0.07955
0.1676
0.118533
17
36
87

3-METHYLHISDINE
3-Methylhistidine
0.007
0.016
0.0294
0.03108
0.0331
0.038386
6
28
70

α-AMINOBUTYRIC ACID
2AB
0.057
0.043
0.0989
0.05536
0.1191
0.102119
16
35
86

CADAVERINE
Cadaverine
0.602
0.539
1.2257
0.86809
1.6484
1.741134
19
37
89

N-ACETYL-β-ALANINE
N-Acetyl-beta-alanine
0.021
0.031
0.0409
0.03495
0.0424
0.033048
9
32
76

ISETHIONIC ACID
Isethionate
0.017
0.016
0.0162
0.01528
0.0178
0.025157
14
32
77

N-ACETYLPHENYLALANINE
N-Acetylphenylalanine
0.007
0.011
0.0003
0.00108
0.0007
0.002282
10
3
10

TRIMETHYLLYSINE
N6,N6,N6-Trimethyllysine
0.001
0.004
0.0071
0.00828
0.0075
0.008624
3
25
57

α-AMINOADIPIC ACID
alpha-Aminoadipate
0.073
0.056
0.089
0.05484
0.095
0.065314
17
34
83

SPERMINE
Spermine
0.004
0.007
0.0207
0.02522
0.0199
0.030295
6
28
60

N1-ACETYLSPERMIDINE
N1-Acetylspermidine
0.025
0.026
0.0386
0.0295
0.0425
0.032472
15
35
87

CREATINE
Creatine
1.034
0.560
0.9723
0.44164
1.0219
0.578194
20
37
90

γ-BUTYROBETAINE
gamma-Butyrobetaine
0.154
0.089
0.2152
0.14508
0.232
0.161565
20
36
89

SARCOSINE
Sarcosine
0.416
0.199
0.4475
0.25949
0.4453
0.284517
20
37
90

PYRUVIC ACID
Pyruvate
3.853
2.269
4.1335
2.94749
4.6006
3.016964
20
37
90

UROCANIC ACID
Urocanate
0.070
0.083
0.1517
0.19424
0.1848
0.27465
13
32
82

PIPERIDINE
Piperidine
0.006
0.011
0.0159
0.02094
0.0348
0.107037
7
25
67

SERINE
Ser
0.708
0.279
0.9755
0.86402
1.0627
1.0582
20
37
90

HOMOVANILLIC ACID
Homovanillate
0.155
0.073
0.143
0.07656
0.1857
0.14732
20
37
88

5-OXOPROLINE
5-Oxoproline
0.568
0.887
0.556
0.62212
0.7212
1.230777
20
37
90

GABA
GABA
0.100
0.046
0.1004
0.06762
0.1752
0.460201
20
36
89

5-AMINOVALERIC ACID
5-Aminovalerate
13.413
11.107
23.718
19.2024
27.708
23.34253
20
37
89

TRIMETHYLAMINE-N-OXIDE
Trimethylamine N-oxide
0.014
0.030
0.0244
0.03508
0.0222
0.031137
4
24
61

2-HYDROXYVALERIC ACID
2-Hydroxypentanoate
0.503
0.284
0.5272
0.35447
0.6226
0.481419
20
37
90

CARNITINE
Carnitine
0.064
0.028
0.0598
0.0358
0.0589
0.030769
20
37
90

ISOPROPANOLAMINE
Isopropanolamine
0.040
0.053
0.061
0.07286
0.0734
0.084822
12
31
77

THREONINE
Thr
0.286
0.085
0.3742
0.24147
0.4102
0.288762
20
37
90

HYPOTAURINE
Hypotaurine
0.007
0.020
0.0487
0.06827
0.05
0.072847
2
19
42

LACTIC ACID
Lactate
13.476
7.556
13.847
10.6027
15.565
17.77329
20
37
90

2-HYDROXY-4-METHYL-
2-Hydroxy-4-methylpentanoate
0.144
0.093
0.1792
0.1286
0.2112
0.174675
20
36
89

PENTANOIC ACID

HYDROXYPROLINE
Hydroxyproline
0.025
0.041
0.048
0.08291
0.0459
0.059641
9
31
75

ALANINE
Ala
1.408
0.577
1.7516
0.78559
2.0262
1.3785
20
37
90

MANN-WHITNEY TEST

ONLY
INCLUDING

BEFORE
DURING

TREATMENT
TREATMENT

vs HEALTHY
vs HEALTHY

SUBJECT
SUBJECT
PUBLICLY

NAME OF SUBSTANCE
P VALUE
Q VALUE
P VALUE
Q VALUE
KNOWN
SIGNIFICANT

CHOLINE
Choline
0.20875
0.42434
0.042705
0.129157

1

2-HYDROXYBUTYRIC ACID
2-Hydroxybutyrate
0.7955
0.90497
0.695525
0.821382

0

β-ALANINE
beta-Ala
0.02613
0.12032
0.010775
0.049363

1

3-METHYLHISDINE
3-Methylhistidine
0.00022
0.00989
2.52E−05
0.001565

1

α-AMINOBUTYRIC ACID
2AB
0.00841
0.0652
0.001245
0.02205

1

CADAVERINE
Cadaverine
0.00496
0.05
0.001936
0.025253
78
1

N-ACETYL-β-ALANINE
N-Acetyl-beta-alanine
0.01176
0.07673
0.003595
0.03184

1

ISETHIONIC ACID
Isethionate
0.88658
0.93166
0.891843
0.929316

0

N-ACETYLPHENYLALANINE
N-Acetylphenylalanine
0.00017
0.00989
2.41E−05
0.001565

1

TRIMETHYLLYSINE
N6,N6,N6-Trimethyllysine
0.00045
0.01366
0.000233
0.009643

1

α-AMINOADIPIC ACID
alpha-Aminoadipate
0.2996
0.53842
0.182378
0.337535

0

SPERMINE
Spermine
0.00077
0.01585
0.001713
0.025253
8
1

N1-ACETYLSPERMIDINE
N1-Acetylspermidine
0.07745
0.20434
0.019641
0.073801
8
1

CREATINE
Creatine
0.8749
0.92725
0.741877
0.824197

0

γ-BUTYROBETAINE
gamma-Butyrobetaine
0.15403
0.34726
0.076587
0.202061

0

SARCOSINE
Sarcosine
0.78449
0.90322
0.990725
0.990725

0

PYRUVIC ACID
Pyruvate
1
1
0.459233
0.618967

0

UROCANIC ACID
Urocanate
0.05766
0.17023
0.004244
0.035084

1

PIPERIDINE
Piperidine
0.02259
0.11672
0.001153
0.02205

1

SERINE
Ser
0.39241
0.63739
0.152761
0.315079

0

HOMOVANILLIC ACID
Homovanillate
0.63652
0.81967
0.97836
0.986315

0

5-OXOPROLINE
5-Oxoproline
0.81009
0.91124
0.249793
0.407335

0

GABA
GABA
0.66039
0.8356
0.650285
0.77534

0

5-AMINOVALERIC ACID
5-Aminovalerate
0.03978
0.13186
0.008131
0.049363

1

TRIMETHYLAMINE-N-OXIDE
Trimethylamine N-oxide
0.01743
0.09637
0.006256
0.0431

1

2-HYDROXYVALERIC ACID
2-Hydroxypentanoate
0.91422
0.95264
0.579501
0.717992

0

CARNITINE
Carnitine
0.32275
0.56367
0.29367
0.444087

0

ISOPROPANOLAMINE
Isopropanolamine
0.1805
0.37303
0.038945
0.120731

1

THREONINE
Thr
0.40171
0.63862
0.086054
0.213453
7
0

HYPOTAURINE
Hypotaurine
0.00265
0.04689
0.002568
0.028374

1

LACTIC ACID
Lactate
0.75912
0.90322
0.736028
0.824197

0

2-HYDROXY-4-METHYL-
2-Hydroxy-4-methylpentanoate
0.48063
0.70115
0.168946
0.331163

0

PENTANOIC ACID

HYDROXYPROLINE
Hydroxyproline
0.04115
0.13186
0.010739
0.049363

1

ALANINE
Ala
0.08353
0.2115
0.012718
0.053624
7
1

TABLE 8-2

RELATIVE CONCENTRATION (NO UNIT)

DETECTION

BREAST CANCER

RATIO (%)

ONLY BEFORE
INCLUDING DURING

BREAST CANCER

HEALTHY SUBJECT
TREATMENT
TREATMENT

INCLUDING

STANDARD

STANDARD

STANDARD
HEALTHY
BEFORE
DURING

NAME OF SUBSTANCE
AVERAGE
DEVIATION
AVERAGE
DEVIATION
AVERAGE
DEVIATION
SUBJECT
TREATMENT
TREATMENT

VALINE
Val
0.655
0.505
0.92
0.4944
1.085
0.7477
20
37
90

BUTYRIC ACID
Butanoate
5.412
4.454
8.34
9.2551
10.66
12.991
20
37
89

SPERMIDINE
Spermidine
0.106
0.082
0.12
0.0928
0.12
0.0849
20
36
88

N8-ACETYLSPERMIDINE
N8-Acetylspermidine
0.001
0.001
0
0.0025
0.002
0.003
3
20
49

ADENINE
Adenine
0.055
0.046
0.05
0.0394
0.058
0.0424
16
34
81

PUTRESCINE
Putrescine(1,4-Butanediamine)
2.585
2.076
3.86
2.4115
4.412
3.3521
20
37
89

N6-ACETYLLYSINE
N-epsilon-Acetyllysine
0.004
0.009
0.01
0.0113
0.01
0.0126
4
22
51

6-HYDROXYHEXANOIC
6-Hydroxyhexanoate
0.103
0.142
0.01
0.0439
0.027
0.0732
9
5
19

ACID

PROPIONIC ACID
Propionate
15.110
13.467
19.6
16.982
21.9
19.007
20
37
89

BETAINE
Betaine
0.235
0.161
0.21
0.1575
0.367
0.9139
18
36
88

N-ACETYLPUTRESCINE
N-Acetylputrescine
0.140
0.098
0.2
0.1397
0.227
0.1674
20
36
89

GLYCINE
Gly
3.852
1.481
4.95
2.8102
5.317
3.6606
20
37
90

HYPOXANTHINE
Hypoxanthine
0.255
0.144
0.27
0.2042
0.279
0.2112
19
35
85

LEUCINE
Leu
0.551
0.355
0.69
0.3443
0.823
0.5284
20
37
90

CROTONIC ACID
Crotonate
0.323
0.408
0.77
1.2057
0.785
1.1358
13
32
75

ORNITHINE
Ornithine
1.004
0.517
1.34
1.0714
1.364
1.0031
20
37
90

ISOLEUCINE
Ile
0.187
0.134
0.25
0.1419
0.304
0.2163
20
37
90

TRYPTOPHAN
Trp
0.063
0.054
0.07
0.0479
0.08
0.0572
16
36
88

CITRULLINE
Citrulline
0.631
0.477
0.66
0.4836
0.711
0.4288
20
37
90

GLUTAMINE
Gln
1.000
0.000
1
0
1
0
20
37
90

N1-,N8-DIACETYLSPERMIDINE
N1,N8-Diacetylspermidine
0.006
0.005
0.01
0.0074
0.008
0.008
15
32
76

PROLINE
Pro
2.119
0.813
3.11
3.2776
3.232
3.1517
20
37
90

2-OXOISOPENTANOIC
2-Oxoisopentanoate
0.082
0.058
0.06
0.0537
0.066
0.0518
17
34
81

ACID

GLUTAMIC ACID
Glu
2.005
0.996
1.91
1.0563
2.005
1.1163
20
37
90

4-METHYLBENZOATE
4-Methylbenzoate
1.109
1.178
1.19
1.201
1.376
1.3512
20
35
87

3-(4-HYDROXYPHENYL)
3-(4-Hydroxyphenyl)propionate
0.456
0.466
0.67
0.8923
0.711
0.7999
19
35
86

PROPIONIC ACID

CYSTEIC ACID
Cysteate
0.014
0.014
0.01
0.0115
0.011
0.0118
13
27
60

AZELAIC ACID
Azelate
0.003
0.010
0
0.0096
0.006
0.0124
2
15
39

RIBULOSE-5-PHOSPHORIC
Ru5P
0.292
0.160
0.23
0.1685
0.245
0.1554
20
36
88

ACID

PICOLINIC ACID
Pipecolate
0.060
0.109
0.03
0.0234
0.037
0.0289
17
34
83

PHENYLALANINE
Phe
1.085
0.645
0.88
0.4717
1.039
0.652
20
37
90

MANN-WHITNEY TEST

ONLY
INCLUDING

BEFORE
DURING

TREATMENT
TREATMENT

vs HEALTHY
vs HEALTHY

SUBJECT
SUBJECT
PUBLICLY

NAME OF SUBSTANCE
P VALUE
Q VALUE
P VALUE
Q VALUE
KNOWN
SIGNIFICANT

VALINE
Val
0.01043
0.0719
0.0009
0.022
7
1

BUTYRIC ACID
Butanoate
0.53387
0.7523
0.10781
0.2476

0

SPERMIDINE
Spermidine
0.54486
0.7532
0.43606
0.6075
8
0

N8-ACETYLSPERMIDINE
N8-Acetylspermidine
0.00706
0.0584
0.00275
0.0284
8
1

ADENINE
Adenine
1
1
0.72416
0.8242

0

PUTRESCINE
Putrescine(1,4-Butanediamine)
0.03978
0.1319
0.01115
0.0494
78
1

N6-ACETYLLYSINE
N-epsilon-Acetyllysine
0.01409
0.0832
0.00812
0.0494

1

6-HYDROXYHEXANOIC
6-Hydroxyhexanoate
0.00361
0.05
0.00982
0.0494

1

ACID

PROPIONIC ACID
Propionate
0.3653
0.6205
0.12208
0.2703

0

BETAINE
Betaine
0.4568
0.6824
0.86766
0.9209

0

N-ACETYLPUTRESCINE
N-Acetylputrescine
0.11859
0.2723
0.02693
0.092

1

GLYCINE
Gly
0.17447
0.373
0.04598
0.1357
7
1

HYPOXANTHINE
Hypoxanthine
0.83443
0.9112
0.95673
0.9724

0

LEUCINE
Leu
0.05291
0.164
0.00521
0.0404
7
1

CROTONIC ACID
Crotonate
0.08358
0.2115
0.03365
0.107

1

ORNITHINE
Ornithine
0.48063
0.7012
0.23726
0.3974
78
0

ISOLEUCINE
Ile
0.04147
0.1319
0.00327
0.0312
7
1

TRYPTOPHAN
Trp
0.4367
0.6704
0.16891
0.3312

0

CITRULLINE
Citrulline
0.69684
0.8555
0.20512
0.3634

0

GLUTAMINE
Gln
NA
NA
NA
NA

0

N1-,N8-DIACETYLSPERMIDINE
N1,N8-Diacetylspermidine
0.56865
0.7664
0.43032
0.6064
8
0

PROLINE
Pro
0.77177
0.9032
0.253
0.4073

0

2-OXOISOPENTANOIC
2-Oxoisopentanoate
0.29187
0.535
0.22048
0.3797

0

ACID

GLUTAMIC ACID
Glu
0.68461
0.8518
0.86767
0.9209
7
0

4-METHYLBENZOATE
4-Methylbenzoate
0.71922
0.8743
0.34639
0.5053

0

3-(4-HYDROXYPHENYL)
3-(4-Hydroxyphenyl)propionate
0.55276
0.7532
0.17378
0.3312

0

PROPIONIC ACID

CYSTEIC ACID
Cysteate
0.64119
0.8197
0.5094
0.6512

0

AZELAIC ACID
Azelate
0.03779
0.1319
0.0165
0.0639

1

RIBULOSE-5-PHOSPHORIC
Ru5P
0.15896
0.352
0.25623
0.4073

0

ACID

PICOLINIC ACID
Pipecolate
0.41234
0.6472
0.72137
0.8242

0

PHENYLALANINE
Phe
0.24078
0.4665
0.58482
0.718

0

TABLE 8-3

RELATIVE CONCENTRATION (NO UNIT)

DETECTION

BREAST CANCER

RATIO (%)

ONLY BEFORE
INCLUDING DURING

BREAST

HEALTHY SUBJECT
TREATMENT
TREATMENT

CANCER

STANDARD

STANDARD

STANDARD
HEALTHY
BEFORE

NAME OF SUBSTANCE
AVERAGE
DEVIATION
AVERAGE
DEVIATION
AVERAGE
DEVIATION
SUBJECT
TREATMENT

O-PHOSPHOSERINE
O-Phosphoserine
0.045
0.060
0.045
0.0378
0.0488
0.03918
14
35

MALONIC ACID
Malonate
0.0721
0.05367
0.046
0.0335
0.0578
0.04882
18
36

HEXANOIC ACID
Hexanoate
1.1716
1.30695
1.794
2.2027
2.0692
2.44959
20
37

3-PHOSPHOGLYCERIC ACID
3PG
0.2068
0.12486
0.164
0.1395
0.1728
0.13155
20
36

N-ACETYLGLUTAMIC ACID
N-Acetylglutamate
0.0101
0.00855
0.01
0.008
0.0107
0.00868
15
30

N-ACETYLGLUCOSAMINE-6-PHOSPHORIC ACID
N-Acetylglucosamine 6-phosphate
0.0135
0.01997
0.012
0.0129
0.0121
0.01325
10
25

2-OXOBUTYRIC ACID
2-Oxobutyrate
0.3398
0.32933
0.288
0.3022
0.2977
0.31856
17
33

GLYCYL-GLYCINE
Glu-Glu
0.007
0.01345
0.011
0.0141
0.0119
0.01543
6
23

LYSINE
Lys
2.37
1.17409
2.109
1.2091
2.2092
1.19698
20
36

ASPARTIC ACID
Asp
1.2001
0.49598
0.926
0.6067
1.0226
0.60552
20
37

METHIONINE
Met
0.063
0.07532
0.073
0.073
0.0827
0.10958
12
31

P-HYDROXYPHENYLACETIC ACID
p-Hydroxyphenylacetate
0.8015
1.44237
0.77
0.9516
0.8143
0.8509
12
29

AGMATINE
Agmatine
0.0072
0.00696
0.007
0.0084
0.0072
0.00815
13
28

2-DEOXYRIBOSE 1-PHOSPHORIC ACID
2-Deoxyribose 1-phosphate
0.0246
0.04973
0.022
0.0305
0.0235
0.03225
10
26

PEPLOMYCIN
PEP
0.0429
0.03478
0.03
0.0222
0.0328
0.02543
18
36

DIHYDROXYACETONEPHOSPHORIC ACID
DHAP
0.6886
0.3374
0.445
0.3238
0.5023
0.36005
20
37

GLYCOLIC ACID
Glycolate
1.0617
0.75961
0.677
0.6006
0.7289
0.69866
20
36

HISTAMINE
Histamine
0.0236
0.04078
0.02
0.035
0.0194
0.02611
13
30

N-ACETYLLEUCINE
N-Acetylleucine
0.0018
0.0034
0.002
0.0029
0.0015
0.00294
6
16

CYTIDINE DISODIUM 5′-MONOPHOSPHATE
CMP
0.0228
0.0348
0.031
0.0476
0.0282
0.04103
10
21

GUANINE
Guanine
0.0706
0.07096
0.061
0.0505
0.0678
0.0594
14
32

4-METHYL-2-OXOPENTANOIC ACID
4-Methyl-2-oxopentanoate
0.1425
0.07182
0.102
0.075
0.1129
0.07547
20
37

N-ACETYLASPARTIC ACID
N-Acetylaspartate
0.0622
0.03537
0.046
0.0434
0.0558
0.05725
20
37

TYROSINE
Tyr
1.5902
0.72068
1.093
0.6385
1.165
0.61484
20
37

SUCCINIC ACID
Succinate
1.61
1.54845
1.123
0.7861
1.6525
2.61266
20
37

GLYCEROPHOSPHORIC ACID
Glycerophosphate
0.7889
0.54774
0.522
0.4592
0.6148
0.53233
20
37

ALANYL-ALANINE
Ala-Ala
0.0498
0.0542
0.035
0.0332
0.0388
0.03904
13
27

1,3-DIAMINOPROPANE
1,3-Diaminopropane
0.1091
0.12897
0.096
0.107
0.097
0.10061
12
29

3-PHENYLPROPIONIC ACID
3-Phenylpropionate
0.9437
1.09581
1.178
1.659
1.395
1.91697
20
31

CIS-ACONITATE
cis-Aconitate
0.0175
0.01899
0.013
0.0169
0.0212
0.06004
12
30

DETECTION
MANN-WHITNEY TEST

RATIO (%)
ONLY BEFORE
INCLUDING

BREAST
TREATMENT
DURING

CANCER
vs HEALTHY
TREATMENT vs

INCLUDING DURING
SUBJECT
HEALTHY SUBJECT
PUBLICLY

NAME OF SUBSTANCE
TREATMENT
P VALUE
Q VALUE
P VALUE
Q VALUE
KNOWN
SIGNIFICANT

O-PHOSPHOSERINE
O-Phosphoserine
84
0.2802
0.52635
0.17599
0.3312

0

MALONIC ACID
Malonate
88
0.0961
0.23376
0.24034
0.3974

0

HEXANOIC ACID
Hexanoate
89
0.7845
0.90322
0.17626
0.3312

0

3-PHOSPHOGLYCERIC ACID
3PG
89
0.0898
0.22266
0.155
0.3151

0

N-ACETYLGLUTAMIC ACID
N-Acetylglutamate
78
0.9732
0.99073
0.74142
0.8242

0

N-ACETYLGLUCOSAMINE-6-PHOSPHORIC ACID
N-Acetylglucosamine 6-phosphate
58
0.5296
0.75227
0.57941
0.718

0

2-OXOBUTYRIC ACID
2-Oxobutyrate
76
0.5524
0.75321
0.48712
0.6406

0

GLYCYL-GLYCINE
Glu-Glu
52
0.0764
0.20434
0.05891
0.1588

0

LYSINE
Lys
89
0.3653
0.62051
0.62264
0.7496

0

ASPARTIC ACID
Asp
90
0.0295
0.1214
0.09959
0.233
7
1

METHIONINE
Met
75
0.3856
0.63739
0.3171
0.4737

0

P-HYDROXYPHENYLACETIC ACID
p-Hydroxyphenylacetate
75
0.3063
0.54264
0.128
0.2784

0

AGMATINE
Agmatine
66
0.8525
0.91124
0.87852
0.9232

0

2-DEOXYRIBOSE 1-PHOSPHORIC ACID
2-Deoxyribose 1-phosphate
63
0.3958
0.63739
0.27727
0.428

0

PEPLOMYCIN
PEP
85
0.1626
0.35379
0.20506
0.3634

0

DIHYDROXYACETONEPHOSPHORIC ACID
DHAP
90
0.0052
0.05
0.01043
0.0494

1

GLYCOLIC ACID
Glycolate
85
0.0336
0.13014
0.02913
0.0951

1

HISTAMINE
Histamine
77
0.8398
0.91124
0.42786
0.6064

0

N-ACETYLLEUCINE
N-Acetylleucine
28
0.4863
0.70123
0.91331
0.936

0

CYTIDINE DISODIUM 5′-MONOPHOSPHATE
CMP
54
0.6165
0.8132
0.50663
0.6512

0

GUANINE
Guanine
78
0.9532
0.98494
0.79472
0.8644

0

4-METHYL-2-OXOPENTANOIC ACID
4-Methyl-2-oxopentanoate
90
0.0258
0.12032
0.04857
0.1401

1

N-ACETYLASPARTIC ACID
N-Acetylaspartate
90
0.027
0.12032
0.08465
0.2135

1

TYROSINE
Tyr
90
0.004
0.05
0.00997
0.0494
7
1

SUCCINIC ACID
Succinate
90
0.2212
0.43533
0.45454
0.619

0

GLYCEROPHOSPHORIC ACID
Glycerophosphate
90
0.0179
0.09637
0.05316
0.1495

1

ALANYL-ALANINE
Ala-Ala
62
0.5935
0.79179
0.62247
0.7496

0

1,3-DIAMINOPROPANE
1,3-Diaminopropane
71
0.8458
0.91124
0.77566
0.8512
8
0

3-PHENYLPROPIONIC ACID
3-Phenylpropionate
80
0.7316
0.88078
0.71847
0.8242

0

CIS-ACONITATE
cis-Aconitate
68
0.8461
0.91124
0.90341
0.9335

0

TABLE 8-4

RELATIVE CONCENTRATION (NO UNIT)

DETECTION

BREAST CANCER

RATIO (%)

ONLY BEFORE
INCLUDING DURING

BREAST

HEALTHY SUBJECT
TREATMENT
TREATMENT

CANCER

STANDARD

STANDARD

STANDARD
HEALTHY
BEFORE

NAME OF SUBSTANCE
AVERAGE
DEVIATION
AVERAGE
DEVIATION
AVERAGE
DEVIATION
SUBJECT
TREATMENT

3-HYDROXYBUTYRIC ACID
3-Hydroxybutyrate
0.647
0.52696
0.369
0.2321
0.4759
0.39905
20
37

BENZOIC ACID
Benzoate
1.332
0.9986
0.799
0.8965
0.8895
1.04938
18
27

GUANOSINE
Guanosine
0.024
0.03472
0.015
0.0197
0.0129
0.0182
10
19

6-PHOSPHOGLUCONIC ACID
6-Phosphogluconate
0.036
0.03115
0.031
0.0485
0.0288
0.03903
16
33

URIDYLIC ACID
UMP
0.012
0.01873
0.01
0.0136
0.0122
0.01936
8
18

ADENOSINE
Adenosine
0.019
0.02147
0.013
0.0212
0.0115
0.01764
11
22

MUCIC ACID
Mucate
0.032
0.04565
0.018
0.0317
0.0178
0.02731
11
18

ETHANOLAMINEPHOSPHORIC ACID
Ethanolamine phosphate
4.256
4.75927
3.189
3.9208
3.5534
4.83037
18
34

ADIPATE
Adipate
0.052
0.04601
0.03
0.0276
0.0385
0.04267
19
35

2-ISOPROPYLMALATE
2-Isopropylmalate
0.025
0.0254
0.014
0.013
0.0175
0.02347
20
36

PHOSPHORYLCHLORINE
Phosphorylcholine
0.547
0.35158
0.261
0.2648
0.3764
0.3664
20
31

NICOTINATE
Nicotinate
0.157
0.12343
0.098
0.1071
0.1272
0.13348
16
23

1-METHYL-2-PYRROLIDINONE
1-Methyl-2-pyrrolidinone
2.577
2.74352
1.798
2.1837
1.739
1.95496
20
36

MALIC ACID
Malate
0.181
0.11191
0.132
0.131
0.1531
0.13135
20
37

PANTOTHENIC ACID
Pantothenate
0.007
0.01177
0.004
0.006
0.007
0.01098
7
14

N-ACETYLNEURAMINATE
N-Acetylneuraminate
4.326
3.32649
2.13
1.8598
2.3366
1.94534
20
37

HISTAMINE
His
0.914
0.35757
0.542
0.309
0:609
0.33134
20
37

FUMARIC ACID
Fumarate
0.042
0.04031
0.03
0.0321
0.0327
0.03294
15
30

2-DEOXYGLUCOSE-6-PHOSPHORIC ACID
2-Deoxyglucose 6-phosphate
0.016
0.03298
0.01
0.0192
0.0093
0.01812
5
11

CITRIC ACID
Citrate
0.636
0.62144
0.449
0.672
0.5399
0.79022
20
35

4-ACETYLBUTYRIC ACID
4-Acetylbutyrate
0.013
0.02228
0.007
0.0149
0.0051
0.01133
6
11

O-ACETYLCARNITINE
o-Acetylcarnitine
0.092
0.0607
0.057
0.0544
0.0588
0.04852
20
37

N-ACETYLGLUCOSAMINE-1-PHOSPHORIC ACID
N-Acetylglucosamine 1-phosphate
0.021
0.01561
0.01
0.0111
0.0103
0.01125
17
26

SEDOHEPTULOSE-7-PHOSPHORIC ACID
S7P
0.038
0.0291
0.024
0.0242
0.0263
0.02339
16
29

HEPTANOIC ACID
Heptanoate
0.032
0.03981
0.015
0.0218
0.0165
0.02305
15
25

CREATININE
Creatinine
0.364
0.23931
0.221
0.2029
0.2462
0.22261
20
37

2,5-DIHYDROXYBENZONATE
2,5-Dihydroxybenzoate
0.04
0.05452
0.021
0.0352
0.0334
0.07638
9
17

CYTIDINE
Cytidine
0.037
0.4016
0.022
0.0278
0.0192
0.02594
14
22

ARGININE
Arg
1.147
0.61391
0.775
0.7669
0.8378
0.72021
20
37

SYRINGIC ACID
Syringate
0.13
0.08544
0.085
0.0942
0.0843
0.08747
20
34

DETECTION
MANN-WHITNEY TEST

RATIO (%)
ONLY BEFORE
INCLUDING

BREAST
TREATMENT
DURING

CANCER
vs HEALTHY
TREATMENT vs

INCLUDING DURING
SUBJECT
HEALTHY SUBJECT
PUBLICLY

NAME OF SUBSTANCE
TREATMENT
P VALUE
Q VALUE
P VALUE
Q VALUE
KNOWN
SIGNIFICANT

3-HYDROXYBUTYRIC ACID
3-Hydroxybutyrate
90
0.037
0.1319
0.14
0.294

1

BENZOIC ACID
Benzoate
67
0.027
0.1203
0.024
0.085

1

GUANOSINE
Guanosine
42
0.624
0.8149
0.345
0.505

0

6-PHOSPHOGLUCONIC ACID
6-Phosphogluconate
77
0.258
0.4929
0.227
0.386

0

URIDYLIC ACID
UMP
42
0.848
0.9112
0.744
0.824

0

ADENOSINE
Adenosine
49
0.455
0.6824
0.272
0.427

0

MUCIC ACID
Mucate
49
0.293
0.535
0.358
0.516

0

ETHANOLAMINEPHOSPHORIC ACID
Ethanolamine phosphate
84
0.219
0.4353
0.28
0.428

0

ADIPATE
Adipate
87
0.03
0.1214
0.093
0.221

1

2-ISOPROPYLMALATE
2-Isopropylmalate
87
0.029
0.1214
0.02
0.074

1

PHOSPHORYLCHLORINE
Phosphorylcholine
81
6E−04
0.0137
0.013
0.054

1

NICOTINATE
Nicotinate
63
0.068
0.1926
0.211
0.369

0

1-METHYL-2-PYRROLIDINONE
1-Methyl-2-pyrrolidinone
87
0.107
0.2553
0.092
0.221

0

MALIC ACID
Malate
90
0.057
0.1702
0.189
0.345

0

PANTOTHENIC ACID
Pantothenate
42
0.787
0.9032
0.549
0.694

0

N-ACETYLNEURAMINATE
N-Acetylneuraminate
90
0.005
0.05
0.006
0.043

1

HISTAMINE
His
90
2E−04
0.0099
9E−04
0.022

1

FUMARIC ACID
Fumarate
72
0.392
0.6374
0.491
0.641

0

2-DEOXYGLUCOSE-6-PHOSPHORIC ACID
2-Deoxyglucose 6-phosphate
24
0.975
0.9907
0.869
0.921

0

CITRIC ACID
Citrate
86
0.075
0.2043
0.138
0.294

0

4-ACETYLBUTYRIC ACID
4-Acetylbutyrate
25
0.687
0.8518
0.449
0.619

0

O-ACETYLCARNITINE
o-Acetylcarnitine
89
0.01
0.0719
0.009
0.049

1

N-ACETYLGLUCOSAMINE-1-PHOSPHORIC ACID
N-Acetylglucosamine 1-phosphate
63
0.006
0.0508
0.002
0.025

1

SEDOHEPTULOSE-7-PHOSPHORIC ACID
S7P
72
0.068
0.1926
0.112
0.252

0

HEPTANOIC ACID
Heptanoate
56
0.117
0.2723
0.086
0.213

0

CREATININE
Creatinine
90
0.005
0.05
0.01
0.049

1

2,5-DIHYDROXYBENZONATE
2,5-Dihydroxybenzoate
38
0.438
0.6704
0.483
0.641

0

CYTIDINE
Cytidine
49
0.178
0.373
0.054
0.149

0

ARGININE
Arg
90
0.013
0.083
0.027
0.092

1

SYRINGIC ACID
Syringate
82
0.039
0.1319
0.015
0.062

1

Even when the whole concentration of saliva of elderly patient is increased, using glutamine that is a substance that correlates with the most metabolites and can be detected in all of the samples as a concentration-correcting marker makes it possible to distinguish a subject with cancer from a healthy subject by eliminating the influence of concentration variations by this method. On the other hand, people equal to or older than 70 years of age have a trend of increasing the whole concentration of saliva. Therefore, when the absolute concentration is used, the construction of a model using only data of people less than 70 years of age leads to a highly accurate separation.

A substance belonging to polyamines among substances that give a significant difference between the healthy subjects (C, n=20) and the patients with breast cancer (BC, all the cases including before treatment, n=90) is shown in FIG. 13.

Examples (the top five substances with a smaller P value) of substances other than polyamines among the substances that give a significant difference between the healthy subjects (C, n=20) and the patients with breast cancer (BC, all the cases including before treatment, n=90) are shown in FIG. 14.

Substances that give a significant difference (p<0.05) between the healthy subjects (C, 20 cases) and the patients with breast cancer (BC, 90 cases) regardless of the presence or absence of concentration correction are shown in FIG. 15. A network diagram (shown in FIG. 16) was formed using all the cases of all the samples (20 cases of C and 90 cases of BC). A concentration correction substance, or Gly (glycine, expressed in o in the drawing) was determined. When concentration correction with this concentration correction marker was not performed, 73 substances exhibited a significant difference. When concentration correction was performed (the concentration of metabolite of interest was divided by the concentration of Gly), 35 substances exhibited a significant difference. Among the substances, 11 substances exhibited a significant difference regardless of the presence or absence of concentration correction. The top five substances that had a smaller P value are shown. An ROC curve at which an MLR model was formed using two substances of spermine and 6-hydaroxyhexanoate is shown in the lower right.

Next, a biomarker for oral cancer will be described.

Substances in which the absolute concentrations of metabolites in saliva exhibit a difference between subjects with oral cancer and healthy subjects are shown in Tables 9-1 and 9-2. The healthy subjects were 20 cases, and patients with breast cancer were 20 cases. For the healthy subjects, saliva was collected 1.5 hours after eating, and for the patients with cancer, saliva was collected two times, before eating (in a fasting state from the previous night) and 1.5 hours after eating. By comparing either of them, a P value was calculated using the Mann-Whitney test, and a Q value was calculated using the false discovery rate (FDR). Substances of Q<0.05 were listed.

TABLE 9-1

TEST (COMPARISON

HEALTHY

WITH HEALTHY SUBJECTS)

SUBJECT
ORAL CANCER
CANCER
CANCER

1.5 HOURS
1.5 HOURS
ON EMPTY
(1.5 HOURS/
(ON EMPTY/

AFTER DIET
AFTER DIET
STOMACH
AFTER DIET)
STOMACH)
PUBLICLY

COMPOUND
NAME OF SUBSTANCE
AVERAGE
S.D.
AVERAGE
S.D.
AVERAGE
S.D.
P-value
Q-value
P-value
Q-value
KNOWN

Gly-Gly
GLYCYL-GLYCINE
0.4069
0.9342
1.058
1.70904
2.6807
3.3424
0.01231
0.08136
7.3E−05
0.0028

Choline
CHOLINE
9.8191
13.615
16.57
10.5053
23.982
18.487
0.00196
0.03719
9.5E−05
0.0029
9

Citrulline
CITRULLINE
15.509
17.115
32.87
33.9328
58.026
59.93
0.04298
0.16753
0.00013
0.0032

gamma-Butyrobetaine
γ-BUTYROBETAINE
3.0834
3.8972
5.898
5.71459
8.116
5.7123
0.01809
0.10186
0.00015
0.0032

3-Phenyllactate
3-PHENYLLACTATE
1.7552
3.8247
2.227
2.43789
4.436
5.4298
0.15394
0.2945
0.00024
0.004

Butanoate
BUTYRIC ACID
63.135
53.789
368.9
572.732
276.63
290.83
0.00195
0.03719
0.00027
0.004

Hexanoate
HEXANOIC ACID
13.695
13.216
38.52
65.1854
71.359
106.92
0.34078
0.45043
0.0003
0.004

Met
METHIONINE
1.372
3.3692
2.48
4.46552
7.1068
10.922
0.12541
0.28034
0.00032
0.004

Hypoxanthine
HYPOXANTHINE
5.4485
8.9829
12.46
16.7868
18.403
21.808
0.0239
0.11717
0.00034
0.004

Spermidine
SPERMIDINE
1.813
1.7732
3.705
5.16895
4.6425
2.7304
0.31408
0.42626
0.00043
0.0042

Val
VALINE
13.282
20.557
27.63
47.2029
47.09
63.858
0.1207
0.27586
0.00044
0.0042
7

Glu
GLUTAMIC ACID
43.09
90.355
54.12
65.7928
93.418
88.182
0.31408
0.42626
0.00044
0.0042
7

Trp
TRYPTOPHAN
1.6971
2.9918
2.367
2.83711
5.3687
6.4
0.10745
0.26082
0.00048
0.0042

Ala
ALANINE
40.573
68.685
73.86
78.6906
137.25
145.91
0.00672
0.06109
0.0005
0.0042
7

Asp
ASPARTIC ACID
19
30.334
27.79
28.8413
45.057
41.162
0.1207
0.27586
0.00104
0.0083

Piperidine
PIPERIDINE
0.1498
0.2574
1.321
2.36202
1.3152
2.5325
0.00501
0.06109
0.00121
0.0083
7

Isopropanolamine
ISOPROPANOLAMINE
0.6591
0.5352
1.688
1.72126
1.5766
0.9826
0.06715
0.20118
0.00121
0.0083

Ala-Ala
ALANYL-ALANINE
0.8831
1.1716
1.732
2.46196
2.8654
3.1707
0.37911
0.48424
0.00123
0.0083

N,N-Dimethylglycine
N,N-DIMETHYLGLYCINE
0.2266
0.5635
0.43
0.47283
0.5607
0.3983
0.00596
0.06109
0.00126
0.0083

N1-Acetylspermidine
N1-ACETYLSPERMIDINE
0.7048
0.9459
1.337
1.27463
2.0556
2.4166
0.01327
0.08387
0.00143
0.0087

N1,N8-Diacetylspermidine
N1-,N8-DIACETYLSPERMIDINE
0.5098
1.6679
0.336
0.33971
0.5473
0.5625
0.03538
0.14152
0.00145
0.0087

N8-Acetylspermidine
N8-ACETYLSPERMIDINE
0.0524
0.0882
0.111
0.1197
0.1356
0.1038
0.06919
0.20192
0.00148
0.0087

2AB
α-AMINOBUTYRIC ACID
1.6132
1.5683
3.175
4.32823
6.9715
13.599
0.14171
0.2945
0.00166
0.0088

Trimethylamine N-oxide
TRIMETHYLAMINE-N-OXIDE
0.1483
0.2186
0.596
0.6416
0.8705
1.2581
0.00337
0.05691
0.00175
0.0088

N-Acetylaspartate
N-ACETYLASPARTIC ACID
0.9277
0.817
2.256
2.45157
2.2515
1.7815
0.01435
0.08387
0.00185
0.0088

Adenine
ADENINE
0.6068
0.6117
0.845
0.73556
1.2708
0.8954
0.06377
0.20118
0.00185
0.0088

Thr
THREONINE
7.3662
12.091
12.76
14.0435
19.827
18.913
0.02831
0.12656
0.00185
0.0088
7

2-Hydroxypentanoate
2-HYDROXIVALERIC ACID
10.065
22.066
12.13
13.4737
16.866
20.227
0.08023
0.21396
0.00193
0.0089

Putrescine(1,4-Butanediamine)
PUTRESCINE
55.793
65.975
139.1
212.235
140.18
122.54
0.04909
0.18199
0.00207
0.009

Ile
ISOLEUCINE
5.4605
9.7987
10.74
19.3536
17.599
23.355
0.0675
0.20118
0.00207
0.009
7

3PG
3-PHOSPHOGLYCERIC ACID BARIUM SALT
2.9479
4.4668
5.387
4.81451
5.5895
3.7831
0.00733
0.06109
0.00231
0.0092

Gln
GLUTAMINE
21.627
22.327
38.27
36.3946
66.62
65.148
0.07178
0.20192
0.00231
0.0092
7

beta-Ala
β-ALANINE
2.2944
2.0961
3.48
3.29484
4.9516
3.9527
0.10216
0.25881
0.00231
0.0092
7

3-Phenylpropionate
3-PHENYLPROPIONIC ACID
8.6553
9.8714
29.14
48.6612
53.281
74.39
0.22064
0.36454
0.00256
0.0094

Ser
SERINE
16.853
20.939
34.11
35.0987
45.686
40.867
0.0143
0.08387
0.00257
0.0094

TABLE 9-2

TEST (COMPARISON

HEALTHY

WITH HEALTHY SUBJECTS)

SUBJECT
ORAL CANCER
CANCER
CANCER

1.5 HOURS
1.5 HOURS
ON EMPTY
(1.5 HOURS/
(ON EMPTY/

AFTER DIET
AFTER DIET
STOMACH
AFTER DIET)
STOMACH)
PUBLICLY

COMPOUND
NAME OF SUBSTANCE
AVERAGE
S.D.
AVERAGE
S.D.
AVERAGE
S.D.
P-value
Q-value
P-value
Q-value
KNOWN

1-Methylnicotinamide
1-METHYLNICOTINAMIDE
0
0
0.0863
0.1482
0.0447
0.061
0.00093
0.0283
0.002658
0.0093969

3-Hydroxy-3-methylglutarate
3-HYDROXY-3-METHYLGLUTARIC ACID
0
0
0.1326
0.2051
0.1545
0.263
0.00093
0.0283
0.002658
0.0093969

Guanine
GUANINE
0.8532
0.8635
2.0106
2.3806
2.1938
2.504
0.08023
0.214
0.002701
0.0093969

3-(4-Hydroxyphenyl)propionate
3-(4-HYDROXYPHENYL)PROPIONIC ACID
5.7327
5.3987
16.824
25.171
26.897
26.52
0.22686
0.3665
0.002948
0.0099587

4-Methylbenzoate
4-METHYLBENZOATE
13.693
16.348
29.523
44.709
40.875
35.76
0.15324
0.2945
0.003242
0.0107135

Ru5P
RIBULOSE-5-PHOSPHORIC ACID
3.6439
3.1654
6.2273
3.6308
6.4447
3.509
0.00427
0.0611
0.003529
0.0111736

Cadaverine
CADAVERINE
17.118
26.089
64.655
111.69
70.095
93.24
0.03264
0.1341
0.003529
0.0111736
7

alpha-Aminoadipate
α-AMINOADIPIC ACID
2.2491
4.0828
2.9071
1.7281
3.6911
4.177
0.00511
0.0611
0.00362
0.011228

N-epsilon-Acetyllysine
N6-ACETYLLYSINE
0.2091
0.532
0.3292
0.4221
0.6021
0.626
0.07152
0.2019
0.004421
0.0131769

Glucosamine
GLUCOSAMINE
0.0982
0.2061
0.4062
0.7291
0.7409
1.101
0.15617
0.2945
0.004612
0.0133543

Pipecolate
PICOLINIC ACID
0.5181
0.5391
0.7482
0.7248
1.5685
1.624
0.27202
0.4195
0.004658
0.0133543
79

Cystine
CYSTINE
0.1372
0.4283
0.2625
0.288
0.4851
0.588
0.01123
0.0813
0.005031
0.0141617

Leu
LEUCINE
15.883
26.949
27.312
49.448
47.753
73.1
0.14171
0.2945
0.005288
0.0146144
7

Carnosine
CARNOSINE
0.2446
0.406
0.092
0.1094
0.0635
0.201
0.30422
0.4242
0.005549
0.0150626

Urocanate
UROCANIC ACID
3.3151
4.3875
5.4696
8.6626
6.7838
7.392
0.14171
0.2945
0.005833
0.0152872

Phe
PHENYLALANINIE
22.976
38.395
24.975
26.739
43.982
42.62
0.34078
0.4504
0.005833
0.0152872

2-Deoxyribose 1-phosphate
2-DEOXYRIBOSE-1-PHOSPHORIC ACID
0
0
0.4503
1.456
1.3412
3.671
0.0198
0.1075
0.005141
0.015558

CMP
CYTIDINE DISODIUM 5′-MONOPHOSPHATE
0
0
0.0262
0.0806
0.6152
1.603
0.16259
0.3014
0.006141
0.015558

p-Hydroxyphenylacetate
p-HYDROXYPHENYLACETIC ACID
9.3775
15.199
23.122
26.812
32.256
31.62
0.02914
0.1266
0.006704
0.0167057

3-Hydroxybutyrate
POLYHYDROXYBUTYRIC ACID
5.849
7.9585
9.1822
7.9574
9.7319
5.422
0.06574
0.2012
0.008441
0.0205966

N-Acetylputrescine
N-ACETYLPUTRESCINE
3.7027
4.2148
6.7508
9.5333
8.5994
8.382
0.14928
0.2945
0.008537
0.0205966

7-Methylguanine
7-METHYLGUANINE
0.0973
0.169
0.1594
0.1784
0.2467
0.17
0.15081
0.2945
0.008981
0.021304

Inosine
INOSINE
1.1818
5.2853
1.1392
2.1327
0.932
1.333
0.00764
0.0611
0.00911
0.021304

Lys
LYSINE
54.872
77.058
59.839
61.894
107.38
92.02
0.35465
0.4647
0.010257
0.0232693

DHAP
DIHYDROXYACETONEPHOSPHORIC ACID
9.8189
12.418
14.832
12.189
15.183
10.62
0.02633
0.1213
0.011224
0.0250892

3-Methylhistidine
3-METHYLHISTIDINE
0.269
0.3248
0.3945
0.198
0.6527
0.531
0.10982
0.2608
0.011398
0.0251085

Carbamoylaspartate
CARBAMOYLASPARTIC ACID
0.1314
0.3286
0.3321
0.6735
0.5884
0.684
0.27515
0.4195
0.012219
0.0259014

Creatinine
CREATINE
4.5834
2.8466
5.6183
2.1009
6.9995
4.008
0.05589
0.1976
0.012269
0.0259014

1-Methyl-2-pyrrolidinone
N-METHYL-2-PYRROLIDONE
0
0
3.393
4.145
1.6277
2.739
0.00093
0.0283
0.013779
0.0286895

Pyruvate
PYRUVIC ACID
71.74
129.01
95.867
72.494
100.95
72.28
0.00733
0.0611
0.014053
0.0288663

Carnitine
CARNITINE
1.3784
1.4646
1.6847
1.0058
2.2939
1.956
0.0524
0.1896
0.014613
0.0292252
79

Propionate
PROPIONIC ACID
212.01
162.5
503.43
443.17
444.31
328.5
0.00733
0.0611
0.01733
0.0333437

5-Aminovalerate
5-AMINOVALERIC ACID
353.84
383.45
680.09
897.61
681.35
530.6
0.0675
0.2012
0.01733
0.0333437

N-Acetylornithine
N-ACETYLORNITHINE
0.15
0.3066
0.3977
0.6845
0.5015
0.628
0.15694
0.2945
0.020103
0.0377233

Tyr
TYROSINE
29.353
30.264
39.195
38.122
49.956
32.98
0.30125
0.4242
0.020467
0.0379391
7

5-Oxoproline
5-OXOPROLINE
11.522
26.849
11.424
13.484
14.236
20.71
0.06343
0.2012
0.022209
0.0406711

Creatinine
CREATININE
30.546
53.384
26.173
13.642
33.368
35.71
0.04595
0.1746
0.024074
0.0434287

Homoserine
HOMOSERINE
0.3548
0.5828
0.657
0.7476
0.6454
0.537
0.07306
0.2019
0.024286
0.0434287

Fumarate
FUMARIC ACID
0.4579
1.4637
1.5633
2.3957
0.703
0.883
0.01212
0.0814
0.025797
0.0455463

Gly
GLYCINE
130.77
167.9
157.76
117.97
222.74
193.5
0.14928
0.2945
0.026069
0.0455463

A substance in which “7” or “9” is described in the column labeled “Publicly Known” is a known substance disclosed in Non-Patent Literature 7 or 9.

Herein, the patients with oral cancer included stages I to IVa, and include oral squamous cell carcinoma (17 cases), malignant melanoma (2 cases), and adenoid cystic carcinoma (1 case). Spermine, spermidine, or acetylated spermine or spermidine consistently have a high concentration in comparison of an oral cancer tissue sample obtained during surgery and a healthy part in a vicinity of the oral cancer tissue.

For example, choline (second substance from the top in Table) among the substances is a known substance in Non-Patent Literatures 7 and 9, and an increase in the concentration of the substance in saliva has been confirmed. However, oral cancer can be identified with high accuracy by a mathematical model combined with a plurality of novel markers by the same procedure as those in pancreatic cancer and breast cancer. The substance is increased in oral cancer, but is not increased in breast cancer. Therefore, when the substance is included as a variable of the mathematical model, the specific type of cancer can be expressed.

The concentrations of metabolites in the cancer tissue sample obtained during surgery of oral cancer and the healthy tissue sample near the cancer tissue sample (herein, the concentration corrected with the weight of the tissue in μM/g is used) are shown in FIG. 17. In the drawing, the healthy tissue is at a left part and the cancer tissue is at a right part. An extent of progression (grade) of cancer is represented by I, II, III, and Via. The drawing shows some substances that have a significant difference between the healthy part and the cancer part.

A difference in the concentration of saliva between the patients with oral cancer and the healthy subjects (C) when a method of collecting saliva in the patients with oral cancer was changed is shown in FIG. 18. For the healthy subjects, choline (Choline) that had the smallest P value in comparison of saliva from the patients with oral cancer is expressed as an example. For the healthy subjects (C), saliva was collected 1.5 hours after eating. For oral cancer, saliva was collected from the same patients, and saliva was collected 1.5 hours after eating as P1, collected 3.5 hours after eating as P2, and collected during fasting (before breakfast) as P3.

Table 10 shows results in which the absolute concentrations of polyamines and hypoxanthine were measured using saliva collected from 17 healthy subjects, 21 patients with pancreatic cancer, 16 patents with breast cancer, and 20 patients with oral cancer during fasting (hungry from 9:00 of previous night, no eating on the collection day) by liquid chromatography-mass spectrometer (LC-MS). A P value for evaluation of difference in average was calculated using the Student's t-test as a parametric test because the number of cases was small.

Results of determination of polyamines and hypoxanthine (Hypoxanthine) as a metabolite other than the polyamines are shown in Table 10. Among the polyamines, when N1,N12-diacetylspermine (N1,N12-diacetylspermine) was measured using CE-TOFMS, the peak thereof overlapped the peak of another substance. When LC-qTOFMS was used, the peak of N1,N12-diacetylspermine and the peak of the other substance could be separately measured. Herein, only the samples that were collected during fasting were used. The quantitative values determined for 17 cases of healthy subjects, 21 cases of pancreatic cancer, 18 cases of oral cancer, and 16 cases of breast cancer are described (the unit of quantitative value is μM).

TABLE 10

HEALTHY
PANCREATIC

SUBJECT
CANCER
ORAL CANCER
BREAST CANCER

AVER-

AVER-

AVER-

AVER-

COMPOUND
JAPANESE NAME
AGE
SD
AGE
SD
P-value
AGE
SD
P-value
AGE
SD
P-value

Hypoxanthine
HYPOXANTHINE
1.088
1.421
3.245
3.158
0.00914
6.569
3.410
0.01718
1.496
1.643
0.452478

Spermidine
SPERMIDINE
1.571
1.553
3.943
2.957
0.003346
4.756
3.889
0.00269
5.119
6.958
0.003078

N8-Acetylspermidine
N8-ACETYL-
0.017
0.030
0.047
0.056
0.046547
0.088
0.114
0.01942
0.065
0.146
0.211951

SPERMIDINE

N1-Acetylspermidine
N1-ACETYL-
0.039
0.052
0.132
0.124
0.004252
0.482
0.689
0.01453
0.224
0.372
0.06753

SPERMIDINE

Spermine
SPERMINE
0.147
0.176
1.328
1.937
0.011404
2.526
3.351
0.00789
0.636
0.539
0.013041

N1,N8-
N1-,N8-DIACETYL-
0.090
0.118
0.184
0.153
0.038241
0.223
0.336
0.12931
0.219
0.292
0.11577

Diacetylspermidine
SPERMIDINE

N1-Acetylspermine
N1-ACETYL-
0.024
0.033
0.114
0.101
0.000769
0.242
0.379
0.02649
0.099
0.133
0.042624

SPERMINE

N1,N12-
N1,N12-DIACETYL-
0.068
0.103
0.189
0.169
0.010603
0.404
0.521
0.01502
0.173
0.243
0.127459

Diacetylspermine
SPERMINE

Saliva for LC-MS is treated as follows.

1) In 270 μL of methanol and ammonium hydroxide solution adjusted to 2 μM 2-morpholinoethanesulfonic acid, saliva stored at −80° C. is dissolved, and 30 μL thereof is added and stirred.

2) The mixture is centrifuged at 4° C. and 15,000 rpm for 10 minutes, and the entire upper layer is transferred to another tube.

3) The whole amount of the liquid is subjected to centrifugal concentration, and added to the liquid are 18 μL of 90% MeOH and 12 μL of BorateBuffer, resulting in redissolution.

4) 5 μL of the liquid is used for LC-MS analysis, and 20 μL of the liquid is used for ELISA analysis.

5) In the LC-MS analysis, 10 μL of ultrapure water containing 4 μM Methionine-sulfone is added to 5 μL of the aforementioned solution to obtain a dilution as a sample.

Measurement conditions of LC-MS are as follows.

LC system: Agilent Technologies 1290 infinity

Mobile phase: Solvent A; Water containing 1% Formic acid: Solvent B; Acetonitrile

containing 0.1% formic acid

Flow rate: 0.5 mL/min

Gradient [min. (% B)]: 0(98)-1(98)-3(55)-5(5)

Stop time: 7 min

Post time: 3 min

Column: CAPCELL CORE PC (Shiseido: 2.1 mm×50 mm, 2.7 mm)

Column temp.: 50° C.

Injection volume: 1 μL

MS: Agilent Technologies G6230A

Gas temp: 350° C.

Gas flow: 13 L/min

Neblizer Gas: 55 psig

Fragmentor: 150
Skimmer: 90
OCT1 RF Vpp: 200
VCap: 3500
Reference: 121.050873, 922.009798
Mode: Positive

According to the present invention, when the concentration of saliva is corrected (normalized), using data analysis of a correlation network reduces the influence of the concentration. Even in saliva in which concentrations vary greatly, a subject with pancreatic cancer can be distinguished from a healthy subject. The present method makes prediction of chronic pancreatitis, IPMN, breast cancer, and oral cancer possible.

A range in which a test can be performed using the marker of the present invention is determined by the value of concentration-correcting marker that reflects the saliva concentration, and saliva whose overall concentration is outside should be treated as outliers. In saliva within the range, a patient with each cancer can be distinguished from a healthy subject by a mathematical model that combines the markers of absolute concentrations or corrected relative concentrations.

INDUSTRIAL APPLICABILITY

Even by using saliva in which the concentration largely varies, pancreatic cancer, breast cancer, and oral cancer can be early detected in a healthy subject.

SALIVARY BIOMARKERS FOR CANCERS, METHODS AND DEVICES FOR ASSAYING THE SAME, AND METHODS FOR DETERMINING SALIVARY BIOMARKERS FOR CANCERS

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