CN107541565A - The cancer DNA methylation mark of host's PMNC and T cell - Google Patents

The cancer DNA methylation mark of host's PMNC and T cell Download PDF

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CN107541565A
CN107541565A CN201610464441.6A CN201610464441A CN107541565A CN 107541565 A CN107541565 A CN 107541565A CN 201610464441 A CN201610464441 A CN 201610464441A CN 107541565 A CN107541565 A CN 107541565A
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hcc
dna methylation
dna
stages
ids
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CN107541565B (en
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李宁
摩西·斯义夫
苏菲·彼得罗普洛斯
张永宏
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Beijing Youan Hospital
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Abstract

Present invention finds cancer to have DNA methylation mark in the DNA of the T cell and PMNC (PBMC) of host.The invention discloses the CG IDs of the DNA from PBMC, predict hepatocellular carcinoma (HCC) clinical progress by stages and chronic hepatitis by methylation levels of the above-mentioned CG IDs in PBMC or the DNA of T cell.The invention also discloses; it is a kind of to predict that HCC kit, and the experiment of pyrosequencing DNA methylation, Receiver Operating Characteristics (ROC) experiment, penalized regression experiment and hierarchical clustering analyze the application in HCC is predicted by using the CG IDs of the invention identified.One of ordinary skill in the art in can obtain the DNA methylation mark for any cancer and any cancer clinical progress by stages using the present invention.The DNA methylation mark (CG IDs) that the present invention describes will be used to diagnose：A. cancer；B. cancer difference clinical progress is by stages；C. just in reaction of the patient receiving treatment to treatment；D. chronic hepatitis B or chronic hepatitis C.

Description

The cancer DNA methylation mark of host's PMNC and T cell
Technical field
The present invention relates to the DNA methylation mark in human DNA, particularly molecular diagnosis field.
Background technology
Hepatocellular carcinoma (HCC) is the fifth-largest most common cancer (1) in the world, and it is especially popular in Asia.And liver The incidence of cell cancer is highest in the region of prevalence of hepatitis B, and this shows that it has possible causality (2).To height The tracking and the early diagnosis to changing from chronic hepatitis to HCC of danger patient's such as chronic hepatitis patient can improve cure rate. The long-term surviving rate of patients with hepatocellular carcinoma is extremely low at present, because hepatocellular carcinoma is nearly all diagnosed to be in middle and advanced stage.Such as Fruit is diagnosed ahead of time, and liver cancer can be treated efficiently, its cure rate>80%.The progress of diagnostic imaging improves HCC non-intruding Formula detects (3,4).However, current diagnostic method, it includes image and using the immune of single albumen such as α-fetoprotein Analysis, HCC (2) can not be often diagnosed ahead of time.The challenge is not limited to HCC, similarly exists in other cancers.The molecule of cancer is examined The tumour focused in the Tumour DNA in histocyte and biomaterial, including blood plasma from tumour (5,6), circulation of breaking is thin Born of the same parents (7) and tumor host microenvironment (8,9).Currently a popular and widely accepted hypothesis is：Drive what cancer occurred and was in progress Molecular change essentially consists in tumour itself, and the associated change of host is occurred mainly in tumor microenvironment.Therefore, tumour micro-loop The identification of immunocyte has been a great concern (10,11) in border.
DNA methylation, DNA covalent modification, be genome functions commitment dominant mechanism, it is including HCC (16) (12-15) generally changes in the tumour including.Tumour DNA methylation characteristic collection of illustrative plates can distinguish clinical tumor and enter Exhibition by stages, and may be used as staging, prognosis and the potential powerful of the response to chemotherapy side effect (17).Examined in early days The major defect to be methylated in disconnected using Tumour DNA is, it is necessary to carry out intrusive mood operation and need the dissection of suspected tumor Visualization.Tumour cell in circulation is the non-intruding source of Tumour DNA, and it be used to measure the DNA in tumor suppressor gene Methylate (18).The DNA of the HCC cells of hypomethylation is (19) that can be detected in blood samples of patients, and full-length genome Bisulfite sequencing has recently been used as the hypomethylation DNA (20) for detecting the cancer cell in the blood plasma from HCC patient.So And the source is limited, the early stage particularly in cancer, and its DNA methylation collection of illustrative plates and host DNA methylate figure Spectrum is obscured.
Host immune-monitoring plays important work in tumour generation by removing tumour cell and suppressing tumour growth With this idea just proposed via Paul Ehrlich (21,22) before a century, but was not propped up then Hold.However, by the immune system of as shown by data host that is accumulated in animal and Human Clinical Study in tumour generation by " exempting from Epidemic disease editor " plays an important role, and it is related to three phases：Eliminate, balance and escape (23-25).In mankind's progressivity black Plain knurl (26-31), cancer of the esophagus (32), oophoroma (33,34) and colon cancer (35-37) some clinical researches in, it is tumor-infiltrated The presence of property cytotoxin CD8+T cells is related to preferable prognosis.It is when the cancer cell in circulation is in no clinical symptoms When can be detected, the reason for immune system is considered as cancer dormancy (15,38).It is interesting that nearest tumour DNA methylation and transcriptome analysis disclose the tumor stage Specific immunological markers (39,40) of infiltrating lymphocytes.So And these marks represent the target immunocyte in tumor microenvironment, and use of these marks in early diagnosis Intrusive mood is needed to operate.What tumor infiltrating immunocyte represented is only sub-fraction peripheral blood cells (41-44).White blood cell Totality (global) DNA methylation change reported in being studied with EWAs is shown in bladder, head and neck and oophoroma White blood cell DNA methylation difference, and these differences are unrelated with the difference that leucocyte is distributed (45).These researchs are mainly In order to identify potential DNA methylation change in cancer gene, it possibly serves for the replacement mark that DNA methylation changes in tumour Will.However, can periphery host immune system show the unique DNA methyl for responding the cancerous state related to cancer progression Change, not yet illustrated.
The content of the invention
The inventors found that cancer progression and DNA methylation collection of illustrative plates phase unique in host's peripheral blood immunocyte Close.The present invention in cancer and potential chronic inflammation liver diseases it is also shown that there is area in these DNA methylation marks Not.
The present invention describes the discovery collection (discovery set) (10 being made up of 69 HCC patients in BeiJing, China region Position control, and from following hepatitis B, hepatitis C, HCC clinical progress BCLC-0 phases, BCLC-A phases, BCLC-B phases by stages Each 10 patients in (stage 1-3) and 9 patients from the BCLC-C/D phases (stage 4)) DNA methylation collection of illustrative plates, wherein HCC clinical progress is according to European liver EASD and European Cancer Treatment Research Organization's EASL-EORTC hepatocellular carcinomas by stages Clinical practice guideline (table 1) determines.The present invention describes DNA methylation figure using full-length genome method (Illumina450k experiments) Spectrum, and the gene type to may relate to is without the weighting of anticipation.Present invention firstly discovers that hepatitis B and hepatitis C DNA methylation collection of illustrative plates in the PMNC DNA methylation collection of illustrative plates different from HCC, and hepatitis B and third DNA methylation collection of illustrative plates in the PMNC of type hepatitis and different clinical progress different HCC DNA methylation by stages Collection of illustrative plates.These collection of illustrative plates and the DNA methylation collection of illustrative plates of the HCC tumours described before do not have obvious overlapping (16), and this shows that they are anti- The change of the gene function of PMNC has been answered, and the change that Tumour DNA methylates can not be represented.Therefore, this hair The bright DNA methylation change for indicating host immune system during cancer.The host T that the present invention also demonstrates cancer patient is thin DNA methylation mark in born of the same parents.The present invention also demonstrates to be existed significantly between PBMCs and the DNA methylation collection of illustrative plates of T cell It is overlapping.Pyrosequencing is carried out by the T cell DNA of the patient (n=79) to one group of independence, present invention demonstrates from It was found that in the T cell of the HCC patient of queue (discovery cohort) differential methylation 4 kinds of genes.
Present invention demonstrates that：The present invention can be predicted by using the statistical model based on these DNA methylation marks Cancer and the cancer clinical progress of unknown sample are by stages.The present invention has important open for the mechanism and its treatment for understanding disease Show, and provide cancer PMNC DNA non-invasive diagnosing.By using any those skilled in the art The method for the mapping that methylated for full-length genome known, such as the sequencing of full-length genome bisulfite, capture are sequenced, methylated DNA immunization co-precipitation (MeDIP) is sequenced and any other available complete genome DNA methylates mapping method, art technology Personnel can obtain DNA methylation mark in the immune system of any cancer according to the present invention.
It is the preferred embodiment of the invention below：
In the first aspect, the present invention provides a kind of cancer patient's PMNC for being used to predict cancer (PBMC) DNA methylation mark, the DNA methylation mark are methylated mapping by complete genome DNA (mapping) obtained from method, such as Illumina 450K or 850K experiment, the sequencing of full-length genome bisulfite, first Base DNA immunization co-precipitation (MeDIP) sequencing or oligonucleotide microarray hybridization.
In one embodiment, DNA methylation mark is the DNA from PBMC as follows CG IDs, its For predicting that hepatocellular carcinoma (HCC) clinic is entered by using DNA methylation levels of the CG IDs in PBMC or T cell Exhibition is by stages and chronic hepatitis：
In one embodiment, DNA methylation mark is the CG IDs from T cell as follows, and it is used for Predict HCC clinical progress by stages and Chronic Liver by using DNA methylation levels of the CG IDs in PBMC or T cell It is scorching：
In one embodiment, DNA methylation mark is CG IDs as follows, and it is used for by using described DNA methylation measured values of the CG IDs in T cell or PBMC predicts different HCC clinical progress by stages, wherein the CG IDs is obtained using statistical model such as penalized regression or cluster analysis,
For distinguishing the target CG IDs in HCC stages 1 (BCLC-0 phases) and control：cg14983135、cg10203922、 cg05941376、cg14762436、cg12019814、cg14426660、cg18882449、cg02914652；
For distinguishing the target CG IDs in HCC stages 2 (BCLC-A phases) and control：cg05941376、cg15188939、 cg12344600、cg03496780、cg12019814；
For distinguishing the target CG IDs in HCC stages 3 (BCLC-B phases) and control：cg05941376、cg02782634、 cg27284331、cg12019814、cg23981150；
For distinguishing the target CG IDs in HCC stages 4 (BCLC-C/D phases) and control：cg02782634、cg05941376、 cg10203922、cg12019814、cg14914552、cg21164050、cg23981150；
For distinguishing the target CG IDs in HCC stages 1 (BCLC-0 phases) and hepatitis B：cg05941376、cg10203922、 cg11767757、cg04398282、cg11151251、cg24742520、cg14711743；
For distinguishing the target CG IDs in HCC stages 1 (BCLC-0 phases) and stage 2-4 (BCLC-A/B/C/D phases)： cg03252499、cg03481488、cg04398282、cg10203922、cg11783497、cg13710613、cg14762436、 cg23486701；
For distinguishing the target CG IDs in HCC stages 2 (BCLC-A phases) and stage 3-4 (BCLC-B/C/D phases)： cg02914652、cg03252499、cg11783497、cg11911769、cg12019814、cg14711743、cg15607708、 cg20956548、cg22876402、cg24958366；
For distinguishing HCC stages 1-3 (BCLC-0/A/B phases) and stage 4 (BCLC-C/D phases) target CG IDs： cg02782634、cg11151251、cg24958366、cg06874640、cg27284331、cg16476382、cg14711743。
In one embodiment, DNA methylation mark is CG IDs as follows, and it is used for by using described DNA methylation measured values of the CG IDs in T cell or PBMC predicts HCC clinical progress by stages, wherein the CG IDs are Obtained using statistical model such as penalized regression or cluster analysis,
In second aspect, the present invention provides a kind of kit for being used to predict cancer, it is characterised in that comprising for examining Survey the device and reagent of the DNA methylation measured value of DNA methylation mark.
In one embodiment, the present invention provides a kind of for predicting that hepatocellular carcinoma HCC clinical progress is by stages or chronic The kit of hepatitis, it is characterised in that surveyed comprising the DNA methylation for detecting the DNA methylation mark in specification table 3 The device and reagent of value.
In one embodiment, the present invention provide it is a kind of be used for predict HCC clinical progress by stages or chronic hepatitis examination Agent box, it is characterised in that include the dress of the DNA methylation measured value for detecting the DNA methylation mark in specification table 6 Put and reagent.
In one embodiment, the present invention provides a kind of for predicting the kit of HCC differences clinical progress by stages, its It is characterised by, device and examination comprising the DNA methylation measured value for detecting the DNA methylation mark in specification table 4 Agent.
In one embodiment, the present invention provides a kind of for predicting the kit of HCC clinical progress by stages, its feature It is, includes the device and reagent of the DNA methylation measured value for detecting the DNA methylation mark in specification table 5.
At the 3rd aspect, the present invention provides a kind of gene pathway, it is characterised in that the gene pathway is in the outer of cancer By commitment in all blood immune systems.
At the 4th aspect, the invention provides CG IDs disclosed in this invention application.
In one embodiment, methylated the invention provides DNA pyrosequencings and test answering in HCC is predicted With, it is characterised in that use following primer by using above-mentioned CG IDs, such as following genes：
AHNAK (outer positive GGATGTGTCGAGTAGTAGGGT, outer reverse CCTATCATCTCCACACTAACGCT, inside just To TGTTAGGGGTGATTTTTAGAGG, interior reverse ATTAACCCCATTTCCATCCTAACTATCTT, and sequencing primer TTTTAGAGGAGTTTTTTTTTTTTA)；
SLFN2L (outer positive GTGATYTTGGTYAYTGTAAYYT, outer reverse TCTCATCTTTCCATARACATTTATTTA R, interior positive AGGGTTTYAYTATATTAGYYAGGTTGG, interior reverse ATRCAAACCATRCARCCCTTTTRC, sequencing primer YYYAAAATAYTGAGATTATAGGTGT)；
AKAP7 (outer positive TAGGAGAAAGGGTTTATTGTGGT, outer reverse ACACACCCTACCTTTTTCACTCCA, it is interior Positive GGTATTGATTTATGGTTAGGGATTTATAG, interior reverse AAACAAAAAAAACTCCACCTCCAATCC, sequencing primer GGGATTTATAGTTTTGTGAGA)；
And
STAP1 (outer positive AGTYATGTYTTYTGYAAATAAAAATGGAYAYY, it is outer reverse TTRCTTTTTAACCACCAACACTACC, interior positive YYGTTTYTTTYATYTTYTGGTGATGTTAA, it is interior reverse ARARRRCAATCTCTRRRTAATCCACATRTR, sequencing primer GGTGATGTTAATYTTYTGTTTA).
In one embodiment, the answering in HCC is predicted the invention provides Receiver Operating Characteristics (ROC) experiment With, it is characterised in that by using above-mentioned CG IDs, such as STAP1 (cg04398282).
In one embodiment, the invention provides hierarchical clustering to analyze the application in HCC is predicted, its feature exists In by using above-mentioned CG IDs.
At the 5th aspect, the present invention provides a kind of method for identifying the DNA methylation mark for predictive disease, its The step of being characterised by performing statistical analysis comprising the DNA methylation measured value to obtaining from sample.
In one embodiment, the present invention provides a kind of side for identifying the DNA methylation mark for predictive disease Method, it is characterised in that the DNA methylation measured value is by performing Ilumina to the DNA extracted in sample Beadchip450K or 850K test what is obtained.
In one embodiment, DNA methylation measured value is by performing DNA pyrophosphoric acids to the DNA extracted in sample It is sequenced, based on mass spectrometry (EpityperTM) or the PCR experiment that methylates obtain.
In one embodiment, methods described includes performs statistics to the DNA methylation measured value obtained from sample The step of analysis, it is related that statistical analysis includes Pearson.
In one embodiment, statistical analysis is tested including Receiver Operating Characteristics (ROC).
In one embodiment, statistical analysis includes hierarchical clustering analysis experiment.
Definition
Term " CG " used herein refers to, the dinucleotides sequence in the DNA containing cytimidine and guanine base.This A little dinucleotides sequences can be methylated in the mankind and other animal DNAs.CG ID show it in human genome Position, (public can know CGs annotations listed herein by following addresses as shown in Illumina 450K：https：// bioconductor.org/packages/release/data/annotation/html/ IlluminaHumanMethylation450k.db.html, it is stored in R language installation kits In IlluminaHumanMethylation450k.db, as described in Triche T and Jr. IlluminaHumanMethylation450k.db：Illumina Human Methylation 45k annotate data, R language Installation kit 2.0.9 versions).
Term " penalized regression " used herein refers to, it is intended to is identified from a large amount of biomarkers for prediction result institute The statistical method of the predictive factor of the minimum number needed, it is held using the R statistics bags " penalized " as described in following documents OK：Goeman, J.J.L1penalized estimation in the Cox proportional hazards model, Biometrical Journal52(1),70-84。
Term " cluster " used herein refers to that be grouped a group objects in one way, the mode causes phase Object compared with the object in group (be referred to as one kind) in other groups (class), it is even more like to each other (in a sense or separately A kind of meaning).
Term " hierarchical clustering " used herein refers to, a kind of similar (near) based between class or different (remote) degree structure The statistical method of the level of " class " is built, such as described in following documents：Kaufman,L.；Rousseeuw,P.J.(1990) .Finding Groups in Data：An Introduction to Cluster Analysis(1ed.).New York： John Wiley.ISBN 0‐471‐87876‐6。
Term " gene pathway " used herein refers to, the gene of one group of encoding proteins, it is known that the albumen leads in physiology Road or during to each other mutually influence.These paths carry out table by biocomputer method such as Ingenuity path analysises Sign：http：//www.ingenuity.com/products/ipa。
Term " Receiver Operating Characteristics (ROC) experiment " used herein refers to that one kind is used to illustrate predictive factor performance And create the statistical method of chart.Under the different threshold values setting of predictive factor (different weight percentage to methylate), relative to False positive rate, True Positive Rate is predicted, described in for example following documents of its predictive factor：Hanley,James A.；McNeil, Barbara J.(1982)."The Meaning and Use of the Area under a Receiver Operating Characteristic(ROC)Curve".Radiology 143(1)：29–36.
Term " multiple linear regression " used herein refers to that one kind is assessed such as the ratio of methylating, age, sex Multiple " independent variables " or " predictive factor " and such as cancer or carcinoma stage " result " or " dependent variable " between relation Statistical method.This method determines, when including several " independent variable " in model, every kind of " predictive factor " (independent variable) In the statistical significance of prediction " result " (dependent variable).
Brief description of the drawings
Fig. 1：The full-length genome distribution of cancer specific DNA methylation mark in PMNC.
Figure 1A come from normal healthy controls (Ref.), chronic hepatitis B (HepB), chronic hepatitis C (HepC) and HCC Full-length genome figure (the IGV of each clinical progress difference that the DNA methylation between (CAN1, CAN2, CAN3, CAN4) rises by stages (integrator gene group reader) genome browser)；
The upper figures of Figure 1B represent to methylate with HCC progress the DNA methylation β value in reduced site.Figure below represents adjoint The DNA methylation β value in the increased site of HCC progress DNA methylations.
Fig. 2 are in the DNA of normal, chronic hepatitis and 69 individual HCC progress of each clinical progress disease stage states of HCC Methylate mark.Each column represents an individual, and often row represents a CG site, and methylation level is by gray level expressing.Black Represent methylation highest, white represents that methylation is minimum, and it is medium that grey represents methylation.
Fig. 3
Each clinical progress of Fig. 3 A.HCC by stages (CAN1, CAN2, CAN3, CAN4) otherness methylate it is overlapping between CG sites CG bit number of points；
Fig. 3 B. are changed into hypomethylation or the CGs numbers of supermethylation during HCC is in progress (CAN1, CAN2, CAN3, CAN4) Amount.
Fig. 4 use the DNA methylation mark from the HCC stages 1 (BLC-0 phases) (20 patients) to 49 Chronic Livers Scorching and HCC patient prediction.Black represents methylation highest, and white represents that methylation is minimum, and grey represents methyl Change degree is medium.
Fig. 5 use the DNA methylation mark from the HCC stages 2 (BLC-A phases) (20 patients) to 49 Chronic Livers Scorching and HCC patient prediction.Black represents methylation highest, and white represents that methylation is minimum, and grey represents methyl Change degree is medium.
Fig. 6 use the DNA methylation mark from the HCC stages 3 (BLC-B phases) (20 patients) to 49 Chronic Livers Scorching and HCC patient prediction.Black represents methylation highest, and white represents that methylation is minimum, and grey represents methyl Change degree is medium.
Fig. 7 use the DNA methylation mark from the HCC stages 4 (BLC-C/D phases) (20 patients) chronic to 49 Hepatitis and the prediction of HCC patient.Black represents methylation highest, and white represents that methylation is minimum, and grey represents first Base degree is medium.
Fig. 8 are using 350 CG DNA methylations marks (table 3) to the pre- of 69 controls, chronic hepatitis and HCC patient Survey.Black represents methylation highest, and white represents that methylation is minimum, and it is medium that grey represents methylation.
Fig. 9 are using 31 CG DNA methylations marks (table 5) to the pre- of 69 controls, chronic hepatitis and HCC patient Survey.Black represents methylation highest, and white represents that methylation is minimum, and it is medium that grey represents methylation.
Figure 10
Figure 10 A. are using following predictive CGs of the present invention DNA methylation measured value to distinguishing HCC stages 2-4 The prediction (possibility is 0 to 1) in (BLC-A/B/C/D phases) and stage 1 (BLC-0 phases), target CG IDs：cg03252499、 cg03481488、cg04398282、cg10203922、cg11783497、cg13710613、cg14762436、cg23486701；
Figure 10 B. are using following predictive CGs of the present invention DNA methylation measured value to distinguishing HCC stages 3-4 The prediction (possibility is 0 to 1) in (BLC-B/C/D phases) and stage 1 and 2 (BLC-0/A phases), target CG IDs：cg02914652、 cg03252499、cg11783497、cg11911769、cg12019814、cg14711743、cg15607708、cg20956548、 cg22876402、cg24958366；
Figure 10 C. are using following predictive CGs of the present invention DNA methylation measured value to distinguishing the HCC stages 4 The prediction (possibility is 0 to 1) in (BLC-C/D phases) and stage 1 to 3 (BLC-0/A/B phases), target CG IDs：cg02782634、 cg11151251、cg24958366、cg06874640、cg27284331、cg16476382、cg14711743。
Figure 11 come from normal healthy controls (n=10；TCTRL-1 to TCTRL-10) and each clinical progress of HCC (n=10 by stages； TCAN1, TCAN2, TCAN3, TCAN4) T cell between DNA methylation collection of illustrative plates difference.
Figure 12 use DNA methylation measured value of 370 CGs (table 6) from T cell in PBMC DNA to 49 Chronic hepatitis and the prediction of HCC patient.
Figure 13
Figure 13 A. use DNA methylation measured value of 350 CGs (table 3) of the DNA from PBMC in T cell DNA pre- Survey HCC；
Figure 13 B. be derived from the T cell (TCAN1-4) of HCC differences clinical progress by stages DNA otherness methylate CGs with The DNA of PBMC (PBMCCAN1, PBMCCAN2, PBMCCAN4) from HCC differences clinical progress by stages otherness methylates It is overlapping between CGs；
Figure 13 C. use DNA methylation measured value of 31 CGs (table 5) of the DNA from PBMC in T cell DNA pre- Survey HCC.
Figure 14 pass through to replicating 4 concentrated in the T cell DNA of all control sample and HCC early clinics progress by stages The DNA methylation difference of gene carries out pyrosequencing to be confirmed.
Figure 15 methylating to distinguish HCC and normal healthy controls as the STAP1 of biomarker using in T cell DNA Specificity (true-positive fraction) (Y-axis) and Receiver Operating Characteristics (ROC) (Illumia of sensitivity (non-false positive) (X-axis) 450K data) (Figure 15 A)；Measurement PBMC in as biomarker STAP1 methylate distinguish HCC and all against (be good for Health and chronic hepatitis) specificity (true-positive fraction) (Y-axis) and sensitivity (non-false positive) (X-axis) Receiver Operating Characteristics (ROC) (Figure 15 B).
STAP1 in Figure 16 (using pyrosequencing) measurement T cell as biomarker methylates to distinguish HCC and the specificity of normal healthy controls (Y-axis) and the Receiver Operating Characteristics (ROC) (Figure 16 A) of sensitivity (X-axis)；(use burnt phosphorus Acid sequencing) measurement T cell in as biomarker STAP1 methylate distinguish HCC and all against specificity (Y Axle) and sensitivity (X-axis) Receiver Operating Characteristics (ROC) (Figure 16 B).
Embodiment
The DNA methyl that embodiment 1. is in progress in by stages related PMNC (PBMC) to HCC cancer clinicals Change mark
Patient Sample A
According to European liver EASD and European Cancer Treatment Research Organization's EASL-EORTC clinical practice guidelines：Liver is thin The management of born of the same parents' cancer diagnoses HCC clinical progress by stages.Patient is divided into four groups, including BLC-0 phases (stage 1), BLC-A phase (ranks Section 2), BLC-3 phases (stage 3) and BLC-C/D phases (stage 4).For simplicity, above-mentioned each phase is in accompanying drawing of the present invention and reality Apply a middle finger stage 1 to 4.Chronic is confirmed for the practice guideline of hepatitis B according to U.S. hepatopathy EASD AASLD The diagnosis of type hepatitis, and according to AASLD recommendations come diagnosing chronic hepatitis C, and test, management and treatment hepatitis C. Strict exclusion standard is：Any other known inflammatory disease of T cell or monocyte feature can be changed (except B-mode Bacterium or viral infection, diabetes beyond hepatitis or hepatitis C, asthma, autoimmune disease, active thyroid disease Disease).The Clinical symptoms of patient is shown in Tables 1 and 2.Study the regulation that participant agrees to the Capital University of Medical Sciences.The research has obtained position In the ethics approval of the Capital University of Medical Sciences of Pekinese and McGill University's (IRB studies numbering A02-M34-13B).
Table 1. trains the clinical data of queue (training cohort)
DNA is prepared by the PBMC cells of all patients.T cell (patient ID is separated from normal healthy controls and HCC patient：1- 1、1-3、1-6、2-2、2-3、2-4、3-6、4-2、4-3)
Table 2. tests the clinical data of (duplication) queue
AFP- α-fetoproteins；HBV- hepatitis type B viruses；HCV- HCVs；TACE- is through transcatheter arterial chemistry Embolism；RFA- RF ablations
Illumina Beadchip 450K are analyzed
The blood for being derived from patient is added and is coated with EDTA test tube, passes through Ficoll-Hypaque using standard method Density gradient centrifugation carrys out separating peripheral blood mononuclear cells, and is come on Ficoll-Hypaque layers using normal experiment step Collecting monocytic cell, because they have relatively low density, monocyte (46) is separated from blood platelet by cleaning.Use city Sell people's DNA extraction kit (Qiagen) and DNA is extracted from cell.Then using bisulfite conversion DNA, and carry out Illumina HumanMethyaltion450k BeadChip hybridize, and the standard method recommended using manufacturer is scanned. Such as the recommendation of McGill genome Quebec innovation center, according to Illumina Infinum HD technical user guides, sample is directed to Sheet glass and its position in microarray are grouped at random, and all samples are hybridized and scanned simultaneously to reduce batch Influence.Illumina microarray hybridizations and scanning are carried out by McGill genome Quebec innovation center according to manufacturer's guide. Illumina microarrays are analyzed using the ChAMP Bioconductor bags (47) based on R language.Using minfi quality controls and Option is calibrated, IDAT files are used as to the input value of champ.load functions.It is P for the detected value at least one sample >0.01 probe, filter initial data.The probe of X or Y chromosome is filtered out, to reduce Effect of gender, and band just like document (48) SNPs identified in probe, and can be with the probe of multiple location matches as what is identified in document (48).Make Influenceed with champ.svd function pair nonstandardized techniques data (non-normalized data) analysis batch.In preceding 6 principal components 5 and group and batch (sheet glass) it is relevant.Using β-mixture digit calibration (BMIQ), by means of champ.norm functions (norm=" BMIQ "), to perform interior array (Intra-array) calibration, to adjust because of the design of Infinium2 type probes The data deviation (47) of introducing.After BMIQ calibrations, the batch influence using champ.runcombat function corrections.
According to Houseman algorithms (49), using estimateCellCounts functions and refer to FlowSorted.Blood.450k data, perform the cell count point that PMNC is distributed in inventive samples Analysis.The β value of the standardized data of batch correction is used for downstream statistical analysis.
It is by stages linear between the DNA methylation distributed number in 450K CG site in order to calculate each clinical progress of HCC The degree of correlation, (adjusted using Pearson's correlation function under R language, and by using Benjamini Hochberg " fdr " method Whole P values (Q)<And traditional Bonferroni correction (Q 0.05)<1x10-7) correction multiple check, to calculate standard Changing DNA methylation value, (wherein sequence number 0 represents to compare, and 1 and 2 represent hepatitis B and the third type respectively by stages with each clinical progress of HCC Hepatitis, 3-6 represent HCC four clinical progress by stages respectively) between Pearson's degree of correlation.Hepatitis B and hepatitis C, Similar method, such as Illumina microarrays of new generation such as Illumina 850K microarrays can be used.
Correlation between the horizontal quantitative distribution of locus specificity DNA methylation and HCC progress
The analysis shows that substantial amounts of DNA methylation mark is related to HCC progress (160,904 sites).The present invention's Analysis concentrates on the most significant 3924 site (r of change>0.8；r<-0.8；Δβ>0.2/、Δβ>- 0.2, p<10-7).Relative to Chronic hepatitis B and hepatitis C and control, DNA methylation of these sites during HCC is in progress strengthen the complete of change Genome figure is as shown in Figure 1A.The DNA methylation horizontal box figure increased or decreased in site proves to be in progress with HCC during HCC DNA methylation change, and with HCC progress, hypomethylation content increase (Figure 1B).It is related using the Pearson that subtracts one The cluster of analysis shows that these sites can divide all individual HCC patients from control, hepatitis B and hepatitis C individual Class comes out, and is sorted in except patient CAN1-5 in HepC and HCC boundary line, and this shows have between different groups of individual member Very strong uniformity (Fig. 2).
The DNA methylation mark of HCC PMNC is used to distinguish cancer sample and control
Therefore these DNA methylation marks have, by Patient Sample A be categorized as different HCC clinical progress by stages should With.Thermal map in Fig. 2 shows the enhancing of the change of the DNA methylation difference with HCC progress.Importantly, the present invention is each The combination of analysis shows that DNA methylation mark can be by the individual HCC patient of earliest period clinical progress by stages from hepatitis B Distinguished with hepatitis C, this is a crucial challenge in HCC early diagnosis.Also, the analytical table of the present invention The change of the bright PBMC from HCC patient DNA methylation can be with changing phase region caused by the chronic inflammation as virus initiation Not.Based on the discovery of the present invention, those skilled in the art can obtain the DNA methylation mark of similar other cancers.
The unique and overlapping differential methylation site of embodiment 2. and different HCC clinical progress are by stages relevant, and can incite somebody to action HCC makes a distinction from hepatitis B and hepatitis C
Inventor by using in ChAMP perform Bioconductor bags Limma (50) describe normal healthy controls and respectively HCC clinical progress by stages between differential methylation CGs.Each HCC clinical progress difference methyl between normal healthy controls by stages Change the quantity (p in CG sites<1x10-7) increase with the progress of clinical stages；Stage 1 (BLC-0 phases) is 14375；Stage 2 (BLC-A phases) is 22018；Stage 3 (BLC-B phases) is 30709；Stage 4 (BLC-C/D phases) is 54580.Using based on The hypergeometry Fei Sheer of R language, which is accurately examined, determines conspicuousness overlapping between two groups.Exist between cancer each stage significant Overlapping (Fig. 3 A), which imply that all clinical progress of HCC by stages in impacted common mark (p be present<1.9e-297)。
The site of hypomethylation is risen to compared to the ratio in the site of supermethylation by the 26% of the BLC-0 phases in HCC 57% (Fig. 3 B) of BLC-C/D phases.Related to Pearson point of the increase of this hypomethylation bit number of points along with HCC progress The result for analysing (Fig. 1, Fig. 2) is consistent.For each HCC clinical progress by stages, by using threshold value p<1x10-7( Bonferroni correction after full-length genome conspicuousness) and Δ β be +/- 0.3 (being used for the HCCBLC-0 phases) and p<10-10With Δ β is +/- 0.3 (being used for the BLC-A/B/C/D phases) (to reduce the site for analysis using more rigorous threshold value to later stage Quantity), the CG for obtaining high robustness methylates mark, and the CG marks that methylate are used for further analysis (the BLC-0 phases are 74, the BLC-A phases are 14, and the BLC-B phases are 58, and the BLC-C/D phases are 298).Independently obtained by integrating from each stage Mark list, and unnecessary CG sites between each stage are removed, so as to obtain the irredundant 350 CG sites of combination (table 3).
Most significant 350 CG IDs that table 3. obtains from PBMC DNA, it is by stages and healthy in each clinical progress of HCC Differential methylation between control
In this research and clinical settings, HCC patient is one group of relevant alcohol, smoking (52-55), sex (56) and age (57) heterogeneous population, and these factors have notified influence DNA methylation.In addition, peripheral mononuclear cells is the different of cell Matter mixture, between individual the change of cell distribution may can also influence DNA methylation.The present invention uses Houseman algorithms The cell count distribution (49) of each case of First Determination.By two factor ANOVA and then carry out it is paired relatively and The correction of multiple check, it is found that the cell count between different groups does not have significant difference.Group, sex and age are performed to CGs As the multifactor ANOVA of co-factor, the CGs is using loop_anova lmFit functions by means of for multiple check The CGs related to HCC that Bonferoni adjusts to screen to obtain.Multivariable is performed to the CG site related to HCC after screening Linear regression analysis, if come test using based on the lmFit functions of R language in linear regression model (LRM) with cell count, property Not, the age and it is addicted to drink as auxiliary variable, whether these correlations are also present.Use more different groups of differential methylations of Venny (relative to control) list of genes (http：//bioinfogp.cnb.csic.es/tools/venny/).Use the Pierre that subtracts one Gloomy correlation analysis carries out hierarchical clustering, and generate in the gene E applications of Bu Luode research institutes thermal map (http：// www.broadinstitute.org/cancer/software/GENE-E/).Then the β after the calibration to 350 CG sites Value perform multiple linear regression, its using group (HCC is to non-HCC), sex, alcohol, smoking, age and cell count as Auxiliary variable can distinguish HCC from all other group.Even if including other auxiliary variables in model, all CG sites are to each The auxiliary variable of group is still highly significant.After Bonferroni corrections are carried out to 350 measured values, 342 CG sites pair In group (HCC is to non-HCC) be still highly significant.Multiple-factor ANOVA analyses are performed, wherein the β value by 350 sites As reliable variable, and using group (HCC is to non-ANOVA), sex and age as non-reliable variable, to determine sex and group Not, age and group and it whether there is possible interaction in sex+DNA methylation between age and group.
But after Bonferroni corrections, group still keeps notable to all 350 CG sites, sex or age Do not find significantly to interact.Sum it up, these tables of data understand includes B-mode liver in HCC and other non-HCC patients There is sane DNA methylation difference between the scorching and PBMC DNA of hepatitis C.
Embodiment 3：By the Pearson's cluster analysis that subtracts one, specific DNA methylates mark cancer clinical progress by stages For predicting the unknown sample from patient, so as to detect the HCC cancers of early clinic progress by stages, and distinguish early clinic and enter The HCC cancers and chronic hepatitis of exhibition by stages
By comparing 10 normal healthy controls and 10 clinical progress specific HCC patient by stages, it is clinical to obtain each HCC The differential methylation site of progress by stages.Other clinical progress are by stages poor not by these with hepatitis B and hepatitis C sample The different CGs that methylates " training " (" training " is used for model to obtain differential methylation site), they " are handed over as unknown sample Fork checking " collects to explain problems with：First, from one clinical progress of cancer mark by stages whether can correctly by Do not classified by the HCC samples of these marks " training "Secondly, it is " trained " the DNA first for distinguishing HCC and normal healthy controls Whether base mark also can be distinguished HCC from hepatitis B and hepatitis CHCC and chronic hepatitis are distinguished for HCC Early diagnosis be a crucial challenge because it by development of chronic hepatitis is HCC that the HCC patient of significantly ratio, which is,.
By the Pearson's correlation analysis that subtracts one, hierarchical clustering is carried out to all HCC and hepatitis sample, wherein it is each individually point Analysis is methylated mark using one group of CG, the CG methylate mark by only test a HCC clinical progress by stages and Compare and be " discovered ".Other all clinical progress are by stages " naive " relative to these marks, and as " intersection is tested Card ".Cross validation refers to a kind of statistics strategy, a small group subset of its sample under study for action be used for " it was found that " mark list (predictive factor), (i.e. " cancer " and " control ") can will be distinguished from each other in it between two groups.The mark of these " being found " is subsequent It is tested as in other " new " samples of predictive factor under study for action.As shown in figures 4 to 7, each HCC independently obtained is specific The mark collection of clinical progress by stages is by " cross validation "；They correctly predict HCC, the sample bag from one group of sample Including " new " HCC and non-HCC cases, (Fig. 4 uses BLC-0 phase marks, and Fig. 5 uses BLC-A phase marks, and Fig. 6 uses the BLC-B phases Mark, Fig. 7 use BLC-C/D phases mark).Significantly, found by only comparing mono- stage of HCC and normal healthy controls CG marks correctly can predict HCC from one group of product comprising HCC and chronic hepatitis case.This is chronic hepatitis and cancer There is disease different DNA methylation spectrums to provide further evidence, and it can be used for predicting whether a patient is still chronic Hepatitis, or whether he/her have turned to HCC.It is interesting that identical mark can also be correctly predicted hepatitis B and Hepatitis C case (Fig. 4-7).
Usage charges She Er hypergeometries examine (p<1.921718e-297) show, each HCC clinical progress can be distinguished by stages Overlapping (Fig. 3 A) between the CG marks independently obtained, for each clinical progress by stages between all it is possible it is overlapping be aobvious Write.The independent weight using highly significant between each clinical progress obtained from only 10 cases and control by stages mark It is folded, the robustness of these marks is consumingly demonstrated, and show that these differential methylations CGs may be used as HCC peripheries mark Thing, it can be used for early detection.
Although existing between cancer difference clinical progress by stages differential methylation CGs very big overlapping, this is overlapping It is part.The present invention proves that we can be each to distinguish HCC using 350 CG list (as described above) (table 3) herein Clinical progress is by stages., can using this 350 CGs by the way that all samples are carried out subtracting one with the hierarchical clustering of Pearson's correlation analysis Correctly HCC cases to be classified according to clinical progress by stages, and hepatitis B and hepatitis C case be classified into it is strong Health compares.Although larger weight also be present between the otherness methylation sites of normal healthy controls and HCC differences clinical progress by stages Folded, the intensity of differential methylation strengthens as HCC is in progress.Therefore, the methylation level in this 350 CG sites can be used for Distinguish each clinical progress of HCC by stages.The device of the DNA methylation measured value of CG IDs comprising detection as described in table 3 and examination The kit of agent, it can be used for predicting hepatocellular carcinoma (HCC) each clinical progress by stages and chronic hepatitis.Pay attention to, DNA methylation mark The list of will thing as only healthier control and the single clinical progress of HCC by stages obtained from, but the list can be correct Predict that other " new " hepatitis B and hepatitis C case are non-HCC (Fig. 8) in ground.
The differential methylation CGs that present disclosure indicates the PBMC from HCC patient can be used for distinguishing specific face HCC and the control that bed is in progress by stages, and distinguish the HCC and chronic hepatitis patient of specific clinical progress by stages.
Embodiment 4：Specific C G's clinical progress is methylated mark by stages, and early clinic is distinguished by using penalized regression Progress HCC and late phase clinical progress HCC by stages by stages
As shown by data PBMC DNA methylations mark can distinguish different HCC clinical progress by stages.It is of the invention and then fixed Justice each clinical progress of differentiation HCC required minimal number of CG sites list by stages.Use the R for being fitted penalized regression model Wrap " penalized " and penalized regression (51) is carried out to 350 CG sites between different clinical progress sample by stages.R bags " penalized " uses likelihood cross validation, and each object come that reserves is predicted.The Model Identification of fitting is used In 8 CGs of prediction BLC-0 phases vs controls, for predicting 5 CGs of BLC-A phases vs controls, for distinguishing vs pairs of BLC-A phases According to 5 each CGs, for distinguishing 7 CGs of BLC-C/D phases vs controls, and be enough distinguish BLC-0 phases and hepatitis B 7 CGs (table 4).The following CGs of selection：8 CGs of BLC-0 phases and later stage BLC-A/B/C/D phases can be distinguished, and will BLC-0 phases and BLC-A phases, and can will be from whole early stages from 10 CGs of the interim differentiations of later stage BLC-B/C/D 7 CGs (table 4) distinguished in (BLC-0/A/B phases).31 CG stages-separators integration list (remove repeat after, Table 5) in the measured value of PBMC DNA methylations, by the Pearson's cluster that subtracts one can calculate to a nicety all HCC cases and Their clinical progress is by stages (Fig. 9).The dress of the DNA methylation measured value of CG IDs comprising detection as described in table 4 and table 5 The kit with reagent is put, can be used for predicting hepatocellular carcinoma (HCC) each clinical progress by stages.
Table 4. use penalized regression model, can by the HCC of different clinical progress by stages from control and hepatitis B and third The CG marks distinguished in type hepatitis
Table 5. uses penalized regression model, can be by the HCC of different clinical progress by stages from control, hepatitis B and the third type 31 CGs distinguished in hepatitis integration list (removing the CGs repeated)
cg14983135 cg10203922 cg05941376 cg14762436 cg12019814
cg03496780 cg02782634 cg27284331 cg23981150 cg14914552
cg13710613 cg23486701 cg11911769 cg14711743 cg15607708
cg14426660 cg18882449 cg02914652 cg15188939 cg12344600
cg21164050 cg03252499 cg03481488 cg04398282 cg11783497
cg20956548 cg22876402 cg24958366 cg11151251 cg06874640
cg16476382
Embodiment 5：Different carcinoma of the CG penalized regressions model with 100% specificity and sensitivity in prediction unknown sample Disease clinical progress by stages in application
Then to other " naive " (fresh sample for being not used for finding mark) HCC cases, hepatitis B and hepatitis C Control, using model is punished obtained from being used to distinguish specific clinical progress by stages as CGs listed in table 4, to predict often Individual case is the possibility of HCC differences clinical progress by stages.The result of these analyses is as shown in Figure 10.The punishment model can be with 100% sensitivity and all clinical progress of 100% specificity predictions sample by stages.
Embodiment 6：DNA methylation mark, for distinguishing HCC and normal healthy controls by the DNA extracted by T cell
Multi-variables analysis shows, even if when the difference of cell count is taken into account, (the control and chronic of HCC and other groups Hepatitis) between the difference of PBMC DNA methylations still exist.Further, once being divided to determine particular cell types From and when reducing the complexity of cell composition (although T cell subtype still has heterogeneity), DNA between cancer and control Whether the difference to methylate can disappear, and analyze 10 patients being isolated from participate in this research 39 HCC patients and own (sample comes from each HCC clinical progress by stages to the difference of DNA methylation collection of illustrative plates between the T cell of normal healthy controls (n=10), such as In table 1 shown in explanation), to determine when particular cell types are separated and partly reduce the complexity of cell composition, cancer Whether the DNA methylation difference between disease and control can disappear.
T cell is separated using AntiCD3 McAb immunomagnetic beads (Dynabed life technologies), it is right to HCC and health using CHAMP bags The DNA methylation value of calibration according between carries out linear (melange effect) and returned, and it shows that in threshold value be p<1x10-7Shi You 24863 differential methylation sites.It is p in threshold value<1x10-7With Δ β>0.3,<370 sane difference first are filtered out when -0.3 Base CGs (table 6), and hierarchical clustering is performed to normal healthy controls and HCC T cells DNA by performing the Pearson's correlation analysis that subtracts one (Figure 11).All samples correctly can be categorized into two groups by this 370 CGs：HCC and control.Comprising detection such as institute in table 6 The CG IDs stated the device of DNA methylation measured value and the kit of reagent, it can be used for predicting each clinical progress of HCC by stages And chronic hepatitis.
Table 6. is derived from most significant 370 CG IDs lists of T cell, and it can be distinguished HCC and be good for by cell DNA Health compares
Embodiment 7：The DNA methylation mark found in T cell is used for the HCC for predicting " not training " and chronic hepatitis is suffered from Person
These can will distinguish 370 CG sites of HCC and normal healthy controls T cell, can be used for " untrained " Different chronic hepatitis and the PBMC samples (n=69) of normal healthy controls are classified.Cluster analysis as shown in figure 12, shows this The CG sites of 370 differential methylations in T cell DNA, can be by the DNA of individual HCC, hepatitis and normal healthy controls from PBMC Classified with 100% degree of accuracy.Therefore, the differential methylation CGs found by using T cell DNA, in different patients (29 different patients for suffering from HCC, 20 suffer from chronic hepatitis) have been worth to intersection by the DNA methylation measurement in PBMC Checking.
Embodiment 8：350 CG sites (table 3) and 31 CG sites (table 5) from PBMC DNA analyses are used to use T cell DNA predicts HCC cancers
The 350 CG sites obtained by analyzing PBMC DNA can be correctly by normal healthy controls and the T cell of HCC samples It is grouped (Figure 13 A).Overlapping (Fei Sheer, the p of highly significant between following two groups of significant CGs be present<1x10-7)：Pass through The notable CGs of normal healthy controls and HCC is distinguished using T cell DNA, and different HCC is distinguished by using PBMC DNA The clinical progress CGs (Figure 13 B) with control by stages.
The present invention is again showed that, 31 CGs (table 5) after penalized regression screening are carried out to PBMC DNA methylations measured value The T cell DNA methylation measured value of HCC patient and control can be divided exactly by using the Pearson's correlation analysis that subtracts one Group simultaneously determines HCC clinical progress (Figure 13 C) by stages.Even if these as shown by data are specific by separating when the complexity of cell type Cell type and when reducing, the DNA methylation difference of HCC and other sample rooms still has, and is these CGs and HCC And the association between their predicted value provides further " cross validation ".
Embodiment 9：HCC PBMC differential methylation gene is enriched with Ia classical path
HCC progress has extensive trace (complete genome DNA methylation profiles) (Fig. 1) in the group that methylates.For depth Enter the function marking for understanding differential methylation gene in PBMC and T cell from HCC patient, use Ingenuity paths point Analyse (IPA) and the list of genes that generation is analyzed by differential methylation is subjected to genome enrichment analysis.We are first to CGs dependency basis Because carrying out genome enrichment analysis, each clinical progress of the CGs and HCC shows cutting edge aligned phase in Pearson's correlation analysis by stages Close (r>0.8；r<-0.8；Δβ>0.2, Δ β<- 0.2) (Fig. 1).It is apparent that the most upstream regulation and control of the gene related to these CGs Son is TGFbeta (p<1.09x10-17)、TNF(p<7.32x10-15), dexamethasone (p<7.74x10-12) and estradiol (p< 4x10-12), it is the principal immune inflammation and stress regulator of immune system.The highest disease of identification is that (p value is cancer 1x10-5To 2x10-51) and liver diseases (p<1.24x10-5To 1.11x10-25).Notice liver regeneration (p<6.19x10-1Extremely 1.11x10-25) and hepatocellular carcinoma (p<5.2x10-1To 3.76x10-25) strong signal.Inspection to otherness methylated genes Show：A large amount of representatives such as IL2, IL4, IL5, IL16, IL7, Il10, IL18, Il24, Il1B and Bai Jie of immune modulatory molecules Plain acceptor such as IL12RB2, IL1B, IL1R1, IL1R2, IL2RA, IL4R, IL5RA；Chemotactic factor (CF) such as CCL1, CCL7, CCL18, CCL24, and chemokine receptors such as CCR6, CCR7 and CCR9；Cell receptor such as CD2, CD6, CD14, CD38, CD44, CD80 and CD83；TGFbeta3 and TGFbetaI, NFKB, STT1, STAT3 and TNFa.
Comparison between PBMC and T cell differential methylation gene IPA analysis show NFKB, TNF, VEGF, IL4 and NFAT is as common upstream regulator.Sum it up, HCC PBMC and the DNA methylation of T cell, which change, shows immunoregulation There is strong mark in function.The otherness being previously described between HCC and non-cancerous liver tissue, which methylates, to be opened Mover (16,58).The present invention determines HCC cancer biopsy (1983 promoters) and PMNC (545 Individual promoter) between the promoter that methylates of otherness with the presence or absence of overlapping, find to have the overlapping of 44 promoters, its Fei Sheer hypergeometries are not statistically significant (p=0.76) in examining.These as shown by data, see in PMNC The DNA methylation reacting condition observed the change of HCC immune systems, and these differential methylations CGs is not probably The trace of Circulating DNA from tumour, or " substitute " of the DNA methylation change occurred in tumour.The application of these paths Fresh target is provided for the treatment of cancer to targeting peripheral immune system.
Embodiment 10：By carrying out pyrosequencing to differential methylation CGs to predict HCC and cancer
Pyrosequencing is performed using PyroMark Q24 machines, is usedQ24 softwares (Qiagen) are analyzed As a result.All data are expressed as the standard error (SEM) of average value ± average value.Statistical analysis is carried out using R language.With Table 7 is listed in the primer of analysis.
Table 7. is used for HCC predictive factors：AHNAK, SLFN2L, AKAP7, STAP1 pyrosequencing
Collect (replication set) for replicating, the present invention reduces cell composition problem using T cell DNA.It is multiple System collection includes 79 people, wherein 10 normal healthy controls, hepatitis B, hepatitis C and each 10 people of 3 carcinoma stages and 19 BLC-0 phases sample (table 2).Find after testing, finding to concentrate T cell methylation differential of the following gene in contrasting with HCC Highly significant：STAP1 (cg04398282) (being included in table 6), AKAP7 (cg12700074), SLFNL2 (cg00974761), And include hypomethylation gene in an other HCC：Neuroblast differentiation GAP-associated protein GAP (AHNAK) (cg14171514)。
All against the line of (health and hepatitis B and hepatitis C) between HCC BLC-0 phases, BLC-A phases (0+A) Property return the HCC BLC-0 phases that show, the BLC-A phases and all 4 CG have after multiple testing adjustment it is significant related (STAP1 p=4.04x10-7；AKAP7 p=.0.046；SLFNL2 p=0.012；AHNAK p=0.003436).It is all right According to and all clinical progress of HCC by stages between linear regression indicate after multiple testing adjustment, STAP1 (p= 6.6x10-6) and AHNAK and HCC (p=0.026) have it is significant related.
ANOVA analyses show all 4 CG of checking in control group (normal healthy controls and hepatitis B and hepatitis C) With early stage HCC group (BLC-0+A；1st, there is significant difference between methylating 2).All against the group with whole HCC it Between comparison show the significant differences of following gene methylations：STAP1 (p=1.7x10-6), AKAP7 (p=0.042), AHNAK (p=0.0062), but SLFNL2 difference be have certain trend but not significantly (p=0.071).ANOVA is shown STAP1 methylates the active effects (F=10.017 of diagnosis；P=7.49x10-6)。
For 5 kinds of control different diagnosis subgroups (normal healthy controls, chronic hepatitis B and chronic hepatitis C) and early Paired analysis after phase HCC (stage 1 and 2 or BLC-0 and BLC-A) multiple testing adjustment shows, the BLC-0 phases (BCLC 0) HCC and normal healthy controls (p=0.00037), chronic hepatitis B (p=0.00849), chronic hepatitis C (p=0.00698) Significant difference between one of them, and BLC-A phases (BCLC A) and normal healthy controls (p=0.00018), hepatitis B (p= 0.00670), the significant difference between one of hepatitis C (p=0.00534).Although there is diagnosis shadow in following genes Ring：SLFN2L methylates (F=3.9376；P=0.00810) AHNAK (F=3.0219；) and AKAP7 (F=p=0.02809 3.4；P=0.01633), between different diagnosis subgroups in contrast with it is less significant.
This 4 CG sites of these as shown by data can be used for predicting stage early stage HCC, and they are distinguished from control Come (Figure 14).
Embodiment 11：It was found that differential methylation CGs lists analyzed by Receiver Operating Characteristics (ROC) for predicting HCC；STAP1 embodiment
Receiver Operating Characteristics (ROC) are measured as to the diagnostic value of biomarker, it is (true to find that it measures " sensitivity " Fraction) function as " specificity " (wig existing fraction).ROC examines one threshold value (i.e. specific CG ratio that methylates of measure Example), it can provide most accurate prediction (highest score of " true to find ", and the minimum number of " wig is existing ") (59) (figure 15)。
The DNA methylation of each sample level is contrasted with threshold value DNA methylation value, then sample is classified as pair According to or HCC.The First Determination of the present invention Illumina 450K beta values of the calibration of the T cell from normal healthy controls and HCC ROC features (Figure 15 A).STAP1 genes cg04398282 shows as perfect biomarker.When threshold value DNA methylation (any sample with much higher value is classified as HCC, any sample quilt with the value less than 0.757 when beta values are 0.757 It is classified as control), the degree of accuracy for identifying the HCC samples of (calling) is 100%, and TG-AUC (AUC) is 1, its sensitivity It is 100% with specificity.STAP1 biomarkers are the DNA methyl by comparing the T cell from HCC and normal healthy controls Change and find.We therefore can by using the beta values of the calibration from PBMC DNA samples come check ROC features from And cross validation STAP1 cg04398282 biomarker characteristic, the sample include hepatitis B and hepatitis C and 29 extra HCC patients, the HCC patient are not included in the analysis of T cell DNA methylation (Figure 15 B).Use PBMC DNA predict that the degree of accuracy of all HCC samples (whole clinical progress are by stages) is 96%, are using threshold value beta values 0.6729 and AUC is 0.9741379 (sensitivity is 0.975 and specificity is 0.973).
The pyrosequencing value for concentrating STAP1 using T cell DNA replication dna checks ROC features (Figure 16).STAP1 CG first Base value passes through the quantification of generally lower than Illumina 450K values in pyrosequencing site.In STAP1 cg04398282 DNA At the threshold value 40.2% to methylate, by the standard of HCC identifications from all other control (health and hepatitis B and hepatitis C) Exactness is 82.2%.For distinguish HCC and all controls TG-AUC (AUC) for 0.8 (85% sensitivity, and 73% specificity) (Figure 16 A).When STAP1 cg04398282 threshold value is 50.12%, by the HCC BLC-0 phases from all The degree of accuracy distinguished in control is 83.6%, and AUC is 0.89 (84% sensitivity and 83% specificity).Threshold value When methylation level is 47.2, the degree of accuracy that the HCC BLC-0 phases are distinguished from normal healthy controls is 93% (Figure 16 A), and AUC is 0.94 (94% sensitivity and 94% specificity) (Figure 16 B).Sum it up, STAP1 shows that the single core of HCC peripheral bloods is thin DNA methylation mark in born of the same parents can be used for distinguishing the BLC-0 phases from chronic hepatitis and normal healthy controls, and it is that liver cancer is early The key obstacle of phase diagnosis.STAP1 is identified using T cell DNA, and it is confirmed (Figure 14) by replicating concentration.
The method used herein for being used to measure DNA methylation only there is provided an example, not exclude other The method for measuring DNA methylation.Sufficiently may be used it will be noted that those skilled in the art can use many known fields to disclose Receive and the method that uses measures STAP1 and other differential methylation sites DNA methylation, such as Illumina 850K Experiment, the mass spectrometry based on such as Epityper (Seqenom) method, the PCR amplifications using Methylation-specific primer (MS-PCR), high-resolution dissolving (HRM), the restriction enzyme of DNA methylation sensitiveness and bisulfite sequencing.
The application of the present invention
The application of the present invention is related to the molecular diagnosis field of HCC and cancer.Those skilled in the art can use the present invention Obtain the similar biomarker of other cancers.Also, the gene and path obtained by these genes, by using embodiment Target listed by 9, it can instruct to concentrate on novel drugs in the immune system of periphery.Research of the DNA methylation in cancer at present Have been focused into the Tumour DNA (5,6) of tumour, tumor microenvironment (8,9) and circulation, and it is main be made that in this respect into Exhibition.However, on whether there is the mechanism for the system scope that can inform our diseases in host system and/or may be used as cancer The problem of DNA methylation change of the non-intrusion type predictive factor of disease, however it remains.HCC is an absorbing example, because Continually can be in progress (2) from the chronic hepatitis and hepatic sclerosis first deposited for it, and the solution problem can be provided can The clinical example of tracking.Present invention demonstrates that the quality of host immune system may defines clinical appearance and the track of cancer.
Importantly, present invention demonstrates that HCC BLC-0 phases and chronic hepatitis the B-mode clear and definite border between the third type can With for early stage from diagnosing chronic hepatitis to HCC change, as illustrated in the embodiment of the invention.The present invention, which again shows that, how will The present invention is for distinguishing several clinical progress of cancer by stages.All experiments are required to sample known to one group and unknown sample Product, the known sample have the CG IDs disclosed by the invention value that methylates, and it is using hierarchical clustering, ROC or punishes back Come back training pattern；Unknown sample will be analyzed using these models described in the embodiment of the present invention.
The present invention refer to the fact that different dependent claims, be not meant to that those skilled in the art can not use Cancer is predicted in the combinations of these claims.Herein disclosed for measurement, statistical analysis and the cancer clinical for predicting cancer Be in progress the embodiment with chronic hepatitis by stages, it is impossible to is considered limitation of the present invention.In order to measure the DNA methyl of cancer patient Change, other modifications are will be obvious to those skilled in the art that such as Illumina 850K are tested, captured micro- battle array Row sequencing, sequencing of future generation, methylation status of PTEN promoter, epitype, the analysis based on restriction enzyme, and other well known field are found Method.Similarly, except listed herein, known field presence largely can predict Patient Sample A's using the present invention The statistical method of cancer.
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Claims (20)

1. the DNA methylation mark of cancer patient's PMNC (PBMC) for predicting cancer, the DNA first Base mark is to be methylated as complete genome DNA obtained from mapping (mapping) method, such as Illumina450K Or 850K experiments, the sequencing of full-length genome bisulfite, methylate DNA co-immunoprecipitation (MeDIP) sequencing or oligonucleotides are micro- Hybridization array.
2. DNA methylation mark as claimed in claim 1, wherein, the DNA methylation mark is source as follows From PBMC DNA CG IDs, the DNA methylation that it is used for by using the CG IDs in PBMC or T cell is horizontal next Predict hepatocellular carcinoma (HCC) clinical progress by stages and chronic hepatitis：
3. DNA methylation mark as claimed in claim 1, wherein, the DNA methylation mark is source as follows From the CG IDs of T cell, it is used to predict by using DNA methylation levels of the CG IDs in PBMC or T cell HCC clinical progress is by stages and chronic hepatitis：
4. DNA methylation mark as claimed in claim 1, wherein, the DNA methylation mark is CG as follows IDs, it is used to predict that different HCC are clinical by using DNA methylation measured values of the CG IDs in T cell or PBMC It is in progress by stages, wherein the CG IDs are obtained using statistical model such as penalized regression or cluster analysis,
For distinguishing the target CG IDs in HCC stages 1 (BCLC-0 phases) and control：cg14983135、cg10203922、 cg05941376、cg14762436、cg12019814、cg14426660、cg18882449、cg02914652；
For distinguishing the target CG IDs in HCC stages 2 (BCLC-A phases) and control：cg05941376、cg15188939、 cg12344600、cg03496780、cg12019814；
For distinguishing the target CG IDs in HCC stages 3 (BCLC-B phases) and control：cg05941376、cg02782634、 cg27284331、cg12019814、cg23981150；
For distinguishing the target CG IDs in HCC stages 4 (BCLC-C/D phases) and control：cg02782634、cg05941376、 cg10203922、cg12019814、cg14914552、cg21164050、cg23981150；
For distinguishing the target CG IDs of HCC BCLC-0 phases and hepatitis B：cg05941376、cg10203922、 cg11767757、cg04398282、cg11151251、cg24742520、cg14711743；
For distinguishing the target CG IDs of HCC BCLC-0 phases and BCLC-A/B/C/D phases：cg03252499、cg03481488、 cg04398282、cg10203922、cg11783497、cg13710613、cg14762436、cg23486701；
For distinguishing the target CG IDs of HCC BCLC-A phases and BCLC-B/C/D phases：cg02914652、cg03252499、 cg11783497、cg11911769、cg12019814、cg14711743、cg15607708、cg20956548、cg22876402、 cg24958366；
For distinguishing the target CG IDs of HCC BCLC-0/A/B phases and BCLC-C/D phases：cg02782634、cg11151251、 cg24958366、cg06874640、cg27284331、cg16476382、cg14711743。
5. DNA methylation mark as claimed in claim 1, wherein, the DNA methylation mark is CG as follows IDs, it is used to predict HCC clinical progress by using DNA methylation measured values of the CG IDs in T cell or PBMC By stages, the CG IDs are obtained using statistical model such as penalized regression or cluster analysis,
6. the kit for predicting cancer, it is characterised in that comprising for detecting DNA methylation as claimed in claim 1 The device and reagent of the DNA methylation measured value of mark.
7. for predict hepatocellular carcinoma HCC clinical progress by stages or chronic hepatitis kit, it is characterised in that comprising for examining Survey the device and reagent of the DNA methylation measured value of DNA methylation mark as claimed in claim 2.
8. for predict HCC clinical progress by stages or chronic hepatitis kit, it is characterised in that comprising for detecting such as right It is required that the device and reagent of the DNA methylation measured value of DNA methylation mark described in 3.
9. for predicting the kit of HCC differences clinical progress by stages, it is characterised in that comprising for detecting such as claim 4 The device and reagent of the DNA methylation measured value of described DNA methylation mark.
10. for predicting the kit of HCC clinical progress by stages, it is characterised in that comprising for detecting as claimed in claim 5 DNA methylation mark DNA methylation measured value device and reagent.
11. gene pathway, it is characterised in that the gene pathway is in the peripheral blood immune system of cancer by commitment.
12.DNA pyrosequencings methylate application of the experiment in HCC is predicted, it is characterised in that by using such as claim DNA methylation mark any one of 1-5, such as use following primer for following genes：
AHNAK (outer positive GGATGTGTCGAGTAGTAGGGT, it is outer reverse
CCTATCATCTCCACACTAACGCT, interior positive TGTTAGGGGTGATTTTTAGAGG, it is interior reverse
ATTAACCCCATTTCCATCCTAACTATCTT, and sequencing primer
TTTTAGAGGAGTTTTTTTTTTTTA)；
SLFN2L (outer positive GTGATYTTGGTYAYTGTAAYYT, it is outer reverse
TCTCATCTTTCCATARACATTTATTTAR, interior forward direction
AGGGTTTYAYTATATTAGYYAGGTTGG, interior reverse ATRCAAACCATRCARCCCTTTTRC, sequencing primer YYYAAAATAYTGAGATTATAGGTGT)；
AKAP7 (outer positive TAGGAGAAAGGGTTTATTGTGGT, it is outer reverse
ACACACCCTACCTTTTTCACTCCA, interior forward direction
GGTATTGATTTATGGTTAGGGATTTATAG, it is interior reverse
AAACAAAAAAAACTCCACCTCCAATCC, sequencing primer GGGATTTATAGTTTTGTGAGA)；And
STAP1 (outer positive AGTYATGTYTTYTGYAAATAAAAATGGAYAYY, it is outer reverse
TTRCTTTTTAACCACCAACACTACC, interior forward direction
YYGTTTYTTTYATYTTYTGGTGATGTTAA, it is interior reverse
ARARRRCAATCTCTRRRTAATCCACATRTR, sequencing primer
GGTGATGTTAATYTTYTGTTTA)。
13. Receiver Operating Characteristics (ROC) test the application in HCC is predicted, it is characterised in that by using such as claim DNA methylation mark any one of 1-5, the DNA methylation mark are, for example, STAP1 (cg04398282).
14. hierarchical clustering analyzes the application in HCC is predicted, it is characterised in that by using such as any one of claim 1-5 Described DNA methylation mark.
A kind of 15. method for identifying the DNA methylation mark for predictive disease, it is characterised in that comprising to being obtained from sample The DNA methylation measured value arrived performs the step of statistical analysis.
16. method as claimed in claim 15, it is characterised in that the DNA methylation measured value is by being carried in sample The DNA taken performs Ilumina Beadchip 450K or 850K and tests what is obtained.
17. method as claimed in claim 15, it is characterised in that the DNA methylation measured value is by being carried in sample The DNA taken performs DNA pyrosequencings, based on mass spectrometry (EpityperTM) or the PCR experiment that methylates obtain.
18. method as claimed in claim 15, it is characterised in that it is related that the statistical analysis includes Pearson.
19. method as claimed in claim 15, it is characterised in that the statistical analysis includes Receiver Operating Characteristics (ROC) Experiment.
20. method as claimed in claim 15, it is characterised in that the statistical analysis includes hierarchical clustering analysis experiment.
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