CN107034295B - DNA methylation index for early diagnosis and risk evaluation of cancer and application thereof - Google Patents

Abstract

The present invention relates to a method for the diagnosis of cancer and/or the assessment of the risk of developing cancer in a subject, said method comprising the steps of: a) determining the DNA methylation status at the at least one DNA methylation site in a sample from the subject and in a sample from a healthy control group; b) and comparing the DNA methylation state at the DNA methylation site in a sample from the subject to the DNA methylation state at the corresponding DNA methylation site in a sample from a healthy control group, and if there is a significant difference in methylation state between the two, indicating that the subject has, or is at risk of having, cancer. The invention also provides a kit for the method.

Description

DNA methylation index for early diagnosis and risk evaluation of cancer and application thereof

Technical Field

The present invention relates to a method for diagnosing cancer and/or evaluating the risk of developing cancer in a subject and a kit therefor.

Background

Ovarian cancer is the most lethal gynecological tumor (1) worldwide (reference number for (1), the same below). The high mortality rate mainly comprises two reasons: lack of early detection means and chemotherapy resistance (2). Although initial chemotherapy has been effective in ovarian cancer patients, approximately 25% of patients develop chemotherapy resistance within six months (3). Intensive studies of tumor tissue have revealed that there are diverse genetic and epigenetic changes in ovarian carcinogenesis and progression. The discovery of effective early detection markers, particularly in the blood, is urgently needed to improve the prognosis of ovarian cancer.

It is well known that tumor cell lysis releases DNA fragments (cfDNA, free DNA) as well as other molecules like mirnas and cytokines into the circulation (4). Mutations of tumor-associated cfDNAs in the circulatory system are based on known mutations in advanced tumors. The release of cfDNA into the blood by tumors at the primary site is limited, so mutation detection to detect early stage cancer is challenging. In addition, the driving events for early cancer may be epigenetic in nature and may not be detectable based on genetic mutations.

Recent success of checkpoint immunotherapy has led us to re-realize that cancer is a systemic disease that can be directly affected by the immune system (5). Recent evidence suggests that specific types of immune cells play an important role in ovarian cancer cell proliferation and metastasis (6-8). Therefore, immune cells may serve as windows for snooping on tumor pathogenesis and physiological balance. Events associated with the immune system may be desirable as markers for tumors, especially in the early stages. It is envisioned that genetic or epigenetic alterations of tumor cells will trigger immune surveillance, and that immune cells will undergo epigenetic reprogramming to fight the appearance of tumor cells.

Recently, DNA methylation profiles of peripheral blood have been found to be associated with several cancers such as breast, lung, stomach, ovarian and pancreas (9-15). Previous whole epigenome association studies based on blood DNA methylation of ovarian cancer risk were performed on a low density platform containing 27,000CpGs (12, 16-18). Therefore, we set out that the current research uses a high-density platform to explore the potential value of leukocyte DNA methylation in epithelial-derived ovarian cancer.

Disclosure of Invention

The inventor conducts a two-stage case control study based on the association study of the whole epigenome in Chinese epithelial ovarian cancer patients. In the exploration phase, 450,000 CpG sites are screened by the whole genome methylation experiment, and the methylated CpG sites with significant differences are verified in the verification phase.

Accordingly, the present invention provides the following:

1. a method for diagnosis of cancer and/or assessment of risk of developing cancer in a subject, the method comprising the steps of:

a) determining the DNA methylation status in a sample from the subject and in a sample from a healthy control group at least one DNA methylation site selected from the group consisting of:

cg11792281, cg08450091, cg14559409, cg13888226, cg 173024, cg 09124, cg 226397887, cg 071933078, cg26097381, cg07930620, cg05526438, cg22119466, cg01381374, cg02488385, cg 06505087, cg 191832928, cg19138325, cg 146778, cg 4508178, cg16409562, cg 91919191585, cg 09712237122371223712237122371223607383, cg21581312, cg20430870, cg 067403, cg21166544, cg 1976690, cg 464646518, cg 09259125252525257708, cg 657708, cg 6520, cg 657708, cg 9808, cg 9805814354779, cg 7235729720, cg 728108, cg 9808, cg 26779, cg 98418108, cg 9808, cg 98418108, cg 26779, cg 267798, cg 98418108, cg 98207, cg 640432057, cg 43547798, cg 37324798, cg 43547798, cg, cgh, cg 43547798, cg, cgh, cg 43547798, cg 43547798, cgh, cg 37324798, cg 43547798, cgh, cg 43547708 cg 43547798, cgh, cg; and

b) comparing the DNA methylation state at the DNA methylation site in a sample from the subject to the DNA methylation state at the corresponding DNA methylation site in a sample from a healthy control group, and if there is a significant difference in methylation state between the two, indicating that the subject has or is at risk of having cancer.

2. The method of 1 above, wherein the DNA methylation sites comprise at least one DNA methylation site selected from the group consisting of: cg09249800, cg12717729, cg12049550, cg22441770, cg17833106, cg09182724, cg22534374, cg16962115, cg08450091, cg03002688, cg00207226, cg23327334, cg25607383, cg 22055, cg06415087, cg11937033, cg 80045, cg13888226, cg08365438, cg20430870, cg14559409, cg02756683, cg 193000854, cg 04508, cg 23078, cg19138325, cg 20956566, cg15125566, cg16409562, cg 22272, cg 14023232323999, cg01535567, cg26772894, cg 276514, cg 2765370637 16541275, cg 1204178, cg 865078, cg 864778, cg 2408678, cg 864778, cg 8678, cg 1404178, cg 864778, cg 2404178, cg 2408678, cg 240865078, cg 2408678;

more preferably selected from the group consisting of:

cg00207226, cg01535567, cg19716090, cg20956594, cg22534374, and cg 2263987; or selected from the group consisting of: cg06784563, cg16541275, cg00207226, and cg 00066854; or cg00207226 or cg 22441770.

3. The method according to 1 or 2 above, wherein the subject is a mammal, preferably a human, more preferably an asian human, more preferably a chinese human, most preferably a han-nationality human; and/or the cancer is an early, intermediate or advanced cancer, such as a solid tumor, preferably selected from breast, lung, stomach, ovarian, pancreatic cancer and the like, more preferably an ovarian cancer, such as early ovarian or advanced ovarian cancer, or selected from serous ovarian cancer, endometrioid ovarian cancer and mucinous ovarian cancer.

4. The method according to any one of the above 1-3, wherein the samples of the subject and the healthy control group are genomic DNA, preferably genomic DNA of peripheral blood, urine, saliva or hair, most preferably genomic DNA of peripheral blood (leukocytes).

5. The method of any one of claims 1-4 above, wherein the methylation state comprises hypermethylation (methylation) and hypomethylation (demethylation).

6. A kit for diagnosis of cancer and/or assessment of risk of developing cancer in a subject, the kit comprising:

(a) reagents or reaction systems for determining the DNA methylation status in a sample from a subject and in a sample from a healthy control group at least one DNA methylation site selected from the group consisting of:

cg11792281, cg08450091, cg14559409, cg13888226, cg 173024, cg 09124, cg 226397887, cg 071933078, cg26097381, cg07930620, cg05526438, cg22119466, cg01381374, cg02488385, cg 06505087, cg 191832928, cg19138325, cg 146778, cg 4508178, cg16409562, cg 91919191585, cg 09712237122371223712237122371223607383, cg21581312, cg20430870, cg 067403, cg21166544, cg 1976690, cg 464646518, cg 09259125252525257708, cg 657708, cg 6520, cg 657708, cg 9808, cg 9805814354779, cg 7235729720, cg 728108, cg 9808, cg 26779, cg 98418108, cg 9808, cg 98418108, cg 26779, cg 267798, cg 98418108, cg 98207, cg 640432057, cg 43547798, cg 37324798, cg 43547798, cg, cgh, cg 43547798, cg, cgh, cg 43547798, cg 43547798, cgh, cg 37324798, cg 43547798, cgh, cg 43547708 cg 43547798, cgh, cg; and optionally

(b) Instructions describing comparing the DNA methylation state at said DNA methylation site in a sample from a subject with the DNA methylation state at the corresponding said DNA methylation site in a sample from a healthy control group, and if there is a significant difference in the methylation states between the two, indicating that the subject has, or is at risk of having, cancer.

7. The kit according to 6 above, wherein the DNA methylation site comprises at least one DNA methylation site selected from the group consisting of:

cg09249800, cg12717729, cg12049550, cg22441770, cg17833106, cg09182724, cg22534374, cg16962115, cg08450091, cg03002688, cg00207226, cg23327334, cg25607383, cg 22055, cg06415087, cg11937033, cg 80045, cg13888226, cg08365438, cg20430870, cg14559409, cg02756683, cg 193000854, cg 04508, cg 23078, cg19138325, cg 20956566, cg15125566, cg16409562, cg 22272, cg 14023232323999, cg01535567, cg26772894, cg 276514, cg 2765370637 16541275, cg 1204178, cg 865078, cg 864778, cg 2408678, cg 864778, cg 8678, cg 1404178, cg 864778, cg 2404178, cg 2408678, cg 240865078, cg 2408678;

more preferably selected from the group consisting of:

cg00207226, cg01535567, cg19716090, cg20956594, cg22534374, and cg 2263987; or selected from the group consisting of: cg06784563, cg16541275, cg00207226, and cg 00066854; or cg00207226 or cg 22441770.

8. The kit according to 6 or 7 above, wherein the subject is a mammal, preferably a human, more preferably an asian human, more preferably a chinese human, most preferably a han-nationality human; and/or the cancer is an early, intermediate or advanced cancer, such as a solid tumor, preferably selected from breast, lung, stomach, ovarian, pancreatic cancer and the like, more preferably an ovarian cancer, such as early ovarian or advanced ovarian cancer, or selected from serous ovarian cancer, endometrioid ovarian cancer and mucinous ovarian cancer.

9. The kit according to any of the above 6-8, wherein the samples of the subject and the healthy control group are genomic DNA, preferably genomic DNA from peripheral blood, urine, saliva or hair, most preferably genomic DNA from peripheral blood (leukocytes).

10. The kit according to any one of the above 6-9, wherein the reagent or reaction system is used for determining the methylation status (hypermethylation or hypomethylation) of DNA by any one of the following methods:

1) methylation-specific PCR;

2) methylation specific restriction enzyme digestion;

3) bisulfite DNA sequencing;

4) methylation sensitive single nucleotide primer extension;

5) restriction enzyme landmark genome scan;

6) differential methylation hybridization;

7) chip-based methylation profile analysis; and

8) base specific cleavage/mass spectrometry.

Drawings

FIG. 1 is a flow chart of a study of methylated biomarker exploration and validation;

FIG. 2 shows the correlation between the genome expression of epithelial ovarian cancer. The Manhattan chart shows the results of all cases and controls analysis (A), early cases and controls analysis (B), late cases and controls analysis (C), and the red long horizontal line represents P<1.0x 10^-4；

FIG. 3. methylation sites with significant differences in the validation phase. The thermograms show differential analysis of DNA methylation including all cases and controls (total), early cases and controls (early), late cases and controls (late), serous ovarian cancer and controls (serous), endometrioid ovarian cancer and controls (endometrioid), mucinous ovarian cancer and controls (mucinous). The ordering of differential methylation sites is based on the false discovery rate of all cases and controls (total);

figure 4.6 analysis of the working profiles of subjects with significantly different methylation sites. (A) Subject working profiles for all cases and controls (total), early cases and controls (early), late cases and controls (late). (B) Subject performance profiles for serous ovarian cancer and control (serous), endometrioid ovarian cancer and control (endometrioid), mucinous ovarian cancer and control (mucinous);

FIG. 5 pathway analysis of Gene Ontology (GO) and differential methylation sites. (A) The venn plot shows the overlapping portions expressing differentially methylated sites. (B) GO annotation (biological process) clearly associated with 46 sites of differential methylation expressed. The vertical axis represents GO classification and the horizontal axis represents enrichment fold with significant GO annotation. (C) Genes included in the GO annotation pathway;

FIG. 6 correlation of DNA methylation with coagulation factor/platelet parameters. The degree of correlation in the hotspot plots with ovarian cancer risk/survival was based on-log 10 (false discovery rate). The color code for each hotspot has assigned white/light red as the lowest risk and dark red as the highest risk for ovarian cancer risk and survival. For clotting factors and platelets, the color codes range from blue to white to red, with red indicating a positive correlation and blue indicating a negative correlation. A positive correlation enhancement is indicated from white to red and a negative correlation enhancement is indicated from white to dark blue. PLCR platelet large cell ratio, PDW platelet distribution breadth, MPV mean platelet volume, DD D-dimer, PLT platelet count, PCT platelet specific volume, Fbg plasma fibrinogen, PT prothrombin time, AT antithrombin III;

FIG. 7. working characteristic curves of 40 subjects with significantly different methylation sites between all cases and controls in example;

FIG. 8. working characteristic curves for 24 subjects with significantly different methylation sites between early cases and controls in example;

FIG. 9. working characteristic curves for 39 subjects with significantly different methylation sites between advanced cases and controls in example;

FIG. 10. working characteristic curves of 32 subjects with significantly different methylation sites between serous ovarian cancer cases and controls in example;

FIG. 11. working characteristic curves for 34 subjects with significantly different methylation sites between endometrioid ovarian cancer cases and controls in example;

FIG. 12. working characteristic curves for 11 subjects with significantly different methylation sites between cases of mucinous ovarian cancer and controls in example; and

FIG. 13 correlation of DNA methylation with coagulation factor/platelet parameters. The degree of correlation in the hotspot plots with ovarian cancer risk/survival was based on-log 10 (false discovery rate). The color code for each hotspot has assigned white/light red as the lowest risk and dark red as the highest risk for ovarian cancer risk and survival. For clotting factors and platelets, the color codes range from blue to white to red, with red indicating a positive correlation and blue indicating a negative correlation. A positive correlation enhancement is indicated from white to red and a negative correlation enhancement is indicated from white to dark blue.

Detailed Description

Solid tumors are increasingly being considered as a systemic disease, which can be recognized by changes in DNA, RNA, proteins and metabolites in the blood. Although many studies have reported genetic mutational events in the circulatory system, few studies have focused on epigenetic DNA methylation markers in the circulatory system. To identify markers of ovarian cancer peripheral blood DNA methylation, the study conducted a two-stage whole epigenome association study. In the first stage, we examined 485,000 DNA methylation sites in peripheral blood cells of 24 cases of epithelial-derived ovarian cancer and 24 age-matched healthy controls and screened 96 CpG sites with significant variability. In the second stage, we validated using Illumina's Custom VeraCode methylation detection method in 206 epithelial-derived ovarian cancers and 205 controls and identified 46 CpG sites. We used 46 CpG sites to build a prediction model, and the final subject working characteristic curve shows that the model built using 6 of the CpG sites has a 77.3% prediction accuracy (95% confidence interval: 72.9% -81.8%). We found the enrichment of genes related to the immune system by performing pathway analysis on genes related to 46 CpG sites, including LYST (cg16962115, FDR ═ 1.24E-04), CADM1(cg21933078, FDR ═ 1.22E-02), NFATC1(cg06784563, FDR ═ 1.46E-02). In addition, there is a correlation between peripheral blood DNA methylation levels and platelet parameters/coagulation factor levels. This study revealed that a panel of epigenetic fluid biopsy markers are closely related to the patient's overall immune status and platelet parameters/coagulation system, etc., and can be used to detect the stage and subtype of all epithelial-derived ovarian cancers.

Accordingly, in one aspect, the present invention provides a method for the diagnosis of cancer and/or the assessment of the risk of developing cancer in a subject, said method comprising the steps of: a) determining at least one (1 to 96, e.g., 1,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, 21,22, 23, 24, 25, 26, 27, 28, 29, 30,31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 68, 66, 67, 72, 71, 72, 17, 18, 19, 20, 21,22, 23, 24, 25, 26, 27, 28, 29, 30,31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 65, 66, 67, 72, 71, 72, 67, 71, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96) DNA methylation status at a DNA methylation site selected from the group consisting of: cg11792281, cg08450091, cg14559409, cg13888226, cg 173024, cg 09124, cg 226397887, cg 071933078, cg26097381, cg07930620, cg05526438, cg22119466, cg01381374, cg02488385, cg 06505087, cg 191832928, cg19138325, cg 146778, cg 4508178, cg16409562, cg 91919191585, cg 09712237122371223712237122371223607383, cg21581312, cg20430870, cg 067403, cg21166544, cg 1976690, cg 464646518, cg 09259125252525257708, cg 65779, cg 657708, cg 9808, cg 98418143547708, cg 9805779, cg 7235728108, cg 728108, cg 9808, cg 98418108, cg 26779, cg 72814354779, cg 729720, cg 728108, cg 26779, cg 267798, cg 98418108, cg 9808, cg 98418108, cg 43547798, cg 43547798, cg, cgh, cg 43547798, cg, cgh, cg 43547798, cg 43547798, cg 37324798, cg 43547798, cg, cgh, cg 43547798, cg 439748, cg, cgh, cg 43547798, cg, cgh, cg 43547798, cgh, cg 43547798, cg 43; and b) comparing the methylation state of DNA at one or more of said DNA methylation sites in a sample from the subject with the methylation state of DNA at the corresponding one or more of said DNA methylation sites in a sample from a healthy control group, and if there is a significant difference in the methylation states between the two, indicating that the subject has, or is at risk of having, cancer.

In a preferred embodiment of the invention, the DNA methylation sites comprise at least one (1 to 46, e.g., 1,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, 20, 21,22, 23, 24, 25, 26, 27, 28, 29, 30,31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46) DNA methylation site selected from the group consisting of: cg09249800, cg12717729, cg12049550, cg22441770, cg17833106, cg09182724, cg22534374, cg16962115, cg08450091, cg03002688, cg00207226, cg23327334, cg25607383, cg 22055 972, cg06415087, cg11937033, cg 80045, cg13888226, cg08365438, cg20430870, cg14559409, cg02756683, cg 193000854, cg 04508, cg 23078, cg19138325, cg 20956566, cg15125566, cg16409562, cg 22272, cg 140232323999, cg01535567, cg26772894, cg 276514, cg 3572357819, cg 864546, cg 8646, cg 8678, cg 864778, cg 1404578, cg 72355648, cg 8678, cg 72999, cg 8678, cg 729748, cg 22678, cg 355648, cg 22678, cg 8678, cg 8648, cg 7235729748, cg 8678, cg 72357279, cg 22627, cg 8648, cg 7227, cg 8678, cg 7227 and cgh.

In a preferred embodiment of the invention, the DNA methylation site is further preferably selected from the group consisting of: cg00207226, cg01535567, cg19716090, cg20956594, cg22534374, and cg 2263978. The 6 CpG sites are used for constructing an ovarian cancer prediction model in the invention, the risk of ovarian cancer is successfully predicted, and the diagnosis effect is better when the hierarchical analysis of different stages and histological subtypes of the ovarian cancer is carried out. Thus, in the methods of the invention, preferably 1-6 (e.g. 1,2, 3, 4, 5 or 6) DNA methylation sites selected from: cg00207226, cg01535567, cg19716090, cg20956594, cg22534374, and cg 2263978. The assay may also simultaneously detect one or more of any other DNA methylation sites listed above.

In a preferred embodiment of the invention, the DNA methylation site is selected from the group consisting of: cg06784563, cg16541275, cg00207226, and cg 00066854. The inventors found for the first time that the changes in the DNA methylation level at these 4 CpG sites correlate with 5 platelet parameters (PLT: platelet count, PCT: platelet specific volume, MPV: mean platelet volume, PDW: platelet distribution breadth, PLCR: large platelet ratio). It is speculated that changes in the DNA methylation status of these CpG sites relative to healthy controls may be used to determine changes in platelet status in a subject and/or to diagnose/determine platelet-associated diseases (e.g., inflammatory diseases) in a test subject. Thus, in the method of the invention, preferably 1-4 (e.g. 1,2, 3 or 4) DNA methylation sites selected from: cg06784563, cg16541275, cg00207226, and cg 00066854. The assay may also simultaneously detect one or more of any other DNA methylation sites listed above. The above DNA methylation site is particularly preferred cg00207226, since the change in DNA methylation status of this site relative to a healthy control can be used not only to diagnose ovarian cancer and/or risk assessment of ovarian cancer, but also to diagnose/determine the platelet-associated disease or disorder in the subject being tested.

In a preferred embodiment of the invention, the DNA methylation site is cg 22441770. The inventors found for the first time that changes in the DNA methylation level AT this CpG site correlate with the coagulation factor parameters PT (prothrombin time) and AT III (antithrombin III). Thus, changes in the DNA methylation state of the site relative to a healthy control can be used not only for diagnosing ovarian cancer and/or risk assessment of ovarian cancer, but can also be used to determine changes in the state of coagulation factors (particularly with parameters PT and AT III), and/or for diagnosing/determining diseases or conditions associated with coagulation factors (particularly with parameters PT and AT III), such as coagulation disorders.

In the present invention, the subject may be any human or non-human mammal. Examples of non-human mammals include primates, livestock animals (e.g., horses, cattle, sheep, pigs, donkeys), laboratory test animals (e.g., mice, rats, rabbits, guinea pigs), pets (e.g., dogs, cats), and captive wild animals (e.g., deer, foxes). Preferably, the mammal is a human. The population of people is not particularly limited, but is preferably asian, more preferably chinese, and most preferably han.

In the present invention, the term "cancer" includes not only malignant tumors originating in epithelial tissues but also malignant tumors originating in mesenchymal tissues, and also includes other malignant tumors such as nephroblastoma, malignant teratoma, and the like. The type of cancer is not particularly limited, but in the present invention, solid tumors are preferred, including, but not limited to, breast cancer, lung cancer, stomach cancer, ovarian cancer, pancreatic cancer, liver cancer, lymphoma, hemangioma, and the like.

In the present invention, the term "diagnosis" refers to the judgment of whether a subject suffers from a certain disease or its symptoms, signs, etc. In the present invention, the methods, reagents or reaction systems for determining changes in DNA methylation status at a DNA methylation site of the invention relative to a healthy control can be used for the diagnosis of cancer, either alone or in combination with one or more of any other cancer diagnostic methods/markers. In the latter case, the method of the present invention may be an adjunct method to the diagnosis of cancer in certain types of cancer diagnosis.

In the present invention, the term "evaluation of risk of developing cancer" refers to predicting the degree of likelihood or risk of a subject having cancer.

In the present invention, the term "sample" refers to any biological material containing genomic DNA or genomic DNA itself, including but not limited to cellular material, biological fluids (such as blood), stool, tissue biopsy specimens, surgical specimens, or fluids introduced into an animal and subsequently removed (e.g., a solution recovered from an enema wash) or genomic DNA extracted therefrom. Biological samples tested according to the methods of the invention may be tested directly or may require some form of processing prior to testing. For example, a biopsy or surgical sample may require homogenization prior to testing, or it may require sectioning to test qualitative expression levels of individual genes in situ. Alternatively, the cell sample may require permeabilization prior to testing. Furthermore, to the extent that the biological sample is not in liquid form (if such a form is to be tested), it may require the addition of reagents (e.g. buffers) to circulate the sample. Preferably, the sample is genomic DNA, preferably genomic DNA from peripheral blood, urine, saliva or hair, most preferably genomic DNA from peripheral blood (leukocytes). The samples of the subject and the control group are generally of the same type.

In the present invention, the term "healthy control group" refers to a healthy subject who does not have cancer. In one embodiment of the invention, the healthy subject has not been previously clinically diagnosed with cancer and is matched in age frequency, etc., to the subject being tested. The determination and selection of a healthy control group is routine for those skilled in the medical arts.

In the present invention, the term "DNA methylation" has a meaning well known in the art. Under the catalysis of methyltransferase, cytosine of two nucleotides of CG of DNA is selectively added with methyl to form 5-methylcytosine, which is commonly seen in 5 '-CG-3' sequence of gene. Most vertebrate genomic DNA has a small amount of methylated cytosine, mainly concentrated in the 5' non-coding region of the gene, and present in clusters. Methylation of DNA can cause inactivation of genes, and DNA methylation causes conformational changes of DNA in certain regions, thereby affecting the interaction of proteins and DNA, and causing gene inactivation. In addition, sequence-specific methylated binding proteins (MBD/MeCP) can bind to methylated CpG in the promoter region, preventing transcription factors from acting on the promoter, thereby repressing the gene transcription process. 70% to 80% of all cpgs are methylated. CpG can be concentrated into clusters, called "CpG islands".

In the present invention, the term "DNA methylation site" refers to a site of CpG on genomic DNA. Specifically, in the present invention, the cytosine residue of the DNA methylation site is denoted by cgxxxxxxxx, which is the site number of the Illunima methylation chip, which is clear to those skilled in the art. For the DNA methylation sites involved in the present invention, the chromosome numbers and related genetic information of these sites are also shown in FIG. 3 of the specification. Due to the double-stranded complementary structure of DNA, DNA methylation sites of the present invention also include methylation sites of cytosines corresponding to the cytosine residues represented by cgxxxxxxxx described above at position n +1 on the complementary DNA strand. Thus, when referring to the DNA methylation site (n position) of a cytosine residue represented by cgxxxxxxxx, the methylation site of the corresponding cytosine at position n +1 on the complementary DNA strand is also included.

In the present invention, the term "DNA methylation state" is understood to refer to the presence, absence and/or amount of methylation in a particular nucleotide or nucleotides in a region of DNA. In one embodiment of the invention, the methylation state at the DNA methylation site includes, but is not limited to, hypermethylation and hypomethylation. In the present invention, "hypermethylation" and "methylation" are used interchangeably, meaning the presence and hypermethylation of CpG at a certain nucleotide; whereas "hypomethylation" and "demethylation" are used interchangeably, meaning that there is no CpG at a certain nucleotide and low methylation. As described above, determining the DNA methylation status of a cytosine residue at a DNA methylation site (n-position) represented by cgxxxxxxxx also includes determining the methylation status of the corresponding cytosine methylation site at position n +1 on the complementary DNA strand.

Methods for assessing DNA methylation status are known to those skilled in the art and include, but are not limited to, the following: 1) methylation-specific PCR; 2) methylation specific restriction enzyme digestion; 3) bisulfite DNA sequencing; 4) methylation sensitive single nucleotide primer extension; 5) restriction enzyme landmark genome scan; 6) differential methylation hybridization; 7) chip-based methylation profiling (examples include the BeadArray platform technology (Illumina, USA)); and 8) base specific cleavage/Mass Spectrometry (Sequenom, USA). The reagents or reaction systems for these methods are well known to those of ordinary skill in the art and are generally provided in the form of commercially available kits. Thus, the invention also provides the use of a reagent or reaction system for assessing the methylation state of DNA in a method of the invention or for preparing a kit of the invention.

In the present invention, the term "significant difference in methylation state" refers to a statistically significant difference in DNA methylation state (hypermethylation/hypomethylation) at a DNA methylation site in a sample from a subject as compared to the DNA methylation state (hypermethylation/hypomethylation) at the DNA methylation site corresponding to a sample from a control group (e.g., a healthy control group), e.g., P<0.05, preferably P<0.01. In a particularly preferred embodiment of the invention, P<1.0x 10^-4。

Examples

The present invention will be described in detail below with reference to examples, which are intended to illustrate the present invention only, and are not intended to limit the scope of the present invention. The scope of the invention is specifically defined by the appended claims.

Materials and methods

Research population

The ovarian cancer cases are original epithelial ovarian cancer females (all Chinese Han nationality) with the age of 25-85 years, which are confirmed by histological diagnosis in tumor hospital of Tianjin medical university in 2007 and 2013. The patient had a history of cancer treatment or transfusion 6 months before the diagnosis. Blood samples of the case groups were collected in the operating room, blood routine and hemagglutination tests gave blood cell counts, coagulation factor values, and these were all performed within one week prior to surgery. The control group was subjects without tumor (all chinese hamsters) in community residents of Tianjin city, who had not been previously diagnosed as cancer and matched the case group with age frequency (5 year interval). Each subject completed a questionnaire (including demographic information, health status, lifestyle and eating habits) and provided a blood sample for study. The study was approved by the ethical review committee of the tumor hospital and cancer institute, department of medicine, tianjin university, and informed consent was obtained for studies with clinical specimens.

In the exploration phase, a group of 24 cases of epithelial ovarian cancer (12 FIGO stages I-II and 12 FIGO stages III-IV) and age-matched controls were randomly selected for whole genome methylation analysis. In the validation stage, 206 epithelial ovarian cancer cases and 205 age-matched controls were used to validate the CpG sites picked during stage I.

DNA extraction and bisulfite conversion

The genomic DNA of the collected sample was extracted from leukocytes by means of the Kit QiazenDNA Blood Mini Kit (Qiazen, Valencia, Calif., USA) and stored at-80 ℃ before bisulfite treatment. Genomic DNA was processed according to the manual of the reagent bisulfite conversion Kit EZ-96DNA Methylation-Gold Kit (D5008) (Zymo Research, Orange, Calif.) in which unmethylated cytosine was converted to uracil and methylated cytosine was left intact. Bisulfite converted DNA was stored at-20 ℃ and used within one week after conversion.

Whole genome methylation profiles

In the exploration phase, whole genome methylation profiles were processed using Illumina Infinium somamethyation 450(San Diego, CA) chips. We used Illumina BeadArray^TMThe Reader obtains the chip image, and Illumina's genome studio software is used for reading data. All samples were subjected to quality control tests including staining control, extension control, target removal control, hybridization control, bisulfite conversion control, specificity control, negative control, non-polymorphic binding control, and background labeling with negative control beads for each well. The DNA methylation value (β value) for each CpG site ranges from 0 (unmethylated) to 1 (methylated) representing the relative ratio of methylated allele signal to total fluorescence signal minus background intensity.

Validation of the selected CpG sites

In the validation stage, Illunima's Custom VeraCode methylation analysis was used to confirm 96 selected CpG sites. Illumina's BeadXpress Reader and Illumina's genome studio are used to scan the chip and read the data, respectively. Quality control tests including 9 internal controls (specific allele extension control, PCR identity control, vacancy extension control, sex control, first hybridization control, second hybridization control, negative control, contamination detection control) were performed in the analysis.

Statistical analysis:

to compare the demographic characteristics of the case and control groups, chi-square test and independent sample t-test were performed on the categorical variables and continuity variables, respectively. In the initial exploration phase, median beta values for each CpG site were compared for differences between case and control groups using independent sample t-tests and stratified by epithelial ovarian cancer stage (I-II, III-IV). We then selected 96 most significantly different methylated CpG sites from the whole genome data for validation, under the following conditions: (a) p<1.0×10^-4(ii) a (b) The trend between stage I-II and stage III-IV epithelial ovarian cancer cases was consistent compared to the control group; (c) genotyping can be determined using the Illumina Custom VeraCode methylation assay. In the validation phase, independent sample t-tests were used to assess differences in methylation values between study groups. Pearson correlation analysis was performed to assess the correlation of DNA methylation with blood counts/clotting factors. To correct for errors from multiple tests, the False Discovery Rate (FDR), FDR, was calculated using Benjamini and Hochberg correction methods<0.05 is significant.

In addition, to evaluate the classification performance of differentially methylated CpG sites during the validation phase, we used a logistic regression model to build a predictive model of the receiver operating characteristic curve (ROC). DNA methylation markers were selected using a stepwise and manual selection method.

Furthermore, to determine the commonality of genes in biological pathways, we performed pathway enrichment assays using the DAVID gene ontology tool for genes near differentially methylated CpG sites associated with ovarian cancer risk. All analyses were performed using SAS software version 9.3 (SAS Institute, Cary, NC, USA) and R software version 3.1.2.

Results

To determine the variability of DNA methylation in peripheral blood leukocytes of ovarian cancer, we performed a two-stage case-control whole epigenomic association study (EWAS), a flow chart of the study design is shown in FIG. 1. In the initial exploration phase, the whole genome was scanned in 24 casesEpithelial ovarian cancer and 24 age-matched healthy controls were performed using Illumina's infinium management 450K chips. There was no statistical significance in the variability of case and control, age, BMI, smoking history, family history of cancer and history of menopause. All cases were serous ovarian cancer, 12 early stages, 12 late stages (table 1), manhattan panel showing the results of whole epigenome analysis (figure 2). The methylation levels of 242 CpG sites between 24 cases of epithelial ovarian cancer and 24 control groups showed significant statistical differences (P)<1.0×10^-4). When the tumor stage was analyzed hierarchically, the 39 CpG sites were in 12 early stages (I) compared to the 24 control groups&Stage II) with statistical variation in epithelial ovaries, 375 CpG sites in 12 late stages (III)&IV) had statistical variability. We selected 96 most distinct CpG sites for validation at stage ii (see table 2).

In a separate sample containing 206 epithelial ovarian cancers and 205 controls, we analyzed the 96 CpG methylation sites selected using the Illunima's Custom VeraCode methylation detection method. Age, BMI, smoking history, family history of cancer, and history of menopause were consistent between the cases and controls. We found that 40 CpG sites were clearly associated with epithelial ovarian cancer risk, 16 hypermethylation and 24 hypomethylation (see figure 3, table 3). Stratified analysis of tumor stage revealed that 88 early cases (stage I & II) showed significant differences in methylation levels of 24 CpG sites (9 hypermethylated and 15 hypomethylated) compared to the control group, and 115 late cases (stage III & IV) showed significant differences in methylation levels of 39 CpG sites (21 hypermethylated and 18 hypomethylated) compared to the control group. The study also performed a stratified analysis of histological typing of methylation levels in case and control groups. Compared with 205 control groups, we found 32 CpG sites in 85 serous ovarian cancers, 34 CpG sites in 59 epithelial ovarian cancers, and 11 CpG sites in 24 mucinous ovarian cancers with significant differences in methylation levels (FIG. 3, Table 4). Overall, we determined the methylation levels of 46 CpG sites with significant variability in all types of comparisons.

Representative operating characteristic curves (ROCs) for subjects at a single CpG locus are shown in fig. 7-12. The 6 CpG sites (cg00207226, cg01535567, cg19716090, cg20956594, cg22534374 and cg 2263987) are screened by a prediction model with the area under the working characteristic curve of the subject being 0.773(0.729-0.818), which shows that the 6 CpG sites have higher prediction capability on the ovarian cancer risk than the control group (77.3%). The 6 CpG sites have better diagnosis effect in the hierarchical analysis of different stages and histological subtypes: the area under the curve of the early stage tumor is 0.754(0.693-0.816), the area under the curve of the late stage tumor is 0.803(0.754-0.853), the area under the curve of serous ovarian cancer is 0.773(0.713-0.833), the area under the curve of endometrioid ovarian cancer is 0.799(0.735-0.864), and the area under the curve of the mucinous ovarian cancer is 0.725(0.609-0.841) (see fig. 4).

To determine the commonality of the 56 genes putatively regulated by these 46 methylated CpG sites, we performed pathway enrichment analysis using the DAVID gene ontology approach. The highest fold enrichment assay is leukocyte-regulated cytotoxicity, which includes two genes in this pathway: LYST (cg16962115, FDR 1.24E-04) and CADM1(cg21933078, FDR 1.22E-02). Most of the enrichment analysis results contain immune system-related pathways; and we found that the gene NFATC1(cg06784563, FDR ═ 1.46E-02) was also associated with the immune system in the Reactom pathway database (see figure 5). In addition, we assessed the difference in methylation sites and the correlation between blood cell number/clotting factors (see table 5). There was no significant correlation between DNA methylation and blood cell number, except platelet number (figure 13). We found several CpG sites to have a correlation with platelet parameters and coagulation factors (fig. 6). The 5 platelet parameters (PLT: platelet count, PCT: platelet specific volume, MPV: mean platelet volume, PDW: platelet distribution breadth, PLCR: large platelet ratio) and the 4 coagulation factor (PT: prothrombin time, D-dimer: D-dimer, Fbg: fibrinogen, AT III: antithrombin III) indices were included in the correlation analysis. We found that four CpG sites (cg06784563, cg16541275, cg00207226, cg00066854) correlated with all platelet parameters. For coagulation factors, there is no site for association with these four coagulation factors. The DNA methylation level AT the cg22441770 site correlated with PT and AT III, and 11 CpG sites correlated with D-dimer.

Discussion of the related Art

In this two-stage case-control whole epigenomic association study, we identified 46 new CpG methylation sites that were significantly correlated with han female ovarian cancer risk. We also constructed a relatively high accuracy risk prediction model using 6 CpG sites, especially for early stage ovarian cancer. Pathway analysis was performed on 46 methylation site genes with significant differences, which were found to be part of immune system-related pathways. Differential methylated CpG sites associated with ovarian cancer risk were not associated with blood cell counts, but were found to be associated with platelet parameters/coagulation factors. To our knowledge, this is the first report that reveals the correlation of peripheral blood leukocyte DNA methylation with platelet parameters/coagulation factors.

Peripheral blood DNA methylation markers have been reported in several types of cancers, including breast cancer, head and neck cancer, ovarian cancer, gastric cancer, colorectal cancer, pancreatic cancer, etc., but their biological significance has not been clarified (19). DNA methylation is known to be associated with lifestyle, diet and environmental exposure (20). Recently, more and more studies have shown that DNA methylation has a correlation with smoking and BMI (21, 22). Previous studies hypothesized that changes in peripheral blood DNA methylation may be due to shifts in blood cell composition. To reveal this mechanism, Koestler et al compared DNA methylation of normal human peripheral blood leukocyte subsets (including B cells, granulocytes, monocytes, NK cells, CD4+ T cells, CD8+ T cells and Pan-T cells) and determined that 50 CpG sites have differences in methylation levels of different leukocyte subsets (11). However, none of these 50 CpG sites were statistically significant in our study, which is also consistent with previous reports in breast cancer (9). In our study, methylation sites associated with ovarian cancer were not associated with leukocyte subpopulations. Interestingly, we found that peripheral blood DNA methylation has some relationship to platelet parameters and coagulation factors. Platelets in the blood have been reported to play a critical role in participating in coagulation, immune response, inflammation and tumor progression/metastasis (8, 23). In ovarian cancer, it has been reported that platelet numbers are associated with the prognosis of epithelial-derived ovarian cancer (24) and that platelets directly promote ovarian cancer cell proliferation (6). In ovarian cancer with epithelial-mesenchymal transition, major alterations in DNA methylation can be induced by TGF β 1 (25), and TGF β 1 secreted by platelets can promote epithelial-mesenchymal transition of tumor cells (26). However, the mechanism of DNA methylation and platelet relationship in peripheral blood is not clear and further studies are needed.

In the present study, we determined that the genes at or near the differential CpG sites associated with ovarian cancer risk are involved in immune system-related pathways, including leukocyte-mediated cytotoxicity, cell killing, and immune response processes. Three of these genes, NFATC1(cg06784563), LYST (cg16962115), and CADM1(cg21933078) are in immune system-related pathways. Cg06784563 is a promoter regulatory region encoding a nuclear transcription factor that activates T cells at the 5' end of NFATC 1. NFAT proteins have been reported to play an important role in the immune system (27), and NFAT transcription factors play an extremely important role in many pathways, including tumors. In ovarian cancer, NFACT1 was significantly overexpressed in tumor tissues relative to paired normal tissues and promoted tumor cell proliferation in vitro by upregulating c-myc through activation of the ERK1/2/p38/MAPK signaling pathway (29). Cg21933078 is 15kb upstream of chromosome 11 CADM1, CADM1 is a cell adhesion molecule and is considered a tumor suppressor gene (30, 31). CADM1 low expression may be due to promoter region methylation and thus promote tumor cell proliferation/invasion (30, 31). In ovarian cancer, the down-regulation of CADM1 may serve as a poor prognostic marker (32). CADM1 performs a new function of inhibiting tumor metastasis by making tumor cells more sensitive to immune surveillance mechanisms, and the loss of CADM1 is a key step in tumor immune editing (33). Cg16962115 is located at 1q42.3, 35kb upstream of LYST, which encodes the lysosomal transport regulatory protein. The LYST mutation is associated with Shederia-east syndrome (34). There are no reports of association of LYST with ovarian or other cancers. Further studies are needed to explore the function of this gene in relation to ovarian cancer.

Previous studies of whole epigenome association of ovarian cancer have identified several differentially methylated CpG sites in peripheral blood (12,16, 17). However, none achieved the selection criteria (P) at the time of our validation<1.0×10^-4). The exploratory phase of previous studies was based on Illumina's 27K methylation chips, and the coverage of the 450K chips we used was much larger than before. Only one of the 96 CpG sites selected for validation in our study was contained in the 27K chip.

In summary, we have determined that a panel of blood-borne DNA methylation markers are associated with epithelial-derived ovarian cancer risk. Therefore, peripheral blood DNA methylation expression profiles can become a new tool for risk assessment and early detection of ovarian cancer. Large prospective cohort studies and intensive biological mechanism studies are needed to validate these new biomarkers and provide an ovarian cancer epigenomic treatment strategy for precision medicine.

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TABLE 1 Total epigenome detection and validation subject Baseline and clinical features

Claims (2)

1. Use of a reagent or reaction system for determining the DNA methylation status in a sample from a subject and in a sample from a healthy control group at the following 6DNA methylation sites in the manufacture of a diagnostic agent for ovarian cancer prognosis and/or risk assessment of ovarian cancer in a subject: cg00207226, cg01535567, cg19716090, cg20956594, cg22534374 and cg 2263987, wherein the samples of the subject and the healthy control group are genomic DNA from peripheral blood leukocytes, wherein the ovarian cancer is epithelial-derived ovarian cancer and the subject is chinese han-nationality.

2. The use of claim 1, wherein the methylation status comprises hypermethylation and hypomethylation of a subject relative to a healthy control group.

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