CN113454219B - Methylation marker for liver cancer detection and diagnosis - Google Patents
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Abstract
Methylation markers for liver cancer detection and diagnosis. 51 differential methylation regions are provided for diagnosis or aiding in diagnosis of cancer. The method provides an accurate, simple and economical early screening means for liver cancer, can improve the detection rate of liver cancer, especially early liver cancer, in high-risk liver cancer groups and common physical examination groups, further improves the survival rate of liver cancer patients, saves a great deal of medical expenditure and reduces medical burden.
Description
Technical Field
The invention relates to the biomedical field, in particular to a methylation marker for liver cancer detection and diagnosis.
Background
Liver cancer is one of cancers with high morbidity and mortality in the world, and the incidence of liver cancer in China is particularly serious, and more than 50% of liver cancer in the world occurs in China. At present, screening means of liver cancer mainly comprise serum Alpha Fetoprotein (AFP) detection and ultrasonic imaging detection, but the methods have the problems of low sensitivity or insufficient specificity to early liver cancer, imaging detection is more limited by factors such as experience of a checking doctor, performance of a detection instrument and the like, most of liver cancer is already late at present, and treatment and prognosis of late liver cancer are poor, and five-year survival rate of patients is poor. Therefore, the establishment of an accurate, simple and economical early screening method for liver cancer has great significance.
When cells in the human body break or die, the DNA is released into the circulatory system, namely, free DNA (cfDNA). Likewise, when tumor cells rupture or die, circulating tumor DNA (circulatingtumorDNA, ctDNA) is also released, which carries genetic information about the tumor cells. By detecting ctDNA mixed in cfDNA and analyzing the mutation carried by the ctDNA and epigenetic information, the possibility of cancer of a subject can be deduced.
Scientists developed a number of high-throughput sequencing technologies with high molecular utilization, which made it possible to detect trace amounts of mutation signals in plasma free DNA, and also motivated the development of accurate tumor early sieves. Scientists first search biomarkers suitable for early screening of tumors from gene mutation, but researches show that the effect of early screening of tumors by singly adopting mutation signals is limited. Therefore, scientists began early screening for tumors from the epigenetic point of view. DNA methylation is an important gene expression control mechanism, can regulate gene expression and silencing, and has a great influence in the occurrence and development of tumors. Abnormal methylation of cancer-associated genes often occurs early in the occurrence of cancer, and DNA methylation signatures are therefore considered potential tumor early screening markers.
The method is seen from the company which is focused on the research of the tumor early screening technology at home and abroad: currently, the main research techniques adopted by Grail are cfDNA targeted sequencing, WGS and WGBS, and tumor-specific mutation and methylation molecular markers are mined by whole genome sequencing of a large number of cancer samples and non-cancer control samples. The strategy can be used for researching the tumor genome map more comprehensively, but the huge cost caused by high-depth whole genome sequencing is not borne by a common research unit. Guardant Health is focused on liquid biopsy technology, and a high-sensitivity detection technology is adopted to conduct early tumor screening research, but many limitations still exist in the use of liquid biopsy technology for early tumor screening, such as: early tumor mutation signals are extremely weak, partial gene mutation exists in a plurality of different cancer species, and clonal hematopoiesis can cause great interference to ctDNA detection and the like. Therefore, the effect of singly adopting mutation as a molecular marker is limited to a large extent. The ubiquitin is detected by combining mutation with a protein marker, and the study also shows that the detection performance can be effectively improved by using multiple genealogy for detection. far gene and reference medical treatment focus on methylation detection, and DNA methylation changes are often caused by simultaneous occurrence of multiple sites, so that the sensitivity is higher than that of gene mutation of a single site, and the tissue specificity of DNA methylation signals enables early screening of a tumor of a cancer-bearing species, so that methylation is an ideal molecular marker for early screening of the tumor.
At present, a plurality of early screening researches on tumors based on DNA methylation exist at home and abroad, such as the hong Kong famous molecular biology clinical application specialist Luming of China, which is known as a foundation for noninvasive DNA prenatal detection, published in 2019 the condition of using low-depth WGBS sequencing to detect methylation and copy number variation (CNA) in urine cfDNA, and the method is used for detecting bladder cancer with the sensitivity reaching 93.5 percent (specificity 95.8 percent); as in Anderson, b.w. et al, 2018, which first found candidate DNA methylation markers related to gastric cancer from the DNA methylation panel, and then tested a number of samples using methylation-specific PCR (MSP) method, finally obtained a panel (ELMO 1, ZNF569, C13orf 18) comprising 3 markers, the sensitivity of which reached 86% (specificity 95%, CI 71-95%). More and more research reports prove the great potential of DNA methylation markers in the field of early tumor screening, and on the basis of a great number of researches, development of a methylation-based 'portable' detection method can accelerate the transformation process of the early tumor screening into clinical industry.
The flow chart of the existing liver cancer screening scheme (Wei Jian Committee published "diagnosis and treatment Specification for primary liver cancer (2019 edition)) is shown in FIG. 1. At present, screening means of liver cancer mainly comprise serum Alpha Fetoprotein (AFP) detection and ultrasonic imaging detection. The methods have the problems of low sensitivity or insufficient specificity to early liver cancer, and the imaging detection is more limited by the experience of a checking doctor, the performance of a detection instrument and other factors, and most of the liver cancers are already late at present, and the treatment and prognosis of the late liver cancer are poorer, so that the five-year survival rate of patients is poorer.
DISCLOSURE OF THE INVENTION
In order to effectively solve the problem that the incidence rate and death rate of liver cancer are high, early liver cancer is asymptomatic, and a patient is often diagnosed to be in middle and late stages, so that the five-year survival rate is greatly reduced; at present, screening means of liver cancer are single and limited, most of the screening means depend on ultrasonic imaging means, and are not sensitive to small liver cancer tissues; the invention provides a methylation marker for liver cancer detection and diagnosis, which is a unique universal blood marker serum Alpha Fetoprotein (AFP) for liver cancer screening at present and has low sensitivity or insufficient specificity to early liver cancer and can not meet the requirement of large-scale liver cancer screening.
In a first aspect, the invention claims a Differential Methylation Region (DMR) set.
The set of differential methylation regions claimed in the present invention contains all or part of the following 51 differential methylation regions (specifically as shown in table 1):
(A1) At position 22140769-22140997 of chromosome 1;
(A2) At position 47909518-47911295 of chromosome 1;
(A3) At position 119522233-119522972 of chromosome 1;
(A4) At position 119525991-119526101 of chromosome 1;
(A5) At position 119526727-119527757 of chromosome 1;
(A6) At position 119531595-119533069 of chromosome 1;
(A7) At position 119535537-119535986 of chromosome 1;
(A8) At position 119542942-119543424 of chromosome 1;
(A9) At position 119549096-119550717 of chromosome 1;
(A10) At position 197882364-197882519 of chromosome 1;
(A11) At position 26624440-26625280 of chromosome 2;
(A12) At position 63282623-63283168 of chromosome 2;
(A13) At position 63283795-63284165 of chromosome 2;
(A14) At position 162279905-162280539 of chromosome 2;
(A15) At position 200326591-200327369 of chromosome 2;
(A16) At position 200333453-200333973 of chromosome 2;
(A17) At position 125075832-125076480 of chromosome 3;
(A18) At position 170137150-170137931 of chromosome 3;
(A19) Positions 995761-996936 on chromosome 4;
(A20) 41875340-41875925 to chromosome 4;
(A21) At position 139047739-139048298 of chromosome 5;
(A22) At position 1624936-1625224 of chromosome 6;
(A23) At position 26271346-26271748 of chromosome 6;
(A24) At position 108488594-108488844 of chromosome 6;
(A25) At position 108492267-108492437 of chromosome 6;
(A26) At position 150285813-150286646 of chromosome 6;
(A27) At position 27207996-27208054 of chromosome 7;
(A28) At position 96636496-96636870 of chromosome 7;
(A29) At position 129418361-129418612 of chromosome 7;
(A30) 17271051-17271340 to chromosome 8;
(A31) 67873733-67874151 to chromosome 8;
(A32) 99961175-99961661 to chromosome 8;
(A33) 99985934-99986482 to chromosome 8;
(A34) 100616319-100616730 to chromosome 9;
(A35) 93646929-93647266 to chromosome 10;
(A36) 134597818-134599519 to chromosome 10;
(A37) 69517700-69518306 to chromosome 11;
(A38) 58021614-58021842 to chromosome 12;
(A39) 81102127-81102896 to chromosome 12;
(A40) 102247495-102248194 to chromosome 14;
(A41) 76630449-76631040 to chromosome 15;
(A42) 29297770-29298669 to chromosome 17;
(A43) 43047552-43047830 to chromosome 17;
(A44) 75368790-75370662 to chromosome 17;
(A45) 76739367-76740382 to chromosome 18;
(A46) 12305592-12306084 to chromosome 19;
(A47) 13210026-13210503 to chromosome 19;
(A48) 15342716-15343266 to chromosome 19;
(A49) 15344024-15344364 to chromosome 19;
(A50) 50721097-50722014 to chromosome 20;
(A51) At position 38220548-38221506 of chromosome 22.
The physical location of the 51 differentially methylated regions was determined based on alignment of human whole genome sequences (version number hg 19).
Wherein the set of differential methylation regions includes, but is not limited to, selecting a subset of the foregoing (A1) - (a 51), small scale substitution, small scale addition, and the like.
Further, the set of differential methylation regions may consist of all or part of the 51 differential methylation regions shown in (A1) - (a 51) above.
In a specific embodiment of the present invention, the set of differential methylation regions consists of 51 differential methylation regions as shown in (A1) - (A51) above.
In a second aspect, the invention claims the use of the set of differentially methylated regions described hereinbefore as a methylation marker in any of:
(B1) Preparing a product for diagnosis or aiding diagnosis of cancer;
(B2) Diagnosing or aiding in the diagnosis of cancer;
(B3) Preparing a product for early warning of cancer prior to clinical symptoms;
(B4) Early warning of cancer prior to clinical symptoms;
(B5) Preparing a product for distinguishing or aiding in distinguishing between cancer and benign lesions;
(B6) Distinguishing or aiding in distinguishing between cancer and benign lesions.
In a third aspect, the invention claims the use of a substance for detecting the methylation level of a set of differential methylation regions as described hereinbefore in any of the following:
(B1) Preparing a product for diagnosis or aiding diagnosis of cancer;
(B2) Diagnosing or aiding in the diagnosis of cancer;
(B3) Preparing a product for early warning of cancer prior to clinical symptoms;
(B4) Early warning of cancer prior to clinical symptoms;
(B5) Preparing a product for distinguishing or aiding in distinguishing between cancer and benign lesions;
(B6) Distinguishing or aiding in distinguishing between cancer and benign lesions.
In a fourth aspect, the invention claims the use of a combination of a substance and a medium in any of the following:
(B1) Preparing a product for diagnosis or aiding diagnosis of cancer;
(B2) Diagnosing or aiding in the diagnosis of cancer;
(B3) Preparing a product for early warning of cancer prior to clinical symptoms;
(B4) Early warning of cancer prior to clinical symptoms;
(B5) Preparing a product for distinguishing or aiding in distinguishing between cancer and benign lesions;
(B6) Distinguishing or aiding in distinguishing between cancer and benign lesions;
the substance is a substance for detecting the differential methylation region group described above;
the medium is stored with a cancer risk prediction model construction and use method;
the construction and use method of the cancer risk prediction model comprises the following steps:
(C1) Constructing a training set comprising methylation level data for the set of differential methylation regions described previously from n1 cancer patient samples and n2 non-cancer patient samples;
(C2) A machine learning method is adopted to construct a cancer risk prediction model, and then the cancer risk prediction model is used for realizing diagnosis or auxiliary diagnosis of cancer, early warning of cancer before clinical symptoms and/or distinguishing or auxiliary distinguishing of cancer and benign lesions.
In (C1), n1 and n2 may be positive integers of 96 and 54 or more, respectively, as follows.
Wherein the substance for detecting the set of differential methylation regions may comprise a bisulfite reagent.
In a fifth aspect, the invention claims the use of a medium storing a method of cancer risk prediction model construction and use in any of the following:
(B1) Preparing a product for diagnosis or aiding diagnosis of cancer;
(B2) Diagnosing or aiding in the diagnosis of cancer;
(B3) Preparing a product for early warning of cancer prior to clinical symptoms;
(B4) Early warning of cancer prior to clinical symptoms;
(B5) Preparing a product for distinguishing or aiding in distinguishing between cancer and benign lesions;
(B6) Distinguishing or aiding in distinguishing between cancer and benign lesions;
the construction and use method of the cancer risk prediction model comprises the following steps:
(C1) Constructing a training set comprising methylation level data for the set of differential methylation regions described previously from n1 cancer patient samples and n2 non-cancer patient samples;
(C2) A machine learning method is adopted to construct a cancer risk prediction model, and then the cancer risk prediction model is used for realizing diagnosis or auxiliary diagnosis of cancer, early warning of cancer before clinical symptoms and/or distinguishing or auxiliary distinguishing of cancer and benign lesions.
In a sixth aspect, the invention claims a kit.
The kit claimed by the invention can be any one of the following:
kit I: comprising:
(a1) A bisulfite reagent; and
(a2) A control nucleic acid comprising a sequence from the set of differential methylation regions described above and having a methylation state associated with a non-cancer patient.
A kit II comprising:
(b1) A bisulfite reagent; and
(b2) A control nucleic acid comprising a sequence from the set of differentially methylated regions described previously and having a methylation state associated with a cancer patient.
Kit III, containing a substance for detecting the set of differentially methylated regions described above and a medium storing a method of cancer risk prediction model construction and use;
the construction and use method of the cancer risk prediction model comprises the following steps:
(C1) Constructing a training set comprising methylation level data for the set of differential methylation regions described previously from n1 cancer patient samples and n2 non-cancer patient samples;
(C2) A machine learning method is adopted to construct a cancer risk prediction model, and then the cancer risk prediction model is used for realizing diagnosis or auxiliary diagnosis of cancer, early warning of cancer before clinical symptoms and/or distinguishing or auxiliary distinguishing of cancer and benign lesions.
Wherein the substance for detecting the set of differential methylation regions may comprise a bisulfite reagent.
In a seventh aspect, the invention claims a system.
The claimed system includes:
(D1) Reagents and/or instrumentation for detecting the methylation level of the set of differentially methylated regions described hereinbefore;
wherein the reagent for detecting the methylation level of said set of differential methylation regions may comprise a bisulfite reagent.
(D2) A device comprising a unit X and a unit Y;
the unit X is used for establishing a cancer risk prediction model and comprises a data acquisition module and a data analysis processing module;
the data acquisition module is used for acquiring methylation level data of the differential methylation region group, which are obtained by (D1) detection and are from n1 cancer patient samples and n2 non-cancer patient samples;
the data analysis processing module can take methylation level data, which are acquired by the data acquisition module and are used for the differential methylation region group, of n1 cancer patient samples and n2 non-cancer patient samples as a training set, and a cancer risk prediction model is constructed based on a machine learning method principle;
the unit Y enables diagnosis or assisted diagnosis of cancer, early warning of cancer before clinical symptoms and/or differentiation or assisted differentiation of cancer and benign lesions based on the cancer risk prediction model and methylation level data for the set of differential methylation regions described hereinbefore from a sample of a subject.
In an eighth aspect, the invention claims the use of a kit as described hereinbefore or a system as described hereinbefore in any of the following:
(B1) Preparing a product for diagnosis or aiding diagnosis of cancer;
(B2) Diagnosing or aiding in the diagnosis of cancer;
(B3) Preparing a product for early warning of cancer prior to clinical symptoms;
(B4) Early warning of cancer prior to clinical symptoms;
(B5) Preparing a product for distinguishing or aiding in distinguishing between cancer and benign lesions;
(B6) Distinguishing or aiding in distinguishing between cancer and benign lesions;
(B7) Preparing a product for distinguishing or assisting in distinguishing liver cancer from liver cirrhosis;
(B8) Liver cancer and cirrhosis are distinguished or assisted.
In a ninth aspect, the invention claims a method of diagnosing or aiding in the diagnosis of cancer.
The method for diagnosing or assisting in diagnosing cancer as claimed in the present invention may comprise the steps of: the methylation status of the sample from the subject is analyzed for the set of differentially methylated regions described previously, thereby enabling diagnosis or aiding diagnosis of cancer.
Methylation status for the set of differential methylation regions that is different from that from a non-cancer patient sample is then considered to have or suspected of having cancer.
Further, the same methylation state as for the set of differential methylation regions from a cancer patient sample is considered to have cancer or suspected of having cancer.
In a specific embodiment of the present invention, the method for diagnosing or aiding in diagnosing cancer may comprise the steps of:
(C1) Constructing a training set comprising methylation level data for the set of differential methylation regions described previously from n1 cancer patient samples and n2 non-cancer patient samples;
(C2) And constructing a cancer risk prediction model by adopting a machine learning method, and then realizing diagnosis or auxiliary diagnosis of cancer by utilizing the cancer risk prediction model.
In a tenth aspect, the invention claims a method of pre-warning cancer prior to clinical symptoms.
The method for early warning cancer before clinical symptoms claimed by the invention can comprise the following steps: methylation status from a sample of a subject is analyzed for the set of differentially methylated regions described previously, thereby effecting early warning of cancer prior to clinical symptoms.
Methylation status for the set of differentially methylated regions, unlike samples from non-cancer patients, is considered to be a high risk cancer patient, and vice versa.
Further, the same methylation status as for the set of differentially methylated regions from a cancer patient sample is considered a high risk cancer patient and vice versa.
In a specific embodiment of the present invention, the method for early warning cancer before clinical symptoms may comprise the steps of:
(C1) Constructing a training set comprising methylation level data for the set of differential methylation regions described previously from n1 cancer patient samples and n2 non-cancer patient samples;
(C2) And constructing a cancer risk prediction model by adopting a machine learning method, and then utilizing the cancer risk prediction model to realize early warning of the cancer before clinical symptoms.
In an eleventh aspect, the invention claims a method of distinguishing or aiding in distinguishing between cancer and benign lesions.
The method of distinguishing or aiding in distinguishing between cancer and benign lesions as claimed in the present invention may comprise the steps of: analyzing the methylation status of the set of differentially methylated regions according to claim 1 or 2 from a sample of a subject to achieve differentiation or aiding in the differentiation of cancer and benign lesions.
Methylation status for the set of differentially methylated regions, unlike samples from non-cancer patients, is considered cancer and vice versa benign lesions.
Further, the same methylation status as for the set of differentially methylated regions from a cancer patient sample is considered cancer and vice versa benign lesions.
In a specific embodiment of the present invention, the method for distinguishing or aiding in distinguishing between cancer and benign lesions may comprise the steps of:
(C1) Constructing a training set comprising methylation level data for the set of differential methylation regions described previously from n1 cancer patient samples and n2 non-cancer patient samples;
(C2) A machine learning method is adopted to construct a cancer risk prediction model, and then the cancer risk prediction model is utilized to realize the differentiation or auxiliary differentiation of cancer and benign lesions.
In the foregoing aspects, the machine learning method may be a random forest method.
In a specific embodiment of the invention, the model is constructed specifically by python scikit-learn with the parameters: the maximum depth is 5, 50 trees, and the feature importance is >0.15.
In the foregoing aspects, the sample is a sample capable of extracting DNA.
Further, the sample includes, but is not limited to, plasma, tissue, saliva, urine, stool, and the like.
In the foregoing aspects, the method of analyzing the methylation status and the method of obtaining the methylation level data may each include, but are not limited to, bisulfite conversion (bisulfite conversion), PCR, methylation-specific PCR (MS-PCR), pyrosequencing (pyrosequencing), mulberry sequencing (Sanger sequencing), high throughput sequencing (High-throughput sequencing), or Third generation sequencing or single molecule sequencing (Third-generation sequencing), and the like.
In a specific embodiment of the invention, plasma free cells DNA (plasma cfDNA) are subjected to targeted methylation high throughput sequencing to obtain the methylation level data of the differential methylation region group described previously. The method specifically comprises the following steps: cfDNA is extracted from a plasma sample, a methylation library is constructed, library hybridization capture is carried out, and high-throughput sequencing is carried out.
Methods of analysis of these DMR methylation levels are associated with computer software and/or computer hardware, including, but not limited to, determining the methylation status of a DMR, comparing the methylation status of a DMR, generating a methylation standard curve, determining Ct values, calculating the methylation rate of a DMR, determining the specificity and/or sensitivity of an assay or label, calculating ROC curves and associated AUCs, sequence analysis, and the like.
In the foregoing aspects, the non-cancerous condition may be a healthy control or benign lesion patient. The healthy control is a physical examination sample without complaints of abnormalities.
In the foregoing aspects, the cancer includes, but is not limited to, liver cancer, colorectal cancer, lung cancer, stomach cancer, pancreatic cancer, or the like.
In a specific embodiment of the invention, the cancer is liver cancer. Accordingly, the benign lesions are benign lesions of the liver or cirrhosis. The benign lesions of the liver are in particular hepatic hemangiomas, hepatic abscesses, hepatic cysts or hepatic focal nodular hyperplasia.
In the foregoing aspects, the liver cancer may be primary hepatocellular carcinoma or intrahepatic cholangiocarcinoma. Further as primary hepatocellular carcinoma or intrahepatic cholangiocarcinoma without receiving any form of anti-tumor therapy.
In the foregoing aspects, the liver cancer may be BCLC staged as liver cancer of stage O, stage a, stage B and/or stage C.
In a specific embodiment of the present invention, the methylation level data described above is methylation rate. That is, the methylation level of the differential methylation region group is the ratio of methylated cytosines to all cytosines in CpG within the corresponding DMR region.
Drawings
Fig. 1 is a flowchart of a liver cancer screening scheme (Wei Jian commission published "diagnosis and treatment Specification for primary liver cancer (2019 edition)).
FIG. 2 shows 2658 Differential Methylation Regions (DMR) based on 44 findings of liver cancer tumor tissue and paracancerous tissue, of which 357 are hypermethylated (Hyper) DMR and 2301 are hypomethylated (Hypo) DMR.
Fig. 3 is a heat map of methylation rate in sample set I based on 51 model DMR.
Fig. 4 is the efficacy of a liver cancer methylation model based on 51 DMR in sample set I.
Fig. 5 is a heat map of methylation rate in sample set II based on 51 model DMR.
Fig. 6 shows the efficacy of a liver cancer methylation model based on 51 DMR in sample set II.
FIG. 7 is a comparison of the performance of a liver cancer methylation model based on 51 DMR with that of conventional AFP detection for liver cancer determination.
Best Mode for Carrying Out The Invention
Firstly, the invention finds 2658 differential methylation regions (Differentially methylated region, DMR) possibly related to the occurrence and development of liver cancer by carrying out methylation high-throughput sequencing on the liver cancer tissue and the liver cancer side tissue by carrying out analysis and calculation on data.
The present invention then collected methylation level data for these DMRs in cfDNA of liver cancer patients and healthy people by targeted methylation high throughput sequencing of 705 total plasma free cells DNA (plasma cfDNA) from 385 liver cancer patients, 259 healthy people, 36 liver benign lesions patients, 25 liver cirrhosis patients.
Finally, combining the collected data, the invention constructs a cancer risk prediction model through screening and machine learning under certain conditions, and selects 51 DMR as markers for screening liver cancer.
The following examples facilitate a better understanding of the present invention, but are not intended to limit the same. The experimental methods in the following examples are conventional methods unless otherwise specified. The test materials used in the examples described below, unless otherwise specified, were purchased from conventional biochemical reagent stores. The quantitative tests in the following examples were all set up in triplicate and the results averaged.
Example 1, discovery of 51 DMR's useful for screening liver cancer
This example describes the discovery of differential methylation regions (Differentially methylated region, DMR) associated with the development of liver cancer and markers useful therein as screening markers for liver cancer.
First, DNA extracted from frozen liver cancer tissue and from frozen paracancerous tissue was subjected to whole genome methylation sequencing (whole genome bisulfite sequencing, WGBS) at 44, and DMR associated with the occurrence and development of liver cancer was identified by calculation of data analysis.
Second, targeted methylation high throughput sequencing was performed on plasma free cells DNA (plasma cfDNA) from liver cancer patients and healthy people in sample set I, including cfDNA from 140 liver cancer patients and 84 healthy people. And then constructing a liver cancer risk model by utilizing machine learning, and screening out markers which can be used for screening liver cancer in the DMR. The standard of liver cancer sample group entry is: the pathologically diagnosed primary hepatocellular carcinoma or intrahepatic cholangiocarcinoma has no past history of malignant tumor, and is not subjected to any form of antitumor treatment before operation. Wherein the liver cancer samples in the 0 phase and the A phase account for more than 60% of the total liver cancer samples. Health is a physical examination sample without complaints of abnormalities.
Study subjects and samples: the study was approved by the ethical committee of the China university affiliated Zhongshan Hospital, complex denier university. Frozen liver cancer tissue, liver cancer patient blood plasma and healthy human blood plasma are all from the auxiliary Zhongshan hospital of the compound university.
1. DNA preparation
1. Tissue sample extraction
DNA extraction was performed on 44 pairs of liver cancer and liver cancer-side Tissue samples using DNeasy Blood & Tissue Ki (Qiagen, # 69506).
2. DNA fragmentation
200ng of extracted DNA is taken, 1ng Unmethylated lambda DNA (PROMEGA, #D1521) is added for subsequent C-U conversion quality control, an ultrasonic breaking instrument is used for breaking the DNA, and AMPure XP (AGENTURT, #A 63882) is used for selecting fragments of the DNA, so that the sizes of the DNA fragments are concentrated at about 100-300. The DNA fragmentation and the magnetic bead double selection are conventional experimental operations, and specific parameters can be adjusted according to the model difference of the breaking instrument and the official mesh specification.
3. Plasma sample extraction
cfDNA extraction was performed on liver cancer and healthy human plasma samples using MagPure Circulating DNA Maxi Kit (MAGEN, #12917 PJ-100). 10ng cfDNA was taken and 0.05ng of disruption was added and screened to approximately 160bp Unmethylated lambda DNA for subsequent C-U transformation quality control.
2. Library construction
Library construction was performed using MGIEasy whole genome methylation library preparation kit (MGI, # 1000005251), and "end repair & addition of dA tail", "linker ligation", "ligation product purification", "bisufite treatment and purification", "PCR amplification", "PCR product purification", and "PCR product quality inspection" steps were completed according to the instructions without the need for subsequent operations of the kit.
3. Library hybridization capture
Hybridization, capture and elution and PostPCR were performed using Seq Cap EZ Hybridization and Wash Kit (ROCHE, 5634253001) and SeqCap Epi CpGiant Enrichment Kit (ROCHE, 7138911001). Because of the sequencing instrument using the MGI platform, the Block used in the hybridization process and the PostPCR primers used in the PostPCR step were the Block and PostPCR primers corresponding to the MGI platform (both from the MGIEasy exome capture auxiliary kit, MGI, # 1000007743).
4. Sequencing
PE100 sequencing was performed using MGISEQ-2000 (MGI). The corresponding commercial kit (MGISEQ-2000 RS high throughput sequencing kit (FCL PE 100), 1000012552) may be purchased for sequencing operations or commissioned for sequencing by an organization providing sequencing services.
6. Results
The DNA extracted from the liver cancer tissues and the paracancerous tissues of 44 liver cancer patients is subjected to whole genome methylation group sequencing, and the sequencing data is analyzed to calculate the methylation rate of each CpG site. By comparing the difference of methylation rates of each CpG site in liver cancer tissues and cancer tissues, 2658 Differential Methylation Regions (DMR) possibly related to occurrence and development of liver cancer are distinguished by adopting a hierarchical Bayesian method, wherein 357 of the differential methylation regions are hypermethylation DMR.
The cfDNA from 140 liver cancer patients and 84 healthy people from sample set I in this example were targeted high throughput sequenced. The hypermethylated DMR was modeled using 10-fold cross validation based on random forests, and 51 DMR's were screened according to feature importance (feature importance > 0.15) as markers for liver cancer screening (Table 1). The model performance was evaluated using the validation set per compromise, resulting in a validation set with an average sensitivity of 0.929, a specificity of 0.894, and an auc of 0.96. The specific operation corresponding to the result is as follows: the samples were divided into 10 copies, with 90% of the samples used as training sets (for modeling) and 10% as validation sets (for validation models) in each compromise in 10 fold cross-validation, with each fold of test samples being different. The modeling is to calculate the depth of single CpG sites and the number of methylated cytosines obtained by targeting high-throughput sequencing of cfDNA (namely the ratio of methylated cytosines in CpG in the DMR region to all cytosines). The model was constructed by python scikit-learn with parameters: the maximum depth is 5, 50 trees, and the feature importance is >0.15.
Fig. 2 shows a thermal diagram of 2658 DMR based on 44 findings of liver cancer tissue and paracancerous tissue. Liver cancer tissue on the left side and paracancerous tissue on the right side. The hypermethylated DMR is located above the heat map and the hypomethylated DMR is located below the heat map. Each cell represents the corresponding DMR methylation rate of the sample at that site, ranging from 0 to 1. The closer the methylation ratio is to 0, the darker the color. As can be seen from the figure, the found DMR has a clear distinction between liver cancer tissue and paracancerous tissue.
Figure 3 shows a heat map of the methylation rate of 51 DMR in the region of 140 liver cancer patients and 84 healthy persons of sample set I. The horizontal axis is the sample, healthy people are on the leftmost side, followed by liver cancer patients. Vertical axis is DMR, hypermethylated DMR above, and hypomethylated DMR below. As can be seen, the 51 DMRs screened clearly differentiated liver cancer patients from healthy human samples.
Fig. 4 shows the performance of sample set I in this example based on a liver cancer methylation model of 51 DMR. The horizontal axis represents false positive rate (1-specificity) and the vertical axis represents sensitivity. It can be seen that the methylation model has good judgment for the sample of this example.
TABLE 151 DMR as marker for liver cancer screening
Note that: the physical positions in the table were determined based on alignment of human whole genome sequences (version number hg 19). The column "gene" indicates that the region is free of annotated genes.
Example 2, verification of 51 DMR in liver cancer screening
The main objective of this example was to verify the performance of these 51 DMR screening for liver cancer in sample set II, including plasma free DNA from 245 liver cancer patients, 36 liver benign lesions patients, 175 healthy people and 25 liver cirrhosis patients. The standard of liver cancer sample group entry is: the pathologically diagnosed primary hepatocellular carcinoma or intrahepatic cholangiocarcinoma has no past history of malignant tumor, and is not subjected to any form of antitumor treatment before operation. Wherein the liver cancer samples in the 0 phase and the A phase account for more than 60% of the total liver cancer samples. Benign lesions of the liver include hepatic hemangioma, liver abscess, hepatic cyst and hepatic focal nodular hyperplasia. Health is a physical examination sample without complaints of abnormalities. Sample set II does not contain any samples from sample set I in example I.
Study subjects and samples: as in example 1.
1. Method of
The performance of these 51 DMR screening liver cancer was verified in sample set II, directly verifying the corresponding model constructed in example 1.
2. Results
Table 2 shows the methylation median of 51 DMR in this model for liver cancer, healthy people, cirrhosis and benign lesions of liver in sample set II. As can be seen from the table, the selected DMR has a distinct differentiation between liver cancer and non-liver cancer samples.
Table 3 shows the performance of the model in sample set II. Wherein the sensitivity in liver cancer sample reaches 81.3%, the specificity in healthy people is 96.9%, and the specificity in liver cirrhosis and benign liver lesions reaches 90.5% and 73.5% respectively. The table shows that the model has good identification capability for liver cancer, healthy people and other liver lesions.
Table 4 shows the performance of the model at different stages of liver cancer (BCLC stage). The sensitivity of phase 0 was 51.8%, the sensitivity of phase a was 82.5%, the sensitivity of phase B was 91.7%, and the sensitivity of phase C was 100%. In conventional AFP assays, the sensitivities of phase 0, phase A, phase B and phase C were 29.6%,27.7%,55.0%,57.1%, respectively. As can be seen from the table, the performance of the model is obviously improved compared with that of the existing AFP.
FIG. 5 shows a thermal map of methylation rates in independent validation sample sets among methylation markers considered in this analysis. As can be seen, liver cancer is clearly distinguished from non-liver cancer.
Fig. 6 shows AUC of the liver cancer methylation rate model in sample set II.
FIG. 7 shows the performance of the methylation model in comparison with conventional AFP assays for liver cancer. The graph shows that the performance of the methylation model under each stage is obviously improved compared with that of the traditional AFP detection.
TABLE 2 methylation rate median at 51 DMR for different sample types in sample set II
TABLE 3 liver cancer prediction model Performance in sample set II
Note that: the open cell in the table indicates that this cell is not applicable, i.e., only liver cancer is sensitive and not liver cancer is specific.
TABLE 4 Performance of liver cancer prediction models at different stages
Note that: there were a total of 127 liver cancer samples with clinical staging information, and 109 of these 127 samples had AFP information.
Industrial application
The present invention provides 51 DMRs that can be used as screening for liver cancer (table 1). By detecting the methylation levels of the DMRs and analyzing the obtained data, the possibility of the liver cancer of the testee can be predicted, and the aim of screening the liver cancer in the common people or the high risk group of the liver cancer is fulfilled. The invention provides an accurate, simple and economical early screening method for liver cancer, which can improve the detection rate of liver cancer, especially early liver cancer in high-risk liver cancer groups and common physical examination groups, further improve the survival rate of liver cancer patients, save a great deal of medical expenditure and reduce medical burden.
Claims (4)
1. Use of a set of differentially methylated regions as methylation markers in any of:
(B1) Preparing a product for diagnosis or aiding diagnosis of cancer;
(B2) Preparing a product for early warning of cancer prior to clinical symptoms;
(B3) Preparing a product for distinguishing or aiding in distinguishing between cancer and benign lesions;
the differential methylation region group consists of 51 differential methylation regions as shown in (A1) - (a 51) below;
(A1) At position 22140769-22140997 of chromosome 1;
(A2) At position 47909518-47911295 of chromosome 1;
(A3) At position 119522233-119522972 of chromosome 1;
(A4) At position 119525991-119526101 of chromosome 1;
(A5) At position 119526727-119527757 of chromosome 1;
(A6) At position 119531595-119533069 of chromosome 1;
(A7) At position 119535537-119535986 of chromosome 1;
(A8) At position 119542942-119543424 of chromosome 1;
(A9) At position 119549096-119550717 of chromosome 1;
(A10) At position 197882364-197882519 of chromosome 1;
(A11) At position 26624440-26625280 of chromosome 2;
(A12) At position 63282623-63283168 of chromosome 2;
(A13) At position 63283795-63284165 of chromosome 2;
(A14) At position 162279905-162280539 of chromosome 2;
(A15) At position 200326591-200327369 of chromosome 2;
(A16) At position 200333453-200333973 of chromosome 2;
(A17) At position 125075832-125076480 of chromosome 3;
(A18) At position 170137150-170137931 of chromosome 3;
(A19) Positions 995761-996936 on chromosome 4;
(A20) 41875340-41875925 to chromosome 4;
(A21) At position 139047739-139048298 of chromosome 5;
(A22) At position 1624936-1625224 of chromosome 6;
(A23) At position 26271346-26271748 of chromosome 6;
(A24) At position 108488594-108488844 of chromosome 6;
(A25) At position 108492267-108492437 of chromosome 6;
(A26) At position 150285813-150286646 of chromosome 6;
(A27) At position 27207996-27208054 of chromosome 7;
(A28) At position 96636496-96636870 of chromosome 7;
(A29) At position 129418361-129418612 of chromosome 7;
(A30) 17271051-17271340 to chromosome 8;
(A31) 67873733-67874151 to chromosome 8;
(A32) 99961175-99961661 to chromosome 8;
(A33) 99985934-99986482 to chromosome 8;
(A34) 100616319-100616730 to chromosome 9;
(A35) 93646929-93647266 to chromosome 10;
(A36) 134597818-134599519 to chromosome 10;
(A37) 69517700-69518306 to chromosome 11;
(A38) 58021614-58021842 to chromosome 12;
(A39) 81102127-81102896 to chromosome 12;
(A40) 102247495-102248194 to chromosome 14;
(A41) 76630449-76631040 to chromosome 15;
(A42) 29297770-29298669 to chromosome 17;
(A43) 43047552-43047830 to chromosome 17;
(A44) 75368790-75370662 to chromosome 17;
(A45) 76739367-76740382 to chromosome 18;
(A46) 12305592-12306084 to chromosome 19;
(A47) 13210026-13210503 to chromosome 19;
(A48) 15342716-15343266 to chromosome 19;
(A49) 15344024-15344364 to chromosome 19;
(A50) 50721097-50722014 to chromosome 20;
(A51) 38220548-38221506 to chromosome 22;
the physical location of the 51 differentially methylated regions is determined based on the human whole genome sequence hg19 alignment;
the cancer is liver cancer.
2. The use according to claim 1, characterized in that: the benign lesions are benign lesions of the liver or cirrhosis of the liver.
3. The use according to claim 2, characterized in that: the liver cancer is primary hepatocellular carcinoma or intrahepatic cholangiocarcinoma.
4. The use according to claim 2, characterized in that: the liver cancer is liver cancer of which the BCLC stage is O stage, A stage, B stage and/or C stage.
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