CN112779334B

CN112779334B - Methylation marker combination for early screening of prostate cancer and screening method

Info

Publication number: CN112779334B
Application number: CN202110137326.9A
Authority: CN
Inventors: 蒲依依; 王孝举; 李超; 袁海宁
Original assignee: Hangzhou Medical College
Current assignee: Hangzhou Medical College
Priority date: 2021-02-01
Filing date: 2021-02-01
Publication date: 2022-05-27
Anticipated expiration: 2041-02-01
Also published as: CN112779334A

Abstract

The invention discloses a methylation marker combination for early screening of prostate cancer and a screening method, relates to the field of bioinformatics, and mainly provides a specificity methylation marker (cg26140475, cg24891312, cg 242422654, cg21359747, cg03254336, cg12697139 and cg19034132) related to early diagnosis of prostate cancer, which is obtained by screening through a database, and a model established based on the marker. The method is based on public databases (TCGA, GEO), and through accurate cancer sample classification, early diagnosis markers are identified in a targeted manner, and then through comparison of markers of different cancers, a series of cancer-specific markers are finally identified.

Description

Methylation marker combination for early screening of prostate cancer and screening method

Technical Field

The invention relates to the field of bioinformatics, in particular to a specific methylation marker for early diagnosis of prostate cancer, which is screened based on a database, a model established by the marker and a screening method of the marker.

Background

Prostate cancer continues to pose a threat to male health worldwide as a cancer of second-degree of morbidity and mortality in men worldwide. According to the latest published data of the national cancer center in China, the incidence rate of prostate cancer is 7.2 thousands and the mortality rate is 3.1 thousands in 2015, and the data are respectively positioned at the 6 th and the 10 th of the rank of the incidence rate and the death rate of malignant tumors of men in China. Different from the western countries, China is not a region with high incidence of prostate cancer, but the incidence rate of prostate cancer is obviously increased in recent years, and the prostate cancer becomes the fastest male malignant tumor in nearly ten years.

Prostate cancer screening based on Prostate Specific Antigen (PSA) has been widely used, but PSA methods have a high false positive rate and are prone to over-diagnosis and over-treatment. In the declaration issued by the U.S. preventive medicine working group in 2018, the PSA method screening is not recommended for people over 70 years old, and for people 55-69 years old, whether the PSA method screening is carried out regularly or not needs to be determined according to personal conditions. In this context, more specific screening tools are available, such as prostate cancer antigen 3(PCA3) and Prostate Health Index (PHI), and a range of marker screening tools including ConfirmMDx, SelectMDx, TMPRSS2-ERG, and the like. However, clinical tests of the methods are mostly based on western people, and the western people have differences in genome and epigenome, so that an effective prostate cancer screening means for the people in China is still lacked.

Cancer has been considered to be not just a genetic disease, but the role of epigenetic modifications in cancer has also been greatly appreciated. The relationship between DNA methylation and cancer is widely studied as one of the most important epigenetic modifications. In recent years, a number of studies have emerged on Cancer markers based on DNA methylation, wherein the studies using databases are not rare, but most of the studies have not subdivided Cancer samples and only performed a general comparison between normal tissues and Cancer tissues [1.De almeido BP, Apolonio JD, Binnie a, cast-branch p.roadmap of DNA methylation in branched biochemical markers. bmc 19(1), (219 2019), 2.Tu Y, Fan G, Xi H et al identification of candidate antibody and differentiated expressed genes in biochemical markers j biological cameras 119(11), (8797) (2018), 3. large sample Y, 3. gene and differentiated expressed genes in biochemical markers in biochemical cells, c.88018), (c.8808, Cell). Cancer progression is a dynamic process, with different stages of cancer having different methylation patterns, and thus, a general sample comparison may make it difficult to screen out methylation markers that are of clinical value.

Disclosure of Invention

The invention aims to screen out a specific DNA methylation marker suitable for early diagnosis of prostate cancer by analyzing methylation chip data in a database and combining with accurate clinical sample classification, and provides a methylation marker combination for early screening of prostate cancer and a screening method.

In a first aspect of the present invention, there is provided a methylation marker combination useful for early screening for prostate cancer, comprising:

cg26140475 located in 8 # chromosome intergenic region (the CpG locus and the DNA base sequence around 60bp are shown in SEQ ID NO: 1);

cg24891312 located in chromosome 12 PRB2 gene (the CpG locus and the DNA base sequence around the CpG locus of 60bp are shown in SEQ ID NO: 2);

cg 24245854 located in the intergenic region of chromosome 12 (the CpG site and the DNA base sequence around the CpG site of 60bp are shown in SEQ ID NO: 3);

cg21359747 located in the ALDH1A3 gene of chromosome 15 (the CpG locus and the DNA base sequence around the CpG locus of 60bp are shown in SEQ ID NO: 4);

cg03254336 located in 10 # chromosome intergenic region (the CpG locus and the DNA base sequence around the CpG locus of 60bp are shown in SEQ ID NO: 5);

cg12697139 located in the intergenic region of chromosome 1 (the CpG locus and the DNA base sequence around the CpG locus of 60bp are shown in SEQ ID NO: 6);

cg19034132 located in the intergenic region of chromosome 10 (the CpG site and the DNA base sequence of 60bp before and after the CpG site are shown in SEQ ID NO: 7).

CpG is an abbreviation for cytosine (C) -phosphate (p) -guanine (G), wherein cytosine can be methylated to 5' -methylcytosine.

In a second aspect of the present invention, there is provided a method for screening a methylation marker specific to prostate cancer related to early diagnosis using a database, comprising the steps of:

1) downloading 450k methylated chip data of a plurality of types of cancers from TCGA and GEO databases;

2) dividing the cancer sample into carcinoma in situ and metastatic carcinoma by using the clinical staging data of the TCGA sample;

3) comparing and analyzing in-situ cancer samples and normal tissue samples of various cancers in TCGA by using limma package (software package for processing microarray data by using a linear model) in R language to obtain differential methylation sites, and verifying by using GEO data;

4) comparing the differential methylation sites of various cancers, and screening out the specific methylation sites of various cancers;

5) combining with the Gleason grading information of the prostate cancer sample and a multivariate modeling method, finally screening out the optimal site combination from the prostate cancer specific methylation sites, namely the methylation marker combination for early screening of the prostate cancer.

The method combining the Gleason grading information of the prostate cancer sample and the multivariate modeling specifically comprises the following steps:

for a data set containing n samples, the data of n-1 samples are adopted each time to establish n models with the minimum AIC value, and sites contained in all the models are selected as key sites.

The present invention identified 7 sites and models thereof for differentiating normal tissues from in situ prostate cancer (12.234 cg26140475+4.247 cg24891312+14.924 cg 24245354-5.902 cg21359747-13.520 cg03254336-3.001 cg12697139-4.841 cg 19034132-0.425). Sites cg26140475 and its model (21.3216 × cg26140475-5.2742) were also identified to distinguish prostate and other urinary cancers.

Compared with the prior art, the invention has the following advantages:

the invention accurately classifies the cancer samples by utilizing clinical information, and then intensively contrasts and analyzes the in-situ cancer samples and the normal samples, thereby more pointedly finding a series of effective early diagnosis related markers. And the invention compares the data of multiple types of cancers in multiple databases, and finally screens out the prostate cancer specific marker combination with clinical application potential which is not reported in documents by combining a modeling means.

Drawings

FIG. 1 is a flow chart of an assay for screening methylation markers specific to prostate cancer using public databases.

Figure 2 is a summary heatmap of 173 cancer-specific differential methylation sites, with data for 16 types of cancer in TCGA summarized on the left and 11 datasets for 7 types of cancer in GEO summarized on the right. The cancer code in the middle of the two figures marks the location of each cancer specific marker. The colors in the heatmap correspond to the mean difference in methylation levels (. beta.) for carcinoma in situ and normal tissues.

Fig. 3 is a graph of methylation level (β) distribution of marker "cg 26140475" in prostate cancer and other urological cancers (bladder cancer, kidney cancer) in TCGA and GEO databases.

Figure 4 is the receiver operating characteristic curve (ROC) validated in the GEO dataset for the predictive model established for the 7 marker loci, with an area under the curve (AUC) of 1 for dataset GSE47915, 0.92 for dataset GSE76938, and 0.99 for dataset GSE 112047.

Detailed Description

The invention establishes a database-based method for screening methylation markers related to early diagnosis of cancers and discloses a methylation marker combination which can be effectively used for early diagnosis of prostate cancer.

The terms:

1) TCGA: the Cancer Genome Atlas, which is The Genome mapping program for human cancers initiated by The National Cancer Institute (NCI) and The national human Genome institute (NHGRI) in combination, currently contains data for 33 types of cancers.

2) GEO: is a Gene Expression database created and maintained by Gene Expression Omnibus, National Center for Biotechnology Information (NCBI).

3) FDR: the False Discovery rate is obtained by correcting the p value by adopting a Benjamini and Hochberg method in multiple comparisons.

4) Gleason score: a scoring system for grading prostate cancer gives 1-5 total histological scores of prostate cells according to the approximate degree of cancer, and the histological scores of the main cells and the secondary cells in the section are added at each time of scoring, so that the prostate cancer is graded into 2-10 points.

5) And (3) AIC: akaike information criterion is a standard for measuring the fitting superiority of a statistical model, and a model with the minimum AIC value is an optimal model.

Example 1, cancer specific methylation marker screening method based on TCGA and GEO databases.

An assay scheme for screening methylation markers specific for prostate cancer using public databases is shown in figure 1.

The method comprises the following steps: downloading and processing of methylated data

Methylated 450k chip Data (Illumina human methylation450 beamchip) for all 33 types of cancer in the TCGA database was downloaded via NIH GDC Data port (https:// port.gdc.cancer. gov. /). The corresponding clinical data was downloaded from the cBioPortal for Cancer Genomics database (http:// www.cbioportal.org /). Subsequent analysis required that each cancer type contained no less than 5 normal samples and had lymphatic and distant metastasis information, so the present invention ultimately selected 16 major cancer subtypes of the 14 cancer major classes: urothelial carcinoma of the Bladder (BLCA), breast invasive ductal carcinoma (D _ BRCA), breast invasive lobular carcinoma (L _ BRCA), colon adenocarcinoma (COAD), esophageal adenocarcinoma (ESCA), head and neck squamous cell carcinoma (HNSC), renal clear cell carcinoma (KIRC), renal papillary cell carcinoma (KIRP), hepatocellular carcinoma (LIHC), lung adenocarcinoma (LUAD), lung squamous cell carcinoma (lucc), pancreatic carcinoma (PAAD), prostate carcinoma (PRAD), rectal adenocarcinoma (READ), follicular thyroid carcinoma (F _ THCA), and papillary thyroid carcinoma (P _ THCA).

Each sample contained methylation data for 485,577 sites, presented as β values (β values from 0 to 1, indicating complete demethylation to complete methylation). The pretreatment comprises three steps: first removing probe data that hybridizes to multiple sites on the genome and removing probe data on the sex chromosome; secondly, each type of cancer removes probe sites missing more than half of the data; finally, for a small amount of missing data, the approximation was used for completion (impute package from Bioconductor). After pretreatment, data for approximately 370,000 probes remained for each type of cancer.

A total of 11 450k methylation chip-based datasets for 7 types of cancer were downloaded from the GEO database: breast cancer (GSE60185, GSE69914), colorectal cancer (GSE42752, GSE48684), renal clear cell carcinoma (GSE61441), hepatocellular carcinoma (GSE56588, GSE77269), lung adenocarcinoma (GSE66836), pancreatic cancer (GSE49149), prostate cancer (GSE76938, GSE 112047). Data preprocessing is consistent with TCGA data.

Step two: analysis of methylation data

The internationally recognized handbook for staging tumors AJCC CANCER STAGING MANUAL staged tumors according to the TNM system (size and range of T-primary tumors, extent of involvement of N-regional lymph nodes, presence or absence of distant metastasis of M). The present invention classifies cancer samples in TCGA into carcinoma in situ (no lymphatic metastasis and no distant metastasis) and metastatic carcinoma (lymphatic or distant metastasis) according to N, M, and defines the finding of carcinoma in situ as a target for early diagnosis. As a log transformed form of β, the M value is more efficient in statistical tests, so β is transformed into M for subsequent significance analysis using the following formula:

the M values of the normal sample and the two types of cancer samples were analyzed using the R limma package:

comparing methylation levels at all sites in normal and carcinoma in situ samples

Comparing methylation levels at all sites in normal and metastatic cancer samples

By correlation analysis, sites with methylation levels correlated with gender, race, age were excluded

Samples of the GEO database to compare methylation levels at all sites in normal and cancer samples

Step three: screening for cancer specific markers

The differential methylation sites of each type of cancer need to satisfy the following conditions:

significant differences in methylation levels (FDR) between normal and carcinoma in situ samples<0.05) and the absolute value of the difference in beta values is greater than 0.1

Significant differences in methylation levels (FDR) between normal and metastatic cancer samples<0.05)

Site methylation level and gender, ethnicity, age-independent (FDR)>0.05)

Cancer types in the GEO database, with significant differences in methylation levels (FDR) between normal and cancer samples<0.05)

On the basis of different cancer methylation sites, the specific markers need to meet the following conditions:

sites were more than 9 significantly differentially methylated in 16 datasets of TCGA

Sites were more than 6 significantly differentially methylated in 11 data sets of GEO

The differential methylation trends of sites in specific cancers and other cancers are opposite

Through the above screening steps, the present invention screened 173 cancer specific markers (including 125 specific for prostate cancer, 20 specific for pancreatic cancer, 10 specific for breast cancer, 9 specific for clear cell renal carcinoma, 7 specific for hepatocellular carcinoma and 2 specific for colorectal cancer) (fig. 2).

Example 2, screening and validation of prostate cancer specific markers.

The method comprises the following steps: screening for prostate cancer specific markers

From all 125 prostate cancer specific markers, based on Gleason scores, markers that can differentiate prostate cancer grade were screened. The study showed that the prognosis for the two patterns of Gleason 7-score prostate cancer 3+4 and 4+3 were significantly different, while Gleason 6&3+4 was defined as low-score prostate cancer and 4+3&8-10 as high-score prostate cancer based on the sample composition of this project. Thereafter, methylation levels of the normal sample and the low-grade and high-grade prostate cancer samples were compared and analyzed by using the R limma package, and finally, 26 prostate cancer specific markers with significant difference (FDR <0.05) in the methylation levels of the three types of samples were selected.

Step two: building predictive models using key markers

To build a linear model to predict the state of a sample using the methylation levels of key sites (β values, between 0-1): normal tissue vs. prostate cancer in situ, prostate cancer vs. other cancers of the urinary system (bladder urothelial cancer, renal clear cell cancer, renal papillary cell cancer). The invention refers to the concept of leave-one-out cross validation, and for a data set containing n samples, the data of n-1 samples are adopted each time to establish n models with the minimum AIC value, and sites contained in all the models are selected as key sites. The present invention identified 7 sites and models thereof for differentiating normal tissues from in situ prostate cancer (12.234 cg26140475+4.247 cg24891312+14.924 cg 24245354-5.902 cg21359747-13.520 cg03254336-3.001 cg12697139-4.841 cg 19034132-0.425). Wherein site cg26140475 was used to model (21.3216 × cg26140475-5.2742) to differentiate prostate cancer from other urinary cancers based on its differential methylation in prostate cancer and other urinary cancers (fig. 3).

Step three: verification of predictive models

The above models were verified using a data set of GEO. GSE47915 included 4 cases each of Gleason 6 grade prostate cancer tissue and normal tissue; GSE76938 contained 73 prostate cancer tissues and 63 normal tissues; GSE112047 contained 31 cases of prostate cancer tissue and 16 cases of normal tissue. The model for differentiating normal tissue from orthotopic prostate cancer was validated in the three data sets above, and the area under the curve (AUC) of the model subject operating characteristic curve (ROC) was as follows: GSE47915, AUC ═ 1; GSE76938, AUC 0.92; GSE112047, AUC 0.99 (fig. 4). The model for distinguishing prostate cancer from other urinary system cancers was verified using the data set GSE52955 (including kidney, bladder and prostate cancer tissues and corresponding normal tissues) to yield AUC 1.

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Claims

1. A methylation marker combination for early screening of prostate cancer, which consists of the following markers:

the cg26140475 is located in 8 # chromosome intergenic region, the CpG locus and the DNA base sequence around the CpG locus of 60bp are shown as SEQ ID NO: 1 is shown in the specification;

cg24891312 located in chromosome 12 PRB2 gene, wherein the CpG locus and the DNA base sequence around the CpG locus of 60bp are shown as SEQ ID NO: 2 is shown in the specification;

cg 242422654 located in the intergenic region of chromosome 12, wherein the CpG site and the DNA base sequence around the CpG site of 60bp are shown as SEQ ID NO: 3 is shown in the specification;

the cg21359747 of the ALDH1A3 gene located on chromosome 15, the CpG locus and the DNA base sequence around the CpG locus of 60bp are shown as SEQ ID NO: 4 is shown in the specification;

the CpG 03254336 is positioned in the 10 # chromosome intergenic region, and the CpG locus and the DNA base sequences of the front and back 60bp of the CpG locus are shown as SEQ ID NO: 5 is shown in the specification;

cg12697139 located in the intergenic region of chromosome 1, the CpG locus and the 60bp DNA base sequence before and after the CpG locus are shown as SEQ ID NO: 6 is shown in the specification;

and cg19034132 located in the intergenic region of chromosome 10, wherein the CpG site and the 60bp DNA base sequences before and after the CpG site are shown as SEQ ID NO: shown at 7.

2. The methylation marker combination for early screening of prostate cancer according to claim 1, wherein CpG is an abbreviation for cytosine-phosphate-guanine, wherein cytosine can be methylated to 5' -methylcytosine.

3. The screening method for a methylation marker combination for early screening of prostate cancer according to claim 1, comprising the steps of:

1) downloading the data of the methylation chips of various cancers from TCGA and GEO databases;

3) comparing and analyzing in-situ cancer samples and normal tissue samples of various cancers in TCGA by using a limma package in an R language to obtain differential methylation sites, and verifying by using GEO data;

5) combining the Gleason grading information of the prostate cancer sample with a multivariate modeling method, and finally screening a methylation marker combination for early screening of the prostate cancer from the prostate cancer specific methylation sites.