MethMarkerDB: a comprehensive cancer DNA methylation biomarker database

Zhixian Zhu; Qiangwei Zhou; Yuanhui Sun; Fuming Lai; Zhenji Wang; Zhigang Hao; Guoliang Li

doi:10.1093/nar/gkad923

. 2023 Oct 27;52(D1):D1380–D1392. doi: 10.1093/nar/gkad923

MethMarkerDB: a comprehensive cancer DNA methylation biomarker database

Zhixian Zhu ^1,^2,^b, Qiangwei Zhou ^3,^4,^b, Yuanhui Sun ^5,^6,^b, Fuming Lai ^7,⁸, Zhenji Wang ^9,¹⁰, Zhigang Hao ^11,¹², Guoliang Li ^13,^14,^✉

¹ National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China

² Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China

³ National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China

⁴ Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China

⁵ National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China

⁶ Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China

⁷ National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China

⁸ Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China

⁹ National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China

¹⁰ Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China

¹¹ National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China

¹² Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China

¹³ National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China

¹⁴ Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China

^✉

To whom correspondence should be addressed. Tel: +86 27 87285078; Fax: +86 27 87286876; Email: guoliang.li@mail.hzau.edu.cn

The authors wish it to be known that, in their opinion, the first three authors should be regarded as Joint First Authors.

PMCID: PMC10767949 PMID: 37889076

Abstract

DNA methylation plays a crucial role in tumorigenesis and tumor progression, sparking substantial interest in the clinical applications of cancer DNA methylation biomarkers. Cancer-related whole-genome bisulfite sequencing (WGBS) data offers a promising approach to precisely identify these biomarkers with differentially methylated regions (DMRs). However, currently there is no dedicated resource for cancer DNA methylation biomarkers with WGBS data. Here, we developed a comprehensive cancer DNA methylation biomarker database (MethMarkerDB, https://methmarkerdb.hzau.edu.cn/), which integrated 658 WGBS datasets, incorporating 724 curated DNA methylation biomarker genes from 1425 PubMed published articles. Based on WGBS data, we documented 5.4 million DMRs from 13 common types of cancer as candidate DNA methylation biomarkers. We provided search and annotation functions for these DMRs with different resources, such as enhancers and SNPs, and developed diagnostic and prognostic models for further biomarker evaluation. With the database, we not only identified known DNA methylation biomarkers, but also identified 781 hypermethylated and 5245 hypomethylated pan-cancer DMRs, corresponding to 693 and 2172 genes, respectively. These novel potential pan-cancer DNA methylation biomarkers hold significant clinical translational value. We hope that MethMarkerDB will help identify novel cancer DNA methylation biomarkers and propel the clinical application of these biomarkers.

Graphical Abstract

Introduction

Aberrant DNA methylation is a hallmark of cancer, characterized by two distinct patterns: global hypomethylation, which induces genome instability and focal hypermethylation, resulting in the silencing of tumor-suppressor genes (1). DNA methylation alterations emerge as an early and widespread event across various cancers. Unlike gene mutations, which exhibit high individuality and heterogeneity, DNA methylation changes in the same type of tumor cells tend to be more consistent (2). Consequently, there has been considerable interest in translating DNA methylation alterations into clinical biomarker applications (3). Several specific methylation biomarkers have been identified for various cancer types. For instance, SEPT9 serves as a colorectal cancer (CRC) screening biomarker, showing high clinical utility (4). Similarly, a combination of GSTP1 with APC and RARB methylation assays has shown high sensitivity and specificity for prostate cancer diagnosis (5). SHOX2 hypermethylation has proven as an effective clinical biomarker for lung cancer diagnosis (6). Numerous DNA methylation biomarkers have also been reported for liver cancer (7), breast cancer (8), cervical cancer (9) and glioblastoma (10). Moreover, some studies have explored pan-cancer differential methylation patterns (11). Dong et al. have identified potential pan-cancer DNA methylation biomarkers, such as HIST1H4F, PCDHGB7 and SIX6, which show promising clinical application prospects (12–14). Notably, over 14 000 scientific publications were available in PubMed for cancer DNA methylation biomarker study, and 14 of these biomarkers were already commercialized for clinical screening, emphasizing the importance of advancing the development of cancer DNA methylation biomarkers (15).

An important prerequisite for the clinical translation of DNA methylation biomarkers is accurate genomic location information. Determining the precise location of clinically relevant DMRs (Differentially Methylated Regions) is a critical step in the development of DNA methylation biomarkers (15). However, in many scientific publications, the detailed genomic location information of biomarkers remains unclear, impeding the clinical application of cancer DNA methylation biomarkers. For instance, some studies have reported GSTP1 gene as a DNA methylation biomarker for hepatocellular carcinoma (HCC) diagnosis, but with widely varying levels of specificity. Jain et al. found that the methylation in the 5′ promoter region of GSTP1 exhibited significantly superior diagnostic performance compared to the methylation in 3′ end (16). Similarly, DMRs in promoter and exon regions of RASSF1 showed substantial difference of performance in early diagnosis of HCC (17). These findings underscore the importance of precise genomic location for cancer DNA methylation biomarkers, rather than just genes.

Whole-genome bisulfite sequencing (WGBS) enables a comprehensive assessment of DNA methylation across the whole genome (18). It offers the opportunity to provide precise genomic coordinates for DNA methylation biomarkers at a whole-genome level. In addition, compared to traditional methylation 450K or EPIC Beadchip data, which only covers approximately 2–3% of CpG sites in the human genome (19), the genomic regions covered by WGBS data offer much larger number of potential biomarker candidates. However, there is currently a lack of dedicated platforms or resources specifically designed for the identification of cancer DNA methylation biomarkers using WGBS data. Therefore, it is necessary to establish a cancer DNA methylation biomarker database based on WGBS data, which offers biomarkers with accurate genomic information. This will help advance the clinical application of cancer DNA methylation research.

In this article, we introduce MethMarkerDB, a comprehensive cancer DNA methylation biomarker database (https://methmarkerdb.hzau.edu.cn/). MethMarkerDB encompasses 5.4 million DMRs identified from 658 WGBS datasets across 13 types of cancer from NCBI GEO and SRA database (20,21). And it catalogues 724 DNA methylation biomarker genes from 1 425 PubMed articles. To enhance the utility of this resource, we have provided comprehensive annotations for genes and DMR regions, such as SNPs, meQTLs and enhancers. Moreover, MethMarkerDB offers a wide range of functions, including literature-based DNA methylation marker gene search, methylation gene/DMR biomarker search, pan-cancer DNA methylation biomarker search and diagnostic and prognostic evaluation tools, among others.

MethMarkerDB is the first cancer DNA methylation biomarker database constructed on WGBS data. This unique resource offers precise genomic location information for biomarkers, encompassing both biomarker genes and DMRs. We believe that MethMarkerDB will serve as a powerful resource for identifying novel cancer DNA methylation biomarkers and facilitating their translation into clinical application.

Methods and materials

Database implementation

Firstly, we collected cancer-related WGBS data from the GEO and SRA databases, methylation 450K BeadChip data from TCGA, and the reported DNA methylation biomarker genes from PubMed (Figure 1A). Using the WGBS data, we identified DMRs in various cancers (Figure 1B). We curated a diverse range of genomic regulatory elements to annotate these DMRs (Figure 1C). Building upon these results, we established the MethMarkerDB database. It includes multiple search functions (Figure 1D), and integrates the JBrowse (version 1.16.6) genome browser (22), which can display the DMR regions and single-base DNA methylation levels (Figure 1E). Moreover, MethMarkerDB offers analysis functions to facilitate the exploration of DNA methylation biomarkers (Figure 1F). MySQL (version 5.7.26) was utilized to organize the database, while Ngnix and Django were employed for web interface development. ECharts (http://echarts.baidu.com) was utilized for data visualization.

Figure 1. — Construction process of the MethMarkerDB database. (A) MethMarkerDB collects datasets from GEO, SRA databases and the TCGA project. Additionally, cancer DNA methylation biomarker genes reported in the literature are curated from PubMed. (B) DMR visualization. (C) Annotation resources in MethMarkerDB. Genes or regions overlapped with DMRs are annotated with enhancers, silencers, SNPs, etc. (D) Search modules in MethMarkerDB. (E) Genome browser in MethMarkerDB. (F) Analysis modules in MethMarkerDB.

Data collection

We collected and downloaded WGBS data from the NCBI GEO and SRA databases up to June 2023. After filtering out low-coverage and low-quality data (sequencing bases < 15G bps, bisulfite conversion rate < 95%), a total of 658 datasets were kept for downstream analysis. These datasets cover 13 different cancer types, including lung cancer, liver cancer, breast cancer and prostate cancer (Table 1).

Table 1.

Numbers of datasets and DMRs

Cancer type	WGBS			DMR
	Number of cancer samples	Number of normal samples	Total	Number of hyper-DMRs	Number of hypo-DMRs	Total
Acute Lymphoblastic Leukemia	18	118	136	719 088	18 102	737 190
Breast cancer	24	10	34	80 854	306 999	387 853
Cervical cancer	13	10	23	83 374	469 505	552 879
Colorectal cancer	30	18	48	67 923	243 725	311 648
Esophagus cancer	52	51	103	2 389	210 506	212 895
Gastric cancer	3	3	6	12 719	24 455	37 174
Liver cancer	53	50	103	15 252	665 810	681 062
Lung cancer	29	31	60	40 847	199 324	240 171
Oral cancer	4	4	8	7 899	586 701	594 600
Ovarian cancer	28	34	62	557 942	62 025	619 967
Pancreatic cancer	13	5	18	82 766	129 011	211 777
Prostate cancer	31	14	45	43 166	458 305	501 471
Retinoblastoma	4	8	12	27 826	287 418	315 244
Total	302	356	658	1 742 045	3 661 886	5 403 931

Open in a new tab

Abbreviation: Hyper-DMRs, hypermethylated DMRs. Hypo-DMRs, hypomethylated DMRs.

Processing of WGBS data

Low-quality and artificial reads were trimmed using Fastp (version 0.23.4) (23) with default parameters. The clean reads were aligned to the reference genome (hg38) using BatMeth2 (24), and the alignment SAM files were converted to BAM format using SAMtools (25). Reads with alignment quality scores below 20 were filtered out, and cytosine sites with a coverage of 5 or higher were retained for further analysis. DNA methylation levels were calculated using the ‘Calmeth’ function in BatMeth2. Since most of the WGBS datasets lack spike-in sequences for bisulfite conversion assessment, we evaluated the bisulfite conversion rate by calculating the CHG methylation level similar to our previous approach (26). Datasets with a bisulfite conversion rate lower than 95% were excluded.

Differential methylation regions (DMRs) analysis

To identify highly credible DMRs, we firstly conducted sample selection from the WGBS data of the same cancer type (Supplementary Figure S1). The selected samples were then employed for DMR analysis (refer to the method for sample selection in the supplementary materials). Subsequently, we employed the SMART2 tool (27) in DeNovoDMR mode (-t) to identify highly reliable DMRs across the whole genome. The parameters used for SMART2 were as follows: a minimum of 5 CpGs was set (-CN), and an absolute mean methylation difference of 0.3 between the case group and the control group was applied (-AD). Finally, we used the ChIPseeker R package (28) to annotate the identified DMRs.

Pan-cancer DMRs analysis

Firstly, we identified pan-cancer DMRs by selecting DMRs annotated to the same gene in multiple cancer types (involving two or more cancer types). Subsequently, we conducted a statistical analysis to quantify the number of pan-cancer DMRs corresponding to each gene across the diverse cancer types. The results were visually presented in the ‘Pan-Cancer DMR Analysis’ module on our website. Furthermore, to facilitate querying of pan-cancer DMRs, we visually illustrated the genomic positions of these DMRs corresponding to each gene across different cancer types.

Diagnostic and prognostic analysis of methylation biomarker

In the ‘MethMarker Evaluation’ module, we conducted an evaluation of methylation markers from two critical perspectives: diagnostic and prognostic performance. For this analysis, we downloaded Methylation 450K BeadChip data and corresponding clinical data for 29 different cancer types from TCGA (29) via UCSC Xena (http://xena.ucsc.edu/). Due to the limited number of samples, some cancer types were excluded from the downstream analysis. LiftOver (30) was used to map the probe annotation from hg19 coordinates to hg38 coordinates.

For each queried gene or queried region, we first extracted the CpG sites that overlapped with the respective gene or region. Subsequently, we processed the CpG sites as follows: (i) CpG sites that were absent in over 20% of all samples were excluded from the analysis. (ii) CpG sites that were absent in <20% of all samples were imputed separately in the cancer and normal groups using the ‘na_interpolation’ function with a linear model from the ‘imputeTS’ R package (31). Next, we established the prognostic model based on all CpG sites using the ‘survfit’ function in the ‘survival’ R package (32). For the diagnostic model, we retained CpG sites that exhibited significant differences in DNA methylation levels between the cancer and normal groups (q value < 0.05). Subsequently, we employed the xgboost R package to compute feature importance scores for each CpG site (33). Finally, CpG sites with importance scores greater than 0 were retained to establish the logistic regression model.

Collecting and processing of annotation resource

To deeply understand the potential functions of DMRs, we curated a diverse set of annotations, including meQTLs (34,35), SNPs (36–41), enhancers (42–47), super-enhancers (48–50), silencers (51), chromatin accessibility information (29,52,53), motif binding sites (52–56) and gene expression data (57–59). BEDTools (60) was utilized to map the corresponding annotations to DMRs with the overlapped coordinates. We employed interactive tables and plots to present the detailed annotations. The sources of each annotation data are provided in Supplementary Table S1.

Collection of biomarker genes from literature

We firstly downloaded ∼35 000 abstracts from PubMed. Subsequently, we systematically scanned these abstracts to identify sentences containing relevant information such as genes, methylation status and cancer types. Following this scanning process and manual selection, we successfully curated a total of 1425 distinct literature sources (Figure 2C) containing information on 724 DNA methylation biomarker genes (Figure 2D) and 53 cancer types (Figure 2E). Finally, on the ‘Literature Search’ module of our website, we presented an interactive visualization of these biomarker genes using tables and graphs for easy exploration.

Figure 2. — Overview of MethMarkerDB database. (A) The homepage of MethMarkerDB. (B) Main functional modules in MethMarkerDB. (C) Statistics of cancer DNA methylation biomarker genes reported in PubMed per year. (D) Proportion of cancer DNA methylation biomarker genes curated from PubMed. (E) The proportion of cancer types corresponding to cancer DNA methylation biomarker genes curated from PubMed. (F) An example of a genome browser screenshot around the *HOXA9* gene region in lung cancer (chr7:27 162 008–27 169 082, 7.08 kb).

Results

Web interface

MethMarkerDB introduces a user-friendly web interface with a variety of well-designed functional modules (Figure 2A, B). These modules include (i) ‘MethMarker Gene Search’ module can display the gene symbols and information on all DMRs associated with queried genes in each given cancer type; (ii) ‘MethMarker Region Search’ module provides details on DMRs within a user-specified genomic region in a particular cancer type; (iii) ‘Literature Search’ module showcases DNA methylation biomarker genes reported in literature; (iv) ‘Pan-Cancer DMR Analysis’ module presents DMRs across multiple cancer types; (v) ‘Cancer DMR Analysis’ module reveals DMRs specific to a chosen cancer type; (vi) ‘MethMarker Evaluation’ module offers evaluation results of genes and genomic regions as DNA methylation diagnostic or prognostic biomarkers in particular cancer types; (vii) ‘Genome Browser’ module allows users to explore single-base resolution DNA methylation levels and DMR genomic locations; (viii) ‘DataList’ and ‘Download’ modules provide access to DNA methylation dataset information and DMR result files for browsing and download; (ix) ‘Tutorial’, ‘Help’ and ‘About Us’ modules feature detailed documentation and tutorials.

MethMarkerDB genome browser

The genome browser, built on JBrowse (version 1.16.6), empowers users with a convenient platform to explore single-base DNA methylation levels and DMRs. Users can easily access to specific genomic regions and investigate DNA methylation patterns across multiple samples. Additionally, the genome browser facilitates the visualization of DMR distributions in diverse cancer types. As an illustrative example, we presented the DMRs and DNA methylation levels near the HOXA9 gene in lung cancer and normal samples (Figure 2F). HOXA9 is known as a tumor suppressor gene, which inhibits tumor cell growth and metastasis (61). Recent studies have indicated the significant hypermethylation of HOXA9 gene in various cancers, including lung cancer (62) and liver cancer (63). Within the genome browser, a hypermethylation pattern can be observed in lung cancer samples compared to lung normal samples. Notably, this distinctive region is identified as a DMR (Figure 2F). The JBrowse plugins (64), including the ‘ScreenShot Plugin’, enable users to save and download the results in PNG/PDF format. Furthermore, users have the option to upload their own DNA methylation data, thereby enriching their exploration within the genome browser.

Functions of MethMarkerDB

MethMarker gene search

The ‘MethMarker Gene Search’ page offers a search function for DNA methylation biomarker genes in different cancer types. The search results encompass: (i) basic information about the queried gene; (ii) a distribution plot of DMRs within the region 3kb upstream, gene body and the region 3kb downstream of the gene, along with precise genomic location information of these DMRs; (iii) annotation information for the queried gene, including variations, enhancers, silencers, transcription factors and chromatin accessibility information; (iv) expression information of the queried gene in cancer and normal samples.

GSTP1 is a member of the Glutathione S-transferase (GST) family and is silenced by DNA hypermethylation in CpG island in 90–95% of prostate cancers (65). Previous studies have proposed GSTP1 methylation as a promising biomarker for early prostate cancer diagnosis (66,67), making it one of the most extensively studied epigenetic biomarkers in prostate cancer research (68). However, it has been challenging to conveniently obtain specific genomic regions of diagnostic DMRs from relevant literature.

Here, we use the GSTP1 gene as an example to showcase the powerful functionality of the ‘MethMarker Gene Search’ module. Figure 3A illustrates the search page where users can select the cancer type, such as prostate cancer and the gene, such as GSTP1 (Figure 3A). Figure 3B displays the distribution of DMRs around the GSTP1 gene in prostate cancer. It is obvious that hypermethylated DMRs are in the promoter region of the GSTP1 gene, and their precise genomic locations are presented in tabular form (Figure 3C). Furthermore, by clicking the ‘Hyper’ box in Figure 3C, users can access to information about the differential DNA methylation levels of the DMR (chr11:67 583 112–67 584 857) in prostate cancer and normal samples (Figure 3D) and explore methylation levels of this DMR in individual samples (Figure 3E). We have provided the regulatory element information, such as enhancers, super-enhancers and silencers, around the GSTP1 gene (Figure 3F). Consistent with research findings, the expression level of the GSTP1 gene is much lower in prostate cancer compared to normal samples (Figure 3G).

These results indicate that the promoter region of the GSTP1 gene undergoes significant hypermethylation in prostate cancer, leading to the suppression of GSTP1 gene expression. The DMR (chr11:67 583 112–67 584 857) within the promoter region of the GSTP1 gene could potentially serve as promising early diagnostic biomarker for prostate cancer.

MethMarker region search

In addition to genes, users can also utilize the ‘MethMarker Region Search’ page to search for DMRs within specific genomic regions across different cancer types. The search results include both gene information and details of the DMRs within the queried genomic region. Moreover, the ‘MethMarker Region Search’ result page also provides genomic annotation information, such as enhancers, silencers and other regulatory elements that overlap with the queried genomic region.

Literature search

Numerous studies have reported a wealth of DNA methylation biomarker genes across various cancer types. We have curated 724 DNA methylation biomarker genes from 1425 PubMed articles. It is noteworthy that we have identified DMRs in over 95% of these reported biomarker genes. On the ‘Literature Search’ page, we present a visualization and statistical summary of manually curated biomarker genes, allowing users to conveniently explore the reported DNA methylation biomarkers for each cancer type. For instance, users can select their interested cancer types (e.g. liver cancer) (Figure 4A), and the ‘Literature Search’ page will display a word cloud representing the DNA methylation biomarker genes reported in liver cancer (Figure 4B). Notably, the word cloud showcases widely studied liver cancer DNA methylation biomarkers, including RASSF1, CDKN2A, GSTP1, SOX11 and other genes (Figure 4B). Additionally, detailed information on these DNA methylation biomarker genes is presented in tabular form (Figure 4C).

Figure 4. — Literature search and Pan-cancer DMR analysis. (A) Literature search page. (B) Word cloud presenting previously reported cancer DNA methylation biomarker genes in liver cancer. (C) Detailed information on the reported cancer DNA methylation biomarker genes. (D) Bar plot of the number of DMRs annotated to the *SOX11* gene in different cancer types. (E) Genomic distribution of DMRs annotated to the *SOX11* gene in various cancer types.

Pan-cancer DMR analysis

Some studies have explored pan-cancer DNA methylation patterns, while the research on pan-cancer DNA methylation biomarkers remains limited. The ‘Pan-Cancer DMR Analysis’ module provides an effective platform to investigate pan-cancer DNA methylation biomarkers. For example, SOX11 has been identified as a DNA methylation biomarker in liver cancer (Figure 4C), but its status as a pan-cancer DNA methylation biomarker lacks literature support (69). In the results of the ‘Pan-Cancer DMR Analysis,’ we found several DMRs near the SOX11 gene in nine cancer types, including Breast cancer, Lung cancer and Liver cancer (Figure 4D). Importantly, these DMRs share consistent genomic positions across different cancers (Figure 4E), suggesting SOX11 as a potentially valuable pan-cancer DNA methylation biomarker.

Dong et al. identified PCDHGB7 and SIX6 as promising pan-cancer DNA methylation biomarkers (13,14). In the ‘Pan-Cancer DMR Analysis’ module, we also observed many DMRs near the PCDHGB7 or SIX6 genes in multiple cancer types, with some DMRs displaying consistent genomic positions across different cancers (Supplementary Figure S2A-D). Recently, several olfactory receptors (ORs) genes have been implicated in cancer proliferation or progression in specific cancer types (70,71), but we found no literature reports of ORs as cancer DNA methylation biomarkers. We highlighted an unreported potentially valuable pan-cancer DNA methylation biomarker gene, OR2M3 (Supplementary Figure S2E, F). Notably, unlike the currently reported pan-cancer DNA methylation biomarkers, the DMRs near OR2M3 were hypomethylated in multiple cancers (Supplementary Figure S2F).

TCGA methylation 450K and methylation EPIC BeadChip data have been widely utilized in cancer DNA methylation biomarker research. However, due to the limited coverage, many potential biomarkers have not been detected. As an example, we have identified a pan-cancer Hyper-DMR (chr9:65 736 695–65 738 185) located within FOXD4L4 (Supplementary Figure S2G, H), which is not covered by any CpG sites within the methylation 450K or EPIC BeadChip. Moreover, we conducted a comparative analysis of DMRs from WGBS and differentially-methylated CpG sites from methylation 450K or EPIC BeadChip. In lung cancer, we observed that DMRs covered by the 450K BeadChip (with a minimum of 1 CpG site within a DMR) accounted for only 10.32% of all DMRs (24 794 out of 240 171). Specifically, 450K BeadChip covered 43.66% of Hyper-DMRs (17 834 out of 40 847) and only 3.49% of Hypo-DMRs (6 960 out of 199 324), while EPIC BeadChip covered 59.71% of Hyper-DMRs (24 390 out of 40 847) and 10.95% of Hypo-DMRs (21 818 out of 199 324). Therefore, the majority of WGBS-based DMRs, especially Hypo-DMRs, were not covered by methylation 450K or EPIC BeadChip (Supplementary Figure S3). This indicates that WGBS holds the promise to detect more pan-cancer DNA methylation biomarkers.

Utilizing the database, we have identified 781 hypermethylated and 5 425 hypomethylated pan-cancer DMRs, corresponding to 693 and 2 172 genes, respectively (Supplementary Table S2). Gene Ontology (GO) analysis (72,73) revealed that hypermethylated pan-cancer DMRs are primarily associated with functions such as ‘embryonic organ morphogenesis’ and ‘transcription from RNA polymerase II promoter’ (Supplementary Table S3). On the other hand, hypomethylated pan-cancer DMRs are prominently linked to pathways such as ‘sensory perception of chemical stimulus’ and ‘olfactory receptor activity’ (Supplementary Table S4). These novel potential pan-cancer DNA methylation biomarkers hold significant clinical translational value, and need further investigation.

In summary, the ‘Pan-Cancer DMR Analysis’ module facilitates the discovery of both reported and novel pan-cancer DNA methylation biomarkers, providing valuable insights into cancer epigenetic biomarker research.

MethMarker evaluation

The ‘MethMarker evaluation’ module provides a diagnostic and prognostic evaluation model based on DNA methylation biomarkers. Users can input biomarker genes or genomic regions to view the diagnostic and prognostic performance of these biomarkers. For biomarkers of interest, users can access DNA sequences and utilize online tools for primer design (74). In cases involving multiple DMRs within a gene or genomic region, users have the option to evaluate the diagnostic and prognostic performance of each individual DMR. This feature helps users identify which specific DMR holds more promise as a cancer DNA methylation biomarker. For instance, previous studies have found significant differences in the performance of DMRs in the promoter and exon regions of the RASSF1 gene for early diagnosis of liver cancer (17). We have also discovered two DMRs for RASSF1 in liver cancer, one located in the promoter region and the other in the exon region (Supplementary Figure S4A). The results from the ‘MethMarker evaluation’ module demonstrate that the DMR located in the promoter region exhibits better diagnostic performance for liver cancer compared to the DMR in the exon region, with a notable difference in the Area Under the Curve (AUC) values (Supplementary Figure S4B, C).

Recently, a meta-analysis has revealed that liver cancer patients with RASSF1 gene promoter hypermethylation have a poorer prognosis (75). We also observed that hypermethylated DMR in the promoter region of RASSF1 is associated with bad outcomes (Supplementary Figure S4D). Furthermore, we found that hypomethylated DMR in the exon region is also linked to poor prognosis in liver cancer (Supplementary Figure S4E) and demonstrates a more significant prognostic value (p(HR)=0.00043).

Cancer DMR analysis

We integrated DMRs detected in different cancer types and performed genomic annotation on these DMRs, which are incorporated into the ‘Cancer DMR analysis’ module. Figure 5A displays the distribution of hypermethylated and hypomethylated DMRs in the genome in lung cancer. Figure 5B presents the genomic annotation results of hypermethylated and hypomethylated DMRs, revealing that hypermethylated DMRs are predominantly located in promoter regions. Additionally, we provide a tabular representation of DMR information in different cancer types for user-friendly queries (Figure 5C).

Figure 5. — Application example of MethMarkerDB. (A) Chromosomal distribution of DMRs in lung cancer. (B) Statistics of genomic annotations for hyper-DMRs and hypo-DMRs, respectively. (C) Detailed information about the identified DMRs. (D) Genome browser screenshot displaying the genomic region around *HIST1H4F* gene in lung cancer. (E) ROC curve for DMR (chr6:26 240 136–26 241 494) overlapped with *HIST1H4F* in classifying cancer and normal samples in the TCGA LUSC cohort. (F) Genomic distribution of DMRs annotated to the *HIST1H4F* gene in various cancers.

Through the integration of DMRs across 13 different cancer types, we have identified a genomic region (chr5:141 000 000–141 500 000) that harbors a substantial number of hypermethylated DMRs. This region exhibits a notable enrichment of clustered protocadherin genes (PCDHs) (Supplementary Figure S5). Epigenetic silencing of PCDHs by hypermethylation has been studied in previous research (76), underscoring the potential of these DMRs as robust diagnostic biomarkers. Notably, the pan-cancer biomarker gene PCDHGB7 mentioned earlier is a member of the protocadherin gene family.

Usage example

To illustrate the functionality of the database, we present a usage example. Histone-related genes are found to be hypermethylated in lung cancer, and HIST1H4F has been identified as a potential pan-cancer biomarker (12). In the ‘Cancer DMR analysis’ module, we identified a DMR (chr6:26 240 136–26 241 494) within the promoter region of HIST1H4F (Figure 5C). Using the ‘Genome Browser’ module, we observed that this DMR region exhibits higher methylation levels in lung cancer samples compared to normal samples (Figure 5D). Moreover, this DMR region effectively distinguishes lung cancer samples from normal lung tissue samples (Figure 5E). Furthermore, in the ‘Pan-Cancer DMR Analysis’ module, we discovered the presence of DMRs near the HIST1H4F gene in multiple cancer types, and these DMRs exhibit consistent genomic positions (Figure 5F). Our thorough analysis emphasizes the clinical significance and diagnostic role of the DMRs that overlap with HIST1H4F.

Datalist and others

The ‘Datalist’ module contains the list of all samples available in the database, allowing users to search samples of interest. For each sample, we provide basic information, including GEO or SRA IDs, sample descriptions and bisulfite conversion rates (Supplementary Figure S6A). Additionally, we present graphical representations showing the count of cytosines with different coverages and methylation levels for each sample (Supplementary Figure S6B, C). In the ‘Download’ module, users can download our curated list of DNA methylation biomarkers from the literature, as well as the results of DMRs for various cancer types. For any data are not currently included in our database, users can use the ‘Contact Us’ feature to submit web links containing the relevant data. We commit to analyze new data regularly and update the results.

Discussion and future directions

The first cancer DNA methylation biomarker database from WGBS data

In this study, we have introduced MethMarkerDB, a comprehensive resource for cancer DNA methylation biomarkers based on WGBS data. There are some existing databases for cancer DNA methylation, such as MethHC 2.0 (77) and DNMIVD (78). However, they are not dedicated to DNA methylation biomarkers. And these databases rely on DNA methylation BeadChip data, covering only approximately 2% to 3% of CpG sites in the human genome. MethMarkerDB collects and analyzes WGBS data from various cancer types to identify cancer DNA methylation biomarkers across the whole genome. Many scientific publications lack detailed genomic location information for DNA methylation biomarkers, which hinders their clinical application (15). In MethMarkerDB, we offer a vast collection of DNA methylation biomarkers with precise genomic information, facilitating their clinical utilization. The database integrates search, analysis, visualization and download functions to assist the identification and evaluation of cancer DNA methylation biomarkers. Furthermore, the ‘Pan-cancer DMR’ module within MethMarkerDB is specifically designed to aid in the identification of pan-cancer DNA methylation biomarkers.

In summary, MethMarkerDB serves as a comprehensive cancer DNA methylation biomarker database, capable of identifying DNA methylation biomarker genes/DMRs in different cancers and promoting their clinical application.

Future directions

In the future, we will continue to update MethMarkerDB as follows: (i) We will expand our collection of WGBS data from diverse sources and cancer types, thereby increasing the database's coverage and utility. (ii) DNA methylation of cell-free DNA (cfDNA) exhibits promising potential for early cancer detection (79,80). In the future, we will incorporate DNA methylation sequencing data of cfDNA into MethMarkerDB to identify additional cancer DNA methylation biomarkers. (iii) The emergence of single-cell methylation sequencing methods has unveiled the cellular heterogeneity at the DNA methylation level, providing valuable insights into cell-specific patterns (81,82). In the future, we can also identify methylation biomarkers at single cell resolution, enriching the database's resources for detailed analyses. (iv) We will provide additional online functions on the website based on user feedback. We are committed to regularly update MethMarkerDB to ensure that it remains a valuable and up-to-date resource for cancer DNA methylation biomarkers. We expect that MethMarkerDB will contribute to advancing research on cancer DNA methylation biomarkers and their clinical applications.

Supplementary Material

gkad923_Supplemental_Files

Click here for additional data file.^{(2.6MB, zip)}

Acknowledgements

We thank Mr. Hao Liu from the National Key Laboratory of Crop Genetic Improvement for essential help in managing the high-throughput computing clusters. We also extend our appreciation to the members of our research group for their valuable feedback on the database. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Contributor Information

Zhixian Zhu, National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.

Qiangwei Zhou, National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.

Yuanhui Sun, National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.

Fuming Lai, National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.

Zhenji Wang, National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.

Zhigang Hao, National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.

Guoliang Li, National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan 430070, China; Agricultural Bioinformatics Key Laboratory of Hubei Province, Hubei Engineering Technology Research Center of Agricultural Big Data, Key Laboratory of Smart Farming for Agricultural Animals, 3D Genomics Research Center, College of Informatics, Huazhong Agricultural University, Wuhan 430070, China.

Data availability

MethMarkerDB is an online and open-access database, accessible at https://methmarkerdb.hzau.edu.cn.

Supplementary data

Supplementary Data are available at NAR Online.

Funding

National Key Research and Development Program of China [2021YFC2701201]; National Natural Science Foundation of China [31 970 590]. Funding for open access charge: National Key Research and Development Program of China [2021YFC2701201]; National Natural Science Foundation of China [31970590]

Conflict of interest statement. None declared.

References

1. Berdasco M., Esteller M.. Clinical epigenetics: seizing opportunities for translation. Nat. Rev. Genet. 2019; 20:109–127. [DOI] [PubMed] [Google Scholar]
2. Baylin S.B., Jones P.A.. A decade of exploring the cancer epigenome — biological and translational implications. Nat. Rev. Cancer. 2011; 11:726–734. [DOI] [PMC free article] [PubMed] [Google Scholar]
3. Yousefi P.D., Suderman M., Langdon R., Whitehurst O., Davey Smith G., Relton C.L.. DNA methylation-based predictors of health: applications and statistical considerations. Nat. Rev. Genet. 2022; 23:369–383. [DOI] [PubMed] [Google Scholar]
4. Song L., Jia J., Peng X., Xiao W., Li Y.. The performance of the SEPT9 gene methylation assay and a comparison with other CRC screening tests: a meta-analysis. Sci. Rep. 2017; 7:3032. [DOI] [PMC free article] [PubMed] [Google Scholar]
5. Van Neste L., Herman J.G., Otto G., Bigley J.W., Epstein J.I., Van Criekinge W.. The Epigenetic promise for prostate cancer diagnosis. Prostate. 2012; 72:1248–1261. [DOI] [PubMed] [Google Scholar]
6. Schmidt B., Liebenberg V., Dietrich D., Schlegel T., Kneip C., Seegebarth A., Flemming N., Seemann S., Distler J., Lewin J.et al.. SHOX2 DNA Methylation is a Biomarker for the diagnosis of lung cancer based on bronchial aspirates. BMC Cancer. 2010; 10:600. [DOI] [PMC free article] [PubMed] [Google Scholar]
7. Fan G., Tu Y., Chen C., Sun H., Wan C., Cai X.. DNA methylation biomarkers for hepatocellular carcinoma. Cancer Cell Int. 2018; 18:140. [DOI] [PMC free article] [PubMed] [Google Scholar]
8. de Almeida B.P., Apolónio J.D., Binnie A., Castelo-Branco P.. Roadmap of DNA methylation in breast cancer identifies novel prognostic biomarkers. BMC Cancer. 2019; 19:219. [DOI] [PMC free article] [PubMed] [Google Scholar]
9. Xu W., Xu M., Wang L., Zhou W., Xiang R., Shi Y., Zhang Y., Piao Y.. Integrative analysis of DNA methylation and gene expression identified cervical cancer-specific diagnostic biomarkers. Signal Transduct. Target Ther. 2019; 4:1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
10. Chen Y., Hu F., Zhou Y., Chen W., Shao H., Zhang Y.. MGMT promoter methylation and glioblastoma prognosis: a systematic review and meta-analysis. Arch. Med. Res. 2013; 44:281–290. [DOI] [PubMed] [Google Scholar]
11. Hao X., Luo H., Krawczyk M., Wei W., Wang W., Wang J., Flagg K., Hou J., Zhang H., Yi S.et al.. DNA methylation markers for diagnosis and prognosis of common cancers. Proc. Natl. Acad. Sci. U.S.A. 2017; 114:7414–7419. [DOI] [PMC free article] [PubMed] [Google Scholar]
12. Dong S., Li W., Wang L., Hu J., Song Y., Zhang B., Ren X., Ji S., Li J., Xu P.et al.. Histone-related genes are hypermethylated in lung cancer and hypermethylated HIST1H4F could serve as a Pan-Cancer biomarker. Cancer Res. 2019; 79:6101–6112. [DOI] [PubMed] [Google Scholar]
13. Dong S., Lu Q., Xu P., Chen L., Duan X., Mao Z., Zhang B., Sui L., Wang Y., Yu W.. Hypermethylated PCDHGB7 as a universal cancer only marker and its application in early cervical cancer screening. Clin. Transl. Med. 2021; 11:e457. [DOI] [PMC free article] [PubMed] [Google Scholar]
14. Dong S., Yang Z., Xu P., Zheng W., Zhang B., Fu F., Mao Z., Yuan J., Chen H., Yu W.. Mutually exclusive epigenetic modification on SIX6 with hypermethylation for precancerous stage and metastasis emergence tracing. Signal Transduct. Target Ther. 2022; 7:208. [DOI] [PMC free article] [PubMed] [Google Scholar]
15. Koch A., Joosten S.C., Feng Z., de Ruijter T.C., Draht M.X., Melotte V., Smits K.M., Veeck J., Herman J.G., Van Neste L.et al.. Analysis of DNA methylation in cancer: location revisited. Nat. Rev. Clin. Oncol. 2018; 15:459–466. [DOI] [PubMed] [Google Scholar]
16. Jain S., Chen S., Chang K.-C., Lin Y.-J., Hu C.-T., Boldbaatar B., Hamilton J.P., Lin S.Y., Chang T.-T., Chen S.-H.et al.. Impact of the Location of CpG Methylation within the GSTP1 Gene on Its Specificity as a DNA Marker for Hepatocellular Carcinoma. PLoS One. 2012; 7:e35789. [DOI] [PMC free article] [PubMed] [Google Scholar]
17. Jain S., Xie L., Boldbaatar B., Lin S.Y., Hamilton J.P., Meltzer S.J., Chen S.-H., Hu C.-T., Block T.M., Song W.et al.. Differential methylation of the promoter and first exon of the RASSF1A gene in hepatocarcinogenesis. Hepatol. Res. 2015; 45:1110–1123. [DOI] [PMC free article] [PubMed] [Google Scholar]
18. Li Q., Hermanson P.J., Springer N.M.. Lagrimini L.M. Maize: Methods and Protocols. 2018; New York, NY: Springer New York; 185–196. [Google Scholar]
19. Moran S., Arribas C., Esteller M.. Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences. Epigenomics. 2015; 8:389–399. [DOI] [PMC free article] [PubMed] [Google Scholar]
20. Barrett T., Wilhite S.E., Ledoux P., Evangelista C., Kim I.F., Tomashevsky M., Marshall K.A., Phillippy K.H., Sherman P.M., Holko M.et al.. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res. 2013; 41:D991–D995. [DOI] [PMC free article] [PubMed] [Google Scholar]
21. Katz K., Shutov O., Lapoint R., Kimelman M., Brister J.R., O’Sullivan C. The Sequence Read Archive: a decade more of explosive growth. Nucleic Acids Res. 2022; 50:D387–D390. [DOI] [PMC free article] [PubMed] [Google Scholar]
22. Buels R., Yao E., Diesh C.M., Hayes R.D., Munoz-Torres M., Helt G., Goodstein D.M., Elsik C.G., Lewis S.E., Stein L.et al.. JBrowse: a dynamic web platform for genome visualization and analysis. Genome Biol. 2016; 17:66. [DOI] [PMC free article] [PubMed] [Google Scholar]
23. Chen S. Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. Imeta. 2023; 2:e107. [DOI] [PMC free article] [PubMed] [Google Scholar]
24. Zhou Q., Lim J.-Q., Sung W.-K., Li G.. An integrated package for bisulfite DNA methylation data analysis with Indel-sensitive mapping. BMC Bioinf. 2019; 20:1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]
25. 1000 Genome Project Data Processing Subgroup Li H., Handsaker B., Wysoker A., Fennell T., Ruan J., Homer N., Marth G., Abecasis G., Durbin R.. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009; 25:2078–2079. [DOI] [PMC free article] [PubMed] [Google Scholar]
26. Zhou Q., Guan P., Zhu Z., Cheng S., Zhou C., Wang H., Xu Q., Sung W., Li G.. ASMdb: a comprehensive database for allele-specific DNA methylation in diverse organisms. Nucleic Acids Res. 2022; 50:D60–D71. [DOI] [PMC free article] [PubMed] [Google Scholar]
27. Liu H., Liu X., Zhang S., Lv J., Li S., Shang S., Jia S., Wei Y., Wang F., Su J.et al.. Systematic identification and annotation of human methylation marks based on bisulfite sequencing methylomes reveals distinct roles of cell type-specific hypomethylation in the regulation of cell identity genes. Nucleic Acids Res. 2016; 44:75–94. [DOI] [PMC free article] [PubMed] [Google Scholar]
28. Wang Q., Li M., Wu T., Zhan L., Li L., Chen M., Xie W., Xie Z., Hu E., Xu S.et al.. Exploring Epigenomic Datasets by ChIPseeker. Curr. Protoc. 2022; 2:e585. [DOI] [PubMed] [Google Scholar]
29. Chang K., Creighton C.J., Davis C., Donehower L., Drummond J., Wheeler D., Ally A., Balasundaram M., Birol I., Butterfield Y.S.N.et al.. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 2013; 45:1113–1120. [DOI] [PMC free article] [PubMed] [Google Scholar]
30. Lee B.T., Barber G.P., Benet-Pagès A., Casper J., Clawson H., Diekhans M., Fischer C., Gonzalez J.N., Hinrichs A.S., Lee C.M.et al.. The UCSC Genome Browser database: 2022 update. Nucleic Acids Res. 2022; 50:D1115–D1122. [DOI] [PMC free article] [PubMed] [Google Scholar]
31. Moritz S., Bartz-Beielstein T.. imputeTS: time series missing value imputation in R. R J. 2017; 9:207. [Google Scholar]
32. Therneau T.M., Lumley T.. Package ‘survival. R Top Doc. 2015; 128:28–33. [Google Scholar]
33. Chen T., Guestrin C.. Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining. 2016; 785–794. [DOI] [PMC free article] [PubMed] [Google Scholar]
34. Gaunt T.R., Shihab H.A., Hemani G., Min J.L., Woodward G., Lyttleton O., Zheng J., Duggirala A., McArdle W.L., Ho K.et al.. Systematic identification of genetic influences on methylation across the human life course. Genome Biol. 2016; 17:61. [DOI] [PMC free article] [PubMed] [Google Scholar]
35. Gong J., Wan H., Mei S., Ruan H., Zhang Z., Liu C., Guo A.-Y., Diao L., Miao X., Han L.. Pancan-meQTL: a database to systematically evaluate the effects of genetic variants on methylation in human cancer. Nucleic Acids Res. 2019; 47:D1066–D1072. [DOI] [PMC free article] [PubMed] [Google Scholar]
36. Landrum M.J., Chitipiralla S., Brown G.R., Chen C., Gu B., Hart J., Hoffman D., Jang W., Kaur K., Liu C.et al.. ClinVar: improvements to accessing data. Nucleic Acids Res. 2020; 48:D835–D844. [DOI] [PMC free article] [PubMed] [Google Scholar]
37. Piñero J., Ramírez-Anguita J.M., Saüch-Pitarch J., Ronzano F., Centeno E., Sanz F., Furlong L.I.. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 2020; 48:D845–D855. [DOI] [PMC free article] [PubMed] [Google Scholar]
38. Eicher J.D., Landowski C., Stackhouse B., Sloan A., Chen W., Jensen N., Lien J.-P., Leslie R., Johnson A.D.. GRASP v2.0: an update on the Genome-Wide Repository of Associations between SNPs and phenotypes. Nucleic Acids Res. 2015; 43:D799–D804. [DOI] [PMC free article] [PubMed] [Google Scholar]
39. Sollis E., Mosaku A., Abid A., Buniello A., Cerezo M., Gil L., Groza T., Güneş O., Hall P., Hayhurst J.et al.. The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. Nucleic Acids Res. 2023; 51:D977–D985. [DOI] [PMC free article] [PubMed] [Google Scholar]
40. Li M.J., Liu Z., Wang P., Wong M.P., Nelson M.R., Kocher J.-P.A., Yeager M., Sham P.C., Chanock S.J., Xia Z.et al.. GWASdb v2: an update database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res. 2016; 44:D869–D876. [DOI] [PMC free article] [PubMed] [Google Scholar]
41. Pan Q., Liu Y.-J., Bai X.-F., Han X.-L., Jiang Y., Ai B., Shi S.-S., Wang F., Xu M.-C., Wang Y.-Z.et al.. VARAdb: a comprehensive variation annotation database for human. Nucleic Acids Res. 2021; 49:D1431–D1444. [DOI] [PMC free article] [PubMed] [Google Scholar]
42. Gao T., Qian J.. EnhancerAtlas 2.0: an updated resource with enhancer annotation in 586 tissue/cell types across nine species. Nucleic Acids Res. 2020; 48:D58–D64. [DOI] [PMC free article] [PubMed] [Google Scholar]
43. Bai X., Shi S., Ai B., Jiang Y., Liu Y., Han X., Xu M., Pan Q., Wang F., Wang Q.et al.. ENdb: a manually curated database of experimentally supported enhancers for human and mouse. Nucleic Acids Res. 2020; 48:D51–D57. [DOI] [PMC free article] [PubMed] [Google Scholar]
44. Fishilevich S., Nudel R., Rappaport N., Hadar R., Plaschkes I., Iny Stein T., Rosen N., Kohn A., Twik M., Safran M.et al.. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Database. 2017; 2017:bax028. [DOI] [PMC free article] [PubMed] [Google Scholar]
45. Kawaji H., Kasukawa T., Forrest A., Carninci P., Hayashizaki Y.. The FANTOM5 collection, a data series underpinning mammalian transcriptome atlases in diverse cell types. Sci. Data. 2017; 4:170113. [DOI] [PMC free article] [PubMed] [Google Scholar]
46. Cai Z., Cui Y., Tan Z., Zhang G., Tan Z., Zhang X., Peng Y.. RAEdb: a database of enhancers identified by high-throughput reporter assays. Database. 2019; 2019:bay140. [DOI] [PMC free article] [PubMed] [Google Scholar]
47. Gao T., Zheng Z., Pan Y., Zhu C., Wei F., Yuan J., Sun R., Fang S., Wang N., Zhou Y.et al.. scEnhancer: a single-cell enhancer resource with annotation across hundreds of tissue/cell types in three species. Nucleic Acids Res. 2022; 50:D371–D379. [DOI] [PMC free article] [PubMed] [Google Scholar]
48. Chen C., Zhou D., Gu Y., Wang C., Zhang M., Lin X., Xing J., Wang H., Zhang Y.. SEA version 3.0: a comprehensive extension and update of the Super-Enhancer archive. Nucleic Acids Res. 2020; 48:D198–D203. [DOI] [PMC free article] [PubMed] [Google Scholar]
49. Visel A., Minovitsky S., Dubchak I., Pennacchio L.A.. VISTA Enhancer Browser—a database of tissue-specific human enhancers. Nucleic Acids Res. 2007; 35:D88–D92. [DOI] [PMC free article] [PubMed] [Google Scholar]
50. Khan A., Zhang X.. dbSUPER: a database of super-enhancers in mouse and human genome. Nucleic Acids Res. 2016; 44:D164–D171. [DOI] [PMC free article] [PubMed] [Google Scholar]
51. Zeng W., Chen S., Cui X., Chen X., Gao Z., Jiang R.. SilencerDB: a comprehensive database of silencers. Nucleic Acids Res. 2021; 49:D221–D228. [DOI] [PMC free article] [PubMed] [Google Scholar]
52. Zheng R., Wan C., Mei S., Qin Q., Wu Q., Sun H., Chen C.-H., Brown M., Zhang X., Meyer C.A.et al.. Cistrome Data Browser: expanded datasets and new tools for gene regulatory analysis. Nucleic Acids Res. 2019; 47:D729–D735. [DOI] [PMC free article] [PubMed] [Google Scholar]
53. Luo Y., Hitz B.C., Gabdank I., Hilton J.A., Kagda M.S., Lam B., Myers Z., Sud P., Jou J., Lin K.et al.. New developments on the Encyclopedia of DNA Elements (ENCODE) data portal. Nucleic Acids Res. 2020; 48:D882–D889. [DOI] [PMC free article] [PubMed] [Google Scholar]
54. Hammal F., de Langen P., Bergon A., Lopez F., Ballester B.. ReMap 2022: a database of Human, Mouse, Drosophila and Arabidopsis regulatory regions from an integrative analysis of DNA-binding sequencing experiments. Nucleic Acids Res. 2022; 50:D316–D325. [DOI] [PMC free article] [PubMed] [Google Scholar]
55. Kolmykov S., Yevshin I., Kulyashov M., Sharipov R., Kondrakhin Y., Makeev V.J., Kulakovskiy I.V., Kel A., Kolpakov F.. GTRD: an integrated view of transcription regulation. Nucleic Acids Res. 2021; 49:D104–D111. [DOI] [PMC free article] [PubMed] [Google Scholar]
56. Zou Z., Ohta T., Miura F., Oki S.. ChIP-Atlas 2021 update: a data-mining suite for exploring epigenomic landscapes by fully integrating ChIP-seq, ATAC-seq and Bisulfite-seq data. Nucleic Acids Res. 2022; 50:W175–W182. [DOI] [PMC free article] [PubMed] [Google Scholar]
57. Tang Z., Kang B., Li C., Chen T., Zhang Z.. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019; 47:W556–W560. [DOI] [PMC free article] [PubMed] [Google Scholar]
58. Lonsdale J., Thomas J., Salvatore M., Phillips R., Lo E., Shad S., Hasz R., Walters G., Garcia F., Young N.et al.. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 2013; 45:580–585. [DOI] [PMC free article] [PubMed] [Google Scholar]
59. Ghandi M., Huang F.W., Jané-Valbuena J., Kryukov G.V., Lo C.C., McDonald E.R., Barretina J., Gelfand E.T., Bielski C.M., Li H.et al.. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature. 2019; 569:503–508. [DOI] [PMC free article] [PubMed] [Google Scholar]
60. Quinlan A.R., Hall I.M.. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010; 26:841–842. [DOI] [PMC free article] [PubMed] [Google Scholar]
61. He Q., Huang W., Liu D., Zhang T., Wang Y., Ji X., Xie M., Sun M., Tian D., Liu M. Homeobox B5 promotes metastasis and poor prognosis in Hepatocellular Carcinoma, via FGFR4 and CXCL1 upregulation. Theranostics. 2021; 11:5759–5777. [DOI] [PMC free article] [PubMed] [Google Scholar]
62. Liu B., Ricarte Filho J., Mallisetty A., Villani C., Kottorou A., Rodgers K., Chen C., Ito T., Holmes K., Gastala N.et al.. Detection of Promoter DNA Methylation in Urine and Plasma Aids the Detection of Non–Small Cell Lung Cancer. Clin. Cancer Res. 2020; 26:4339–4348. [DOI] [PMC free article] [PubMed] [Google Scholar]
63. Dong X., Hou Q., Chen Y., Wang X.. Diagnostic Value of the Methylation of Multiple Gene Promoters in Serum in Hepatitis B Virus-Related Hepatocellular Carcinoma. Dis. Markers. 2017; 2017:2929381. [DOI] [PMC free article] [PubMed] [Google Scholar]
64. Hofmeister B.T., Schmitz R.J.. Enhanced JBrowse plugins for epigenomics data visualization. BMC Bioinf. 2018; 19:159. [DOI] [PMC free article] [PubMed] [Google Scholar]
65. Vidal I., Zheng Q., Hicks J.L., Chen J., Platz E.A., Trock B.J., Kulac I., Baena-Del Valle J.A., Sfanos K.S., Ernst S.et al.. GSTP1 positive prostatic adenocarcinomas are more common in Black than White men in the United States. PLoS One. 2021; 16:e0241934. [DOI] [PMC free article] [PubMed] [Google Scholar]
66. Henrique R., Jerónimo C.. Molecular Detection of Prostate Cancer: a Role for GSTP1 Hypermethylation. Eur. Urol. 2004; 46:660–669. [DOI] [PubMed] [Google Scholar]
67. Fiano V., Zugna D., Grasso C., Trevisan M., Delsedime L., Molinaro L., Cassoni P., Papotti M., Merletti F., Akre O.et al.. DNA methylation in repeat negative prostate biopsies as a marker of missed prostate cancer. Clin. Epigenetics. 2019; 11:152. [DOI] [PMC free article] [PubMed] [Google Scholar]
68. Costa V.L., Henrique R., Jerónimo C.. Epigenetic markers for molecular detection of prostate cancer. Dis. Markers. 2007; 23:31–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
69. Teng Y., Fan Y., Mu N., Zhao J., Sun F., Wang K.. Serum SOX11 promoter methylation is a novel biomarker for the diagnosis of Hepatitis B virus-related hepatocellular carcinoma. Neoplasma. 2016; 63:419–426. [DOI] [PubMed] [Google Scholar]
70. Weber L., Maßberg D., Becker C., Altmüller J., Ubrig B., Bonatz G., Wölk G., Philippou S., Tannapfel A., Hatt H.et al.. Olfactory receptors as biomarkers in human breast carcinoma tissues. Front. Oncol. 2018; 8:33. [DOI] [PMC free article] [PubMed] [Google Scholar]
71. Masjedi S., Zwiebel L.J., Giorgio T.D.. Olfactory receptor gene abundance in invasive breast carcinoma. Sci. Rep. 2019; 9:13736. [DOI] [PMC free article] [PubMed] [Google Scholar]
72. McLean C.Y., Bristor D., Hiller M., Clarke S.L., Schaar B.T., Lowe C.B., Wenger A.M., Bejerano G.. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 2010; 28:495–501. [DOI] [PMC free article] [PubMed] [Google Scholar]
73. Tanigawa Y., Dyer E.S., Bejerano G.. WhichTF is functionally important in your open chromatin data?. PLoS Comp. Biol. 2022; 18:e1010378. [DOI] [PMC free article] [PubMed] [Google Scholar]
74. Lu J., Johnston A., Berichon P., Ru K., Korbie D., Trau M.. PrimerSuite: a high-throughput web-based primer design program for multiplex bisulfite PCR. Sci. Rep. 2017; 7:41328. [DOI] [PMC free article] [PubMed] [Google Scholar]
75. Xu G., Zhou X., Xing J., Xiao Y., Jin B., Sun L., Yang H., Du S., Xu H., Mao Y.. Identification of RASSF1A promoter hypermethylation as a biomarker for hepatocellular carcinoma. Cancer Cell Int. 2020; 20:547. [DOI] [PMC free article] [PubMed] [Google Scholar]
76. Vega-Benedetti A.F., Loi E., Moi L., Blois S., Fadda A., Antonelli M., Arcella A., Badiali M., Giangaspero F., Morra I.et al.. Clustered protocadherins methylation alterations in cancer. Clin. Epigenetics. 2019; 11:100. [DOI] [PMC free article] [PubMed] [Google Scholar]
77. Huang H.-Y., Li J., Tang Y., Huang Y.-X., Chen Y.-G., Xie Y.-Y., Zhou Z.-Y., Chen X.-Y., Ding S.-Y., Luo M.-F.et al.. MethHC 2.0: information repository of DNA methylation and gene expression in human cancer. Nucleic Acids Res. 2021; 49:D1268–D1275. [DOI] [PMC free article] [PubMed] [Google Scholar]
78. Ding W., Chen J., Feng G., Chen G., Wu J., Guo Y., Ni X., Shi T.. DNMIVD: DNA methylation interactive visualization database. Nucleic Acids Res. 2020; 48:D856–D862. [DOI] [PMC free article] [PubMed] [Google Scholar]
79. Roy D., Tiirikainen M.. Diagnostic power of DNA methylation classifiers for early detection of cancer. Trends Cancer. 2020; 6:78–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
80. Chen X., Gole J., Gore A., He Q., Lu M., Min J., Yuan Z., Yang X., Jiang Y., Zhang T.et al.. Non-invasive early detection of cancer four years before conventional diagnosis using a blood test. Nat. Commun. 2020; 11:3475. [DOI] [PMC free article] [PubMed] [Google Scholar]
81. Smallwood S.A., Lee H.J., Angermueller C., Krueger F., Saadeh H., Peat J., Andrews S.R., Stegle O., Reik W., Kelsey G.. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods. 2014; 11:817–820. [DOI] [PMC free article] [PubMed] [Google Scholar]
82. Huang Y., Wang P., Zhou W., Luo M., Xu Z., Cheng R., Xu C., Jin X., Li Y., Jiang Q.. Comprehensive analysis of partial methylation domains in colorectal cancer based on single-cell methylation profiles. Brief. Bioinform. 2021; 22:bbab267. [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

gkad923_Supplemental_Files

Click here for additional data file.^{(2.6MB, zip)}

Data Availability Statement

MethMarkerDB is an online and open-access database, accessible at https://methmarkerdb.hzau.edu.cn.

[B1] 1. Berdasco M., Esteller M.. Clinical epigenetics: seizing opportunities for translation. Nat. Rev. Genet. 2019; 20:109–127. [DOI] [PubMed] [Google Scholar]

[B2] 2. Baylin S.B., Jones P.A.. A decade of exploring the cancer epigenome — biological and translational implications. Nat. Rev. Cancer. 2011; 11:726–734. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B3] 3. Yousefi P.D., Suderman M., Langdon R., Whitehurst O., Davey Smith G., Relton C.L.. DNA methylation-based predictors of health: applications and statistical considerations. Nat. Rev. Genet. 2022; 23:369–383. [DOI] [PubMed] [Google Scholar]

[B4] 4. Song L., Jia J., Peng X., Xiao W., Li Y.. The performance of the SEPT9 gene methylation assay and a comparison with other CRC screening tests: a meta-analysis. Sci. Rep. 2017; 7:3032. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B5] 5. Van Neste L., Herman J.G., Otto G., Bigley J.W., Epstein J.I., Van Criekinge W.. The Epigenetic promise for prostate cancer diagnosis. Prostate. 2012; 72:1248–1261. [DOI] [PubMed] [Google Scholar]

[B6] 6. Schmidt B., Liebenberg V., Dietrich D., Schlegel T., Kneip C., Seegebarth A., Flemming N., Seemann S., Distler J., Lewin J.et al.. SHOX2 DNA Methylation is a Biomarker for the diagnosis of lung cancer based on bronchial aspirates. BMC Cancer. 2010; 10:600. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B7] 7. Fan G., Tu Y., Chen C., Sun H., Wan C., Cai X.. DNA methylation biomarkers for hepatocellular carcinoma. Cancer Cell Int. 2018; 18:140. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B8] 8. de Almeida B.P., Apolónio J.D., Binnie A., Castelo-Branco P.. Roadmap of DNA methylation in breast cancer identifies novel prognostic biomarkers. BMC Cancer. 2019; 19:219. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B9] 9. Xu W., Xu M., Wang L., Zhou W., Xiang R., Shi Y., Zhang Y., Piao Y.. Integrative analysis of DNA methylation and gene expression identified cervical cancer-specific diagnostic biomarkers. Signal Transduct. Target Ther. 2019; 4:1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B10] 10. Chen Y., Hu F., Zhou Y., Chen W., Shao H., Zhang Y.. MGMT promoter methylation and glioblastoma prognosis: a systematic review and meta-analysis. Arch. Med. Res. 2013; 44:281–290. [DOI] [PubMed] [Google Scholar]

[B11] 11. Hao X., Luo H., Krawczyk M., Wei W., Wang W., Wang J., Flagg K., Hou J., Zhang H., Yi S.et al.. DNA methylation markers for diagnosis and prognosis of common cancers. Proc. Natl. Acad. Sci. U.S.A. 2017; 114:7414–7419. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B12] 12. Dong S., Li W., Wang L., Hu J., Song Y., Zhang B., Ren X., Ji S., Li J., Xu P.et al.. Histone-related genes are hypermethylated in lung cancer and hypermethylated HIST1H4F could serve as a Pan-Cancer biomarker. Cancer Res. 2019; 79:6101–6112. [DOI] [PubMed] [Google Scholar]

[B13] 13. Dong S., Lu Q., Xu P., Chen L., Duan X., Mao Z., Zhang B., Sui L., Wang Y., Yu W.. Hypermethylated PCDHGB7 as a universal cancer only marker and its application in early cervical cancer screening. Clin. Transl. Med. 2021; 11:e457. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B14] 14. Dong S., Yang Z., Xu P., Zheng W., Zhang B., Fu F., Mao Z., Yuan J., Chen H., Yu W.. Mutually exclusive epigenetic modification on SIX6 with hypermethylation for precancerous stage and metastasis emergence tracing. Signal Transduct. Target Ther. 2022; 7:208. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B15] 15. Koch A., Joosten S.C., Feng Z., de Ruijter T.C., Draht M.X., Melotte V., Smits K.M., Veeck J., Herman J.G., Van Neste L.et al.. Analysis of DNA methylation in cancer: location revisited. Nat. Rev. Clin. Oncol. 2018; 15:459–466. [DOI] [PubMed] [Google Scholar]

[B16] 16. Jain S., Chen S., Chang K.-C., Lin Y.-J., Hu C.-T., Boldbaatar B., Hamilton J.P., Lin S.Y., Chang T.-T., Chen S.-H.et al.. Impact of the Location of CpG Methylation within the GSTP1 Gene on Its Specificity as a DNA Marker for Hepatocellular Carcinoma. PLoS One. 2012; 7:e35789. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B17] 17. Jain S., Xie L., Boldbaatar B., Lin S.Y., Hamilton J.P., Meltzer S.J., Chen S.-H., Hu C.-T., Block T.M., Song W.et al.. Differential methylation of the promoter and first exon of the RASSF1A gene in hepatocarcinogenesis. Hepatol. Res. 2015; 45:1110–1123. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B18] 18. Li Q., Hermanson P.J., Springer N.M.. Lagrimini L.M. Maize: Methods and Protocols. 2018; New York, NY: Springer New York; 185–196. [Google Scholar]

[B19] 19. Moran S., Arribas C., Esteller M.. Validation of a DNA methylation microarray for 850,000 CpG sites of the human genome enriched in enhancer sequences. Epigenomics. 2015; 8:389–399. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B20] 20. Barrett T., Wilhite S.E., Ledoux P., Evangelista C., Kim I.F., Tomashevsky M., Marshall K.A., Phillippy K.H., Sherman P.M., Holko M.et al.. NCBI GEO: archive for functional genomics data sets—update. Nucleic Acids Res. 2013; 41:D991–D995. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B21] 21. Katz K., Shutov O., Lapoint R., Kimelman M., Brister J.R., O’Sullivan C. The Sequence Read Archive: a decade more of explosive growth. Nucleic Acids Res. 2022; 50:D387–D390. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B22] 22. Buels R., Yao E., Diesh C.M., Hayes R.D., Munoz-Torres M., Helt G., Goodstein D.M., Elsik C.G., Lewis S.E., Stein L.et al.. JBrowse: a dynamic web platform for genome visualization and analysis. Genome Biol. 2016; 17:66. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B23] 23. Chen S. Ultrafast one-pass FASTQ data preprocessing, quality control, and deduplication using fastp. Imeta. 2023; 2:e107. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B24] 24. Zhou Q., Lim J.-Q., Sung W.-K., Li G.. An integrated package for bisulfite DNA methylation data analysis with Indel-sensitive mapping. BMC Bioinf. 2019; 20:1–11. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B25] 25. 1000 Genome Project Data Processing Subgroup Li H., Handsaker B., Wysoker A., Fennell T., Ruan J., Homer N., Marth G., Abecasis G., Durbin R.. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009; 25:2078–2079. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B26] 26. Zhou Q., Guan P., Zhu Z., Cheng S., Zhou C., Wang H., Xu Q., Sung W., Li G.. ASMdb: a comprehensive database for allele-specific DNA methylation in diverse organisms. Nucleic Acids Res. 2022; 50:D60–D71. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B27] 27. Liu H., Liu X., Zhang S., Lv J., Li S., Shang S., Jia S., Wei Y., Wang F., Su J.et al.. Systematic identification and annotation of human methylation marks based on bisulfite sequencing methylomes reveals distinct roles of cell type-specific hypomethylation in the regulation of cell identity genes. Nucleic Acids Res. 2016; 44:75–94. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B28] 28. Wang Q., Li M., Wu T., Zhan L., Li L., Chen M., Xie W., Xie Z., Hu E., Xu S.et al.. Exploring Epigenomic Datasets by ChIPseeker. Curr. Protoc. 2022; 2:e585. [DOI] [PubMed] [Google Scholar]

[B29] 29. Chang K., Creighton C.J., Davis C., Donehower L., Drummond J., Wheeler D., Ally A., Balasundaram M., Birol I., Butterfield Y.S.N.et al.. The Cancer Genome Atlas Pan-Cancer analysis project. Nat. Genet. 2013; 45:1113–1120. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B30] 30. Lee B.T., Barber G.P., Benet-Pagès A., Casper J., Clawson H., Diekhans M., Fischer C., Gonzalez J.N., Hinrichs A.S., Lee C.M.et al.. The UCSC Genome Browser database: 2022 update. Nucleic Acids Res. 2022; 50:D1115–D1122. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B31] 31. Moritz S., Bartz-Beielstein T.. imputeTS: time series missing value imputation in R. R J. 2017; 9:207. [Google Scholar]

[B32] 32. Therneau T.M., Lumley T.. Package ‘survival. R Top Doc. 2015; 128:28–33. [Google Scholar]

[B33] 33. Chen T., Guestrin C.. Proceedings of the 22nd Acm Sigkdd International Conference on Knowledge Discovery and Data Mining. 2016; 785–794. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B34] 34. Gaunt T.R., Shihab H.A., Hemani G., Min J.L., Woodward G., Lyttleton O., Zheng J., Duggirala A., McArdle W.L., Ho K.et al.. Systematic identification of genetic influences on methylation across the human life course. Genome Biol. 2016; 17:61. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B35] 35. Gong J., Wan H., Mei S., Ruan H., Zhang Z., Liu C., Guo A.-Y., Diao L., Miao X., Han L.. Pancan-meQTL: a database to systematically evaluate the effects of genetic variants on methylation in human cancer. Nucleic Acids Res. 2019; 47:D1066–D1072. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B36] 36. Landrum M.J., Chitipiralla S., Brown G.R., Chen C., Gu B., Hart J., Hoffman D., Jang W., Kaur K., Liu C.et al.. ClinVar: improvements to accessing data. Nucleic Acids Res. 2020; 48:D835–D844. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B37] 37. Piñero J., Ramírez-Anguita J.M., Saüch-Pitarch J., Ronzano F., Centeno E., Sanz F., Furlong L.I.. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 2020; 48:D845–D855. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B38] 38. Eicher J.D., Landowski C., Stackhouse B., Sloan A., Chen W., Jensen N., Lien J.-P., Leslie R., Johnson A.D.. GRASP v2.0: an update on the Genome-Wide Repository of Associations between SNPs and phenotypes. Nucleic Acids Res. 2015; 43:D799–D804. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B39] 39. Sollis E., Mosaku A., Abid A., Buniello A., Cerezo M., Gil L., Groza T., Güneş O., Hall P., Hayhurst J.et al.. The NHGRI-EBI GWAS Catalog: knowledgebase and deposition resource. Nucleic Acids Res. 2023; 51:D977–D985. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B40] 40. Li M.J., Liu Z., Wang P., Wong M.P., Nelson M.R., Kocher J.-P.A., Yeager M., Sham P.C., Chanock S.J., Xia Z.et al.. GWASdb v2: an update database for human genetic variants identified by genome-wide association studies. Nucleic Acids Res. 2016; 44:D869–D876. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B41] 41. Pan Q., Liu Y.-J., Bai X.-F., Han X.-L., Jiang Y., Ai B., Shi S.-S., Wang F., Xu M.-C., Wang Y.-Z.et al.. VARAdb: a comprehensive variation annotation database for human. Nucleic Acids Res. 2021; 49:D1431–D1444. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B42] 42. Gao T., Qian J.. EnhancerAtlas 2.0: an updated resource with enhancer annotation in 586 tissue/cell types across nine species. Nucleic Acids Res. 2020; 48:D58–D64. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B43] 43. Bai X., Shi S., Ai B., Jiang Y., Liu Y., Han X., Xu M., Pan Q., Wang F., Wang Q.et al.. ENdb: a manually curated database of experimentally supported enhancers for human and mouse. Nucleic Acids Res. 2020; 48:D51–D57. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B44] 44. Fishilevich S., Nudel R., Rappaport N., Hadar R., Plaschkes I., Iny Stein T., Rosen N., Kohn A., Twik M., Safran M.et al.. GeneHancer: genome-wide integration of enhancers and target genes in GeneCards. Database. 2017; 2017:bax028. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B45] 45. Kawaji H., Kasukawa T., Forrest A., Carninci P., Hayashizaki Y.. The FANTOM5 collection, a data series underpinning mammalian transcriptome atlases in diverse cell types. Sci. Data. 2017; 4:170113. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B46] 46. Cai Z., Cui Y., Tan Z., Zhang G., Tan Z., Zhang X., Peng Y.. RAEdb: a database of enhancers identified by high-throughput reporter assays. Database. 2019; 2019:bay140. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B47] 47. Gao T., Zheng Z., Pan Y., Zhu C., Wei F., Yuan J., Sun R., Fang S., Wang N., Zhou Y.et al.. scEnhancer: a single-cell enhancer resource with annotation across hundreds of tissue/cell types in three species. Nucleic Acids Res. 2022; 50:D371–D379. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B48] 48. Chen C., Zhou D., Gu Y., Wang C., Zhang M., Lin X., Xing J., Wang H., Zhang Y.. SEA version 3.0: a comprehensive extension and update of the Super-Enhancer archive. Nucleic Acids Res. 2020; 48:D198–D203. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B49] 49. Visel A., Minovitsky S., Dubchak I., Pennacchio L.A.. VISTA Enhancer Browser—a database of tissue-specific human enhancers. Nucleic Acids Res. 2007; 35:D88–D92. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B50] 50. Khan A., Zhang X.. dbSUPER: a database of super-enhancers in mouse and human genome. Nucleic Acids Res. 2016; 44:D164–D171. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B51] 51. Zeng W., Chen S., Cui X., Chen X., Gao Z., Jiang R.. SilencerDB: a comprehensive database of silencers. Nucleic Acids Res. 2021; 49:D221–D228. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B52] 52. Zheng R., Wan C., Mei S., Qin Q., Wu Q., Sun H., Chen C.-H., Brown M., Zhang X., Meyer C.A.et al.. Cistrome Data Browser: expanded datasets and new tools for gene regulatory analysis. Nucleic Acids Res. 2019; 47:D729–D735. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B53] 53. Luo Y., Hitz B.C., Gabdank I., Hilton J.A., Kagda M.S., Lam B., Myers Z., Sud P., Jou J., Lin K.et al.. New developments on the Encyclopedia of DNA Elements (ENCODE) data portal. Nucleic Acids Res. 2020; 48:D882–D889. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B54] 54. Hammal F., de Langen P., Bergon A., Lopez F., Ballester B.. ReMap 2022: a database of Human, Mouse, Drosophila and Arabidopsis regulatory regions from an integrative analysis of DNA-binding sequencing experiments. Nucleic Acids Res. 2022; 50:D316–D325. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B55] 55. Kolmykov S., Yevshin I., Kulyashov M., Sharipov R., Kondrakhin Y., Makeev V.J., Kulakovskiy I.V., Kel A., Kolpakov F.. GTRD: an integrated view of transcription regulation. Nucleic Acids Res. 2021; 49:D104–D111. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B56] 56. Zou Z., Ohta T., Miura F., Oki S.. ChIP-Atlas 2021 update: a data-mining suite for exploring epigenomic landscapes by fully integrating ChIP-seq, ATAC-seq and Bisulfite-seq data. Nucleic Acids Res. 2022; 50:W175–W182. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B57] 57. Tang Z., Kang B., Li C., Chen T., Zhang Z.. GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis. Nucleic Acids Res. 2019; 47:W556–W560. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B58] 58. Lonsdale J., Thomas J., Salvatore M., Phillips R., Lo E., Shad S., Hasz R., Walters G., Garcia F., Young N.et al.. The Genotype-Tissue Expression (GTEx) project. Nat. Genet. 2013; 45:580–585. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B59] 59. Ghandi M., Huang F.W., Jané-Valbuena J., Kryukov G.V., Lo C.C., McDonald E.R., Barretina J., Gelfand E.T., Bielski C.M., Li H.et al.. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature. 2019; 569:503–508. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B60] 60. Quinlan A.R., Hall I.M.. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010; 26:841–842. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B61] 61. He Q., Huang W., Liu D., Zhang T., Wang Y., Ji X., Xie M., Sun M., Tian D., Liu M. Homeobox B5 promotes metastasis and poor prognosis in Hepatocellular Carcinoma, via FGFR4 and CXCL1 upregulation. Theranostics. 2021; 11:5759–5777. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B62] 62. Liu B., Ricarte Filho J., Mallisetty A., Villani C., Kottorou A., Rodgers K., Chen C., Ito T., Holmes K., Gastala N.et al.. Detection of Promoter DNA Methylation in Urine and Plasma Aids the Detection of Non–Small Cell Lung Cancer. Clin. Cancer Res. 2020; 26:4339–4348. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B63] 63. Dong X., Hou Q., Chen Y., Wang X.. Diagnostic Value of the Methylation of Multiple Gene Promoters in Serum in Hepatitis B Virus-Related Hepatocellular Carcinoma. Dis. Markers. 2017; 2017:2929381. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B64] 64. Hofmeister B.T., Schmitz R.J.. Enhanced JBrowse plugins for epigenomics data visualization. BMC Bioinf. 2018; 19:159. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B65] 65. Vidal I., Zheng Q., Hicks J.L., Chen J., Platz E.A., Trock B.J., Kulac I., Baena-Del Valle J.A., Sfanos K.S., Ernst S.et al.. GSTP1 positive prostatic adenocarcinomas are more common in Black than White men in the United States. PLoS One. 2021; 16:e0241934. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B66] 66. Henrique R., Jerónimo C.. Molecular Detection of Prostate Cancer: a Role for GSTP1 Hypermethylation. Eur. Urol. 2004; 46:660–669. [DOI] [PubMed] [Google Scholar]

[B67] 67. Fiano V., Zugna D., Grasso C., Trevisan M., Delsedime L., Molinaro L., Cassoni P., Papotti M., Merletti F., Akre O.et al.. DNA methylation in repeat negative prostate biopsies as a marker of missed prostate cancer. Clin. Epigenetics. 2019; 11:152. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B68] 68. Costa V.L., Henrique R., Jerónimo C.. Epigenetic markers for molecular detection of prostate cancer. Dis. Markers. 2007; 23:31–41. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B69] 69. Teng Y., Fan Y., Mu N., Zhao J., Sun F., Wang K.. Serum SOX11 promoter methylation is a novel biomarker for the diagnosis of Hepatitis B virus-related hepatocellular carcinoma. Neoplasma. 2016; 63:419–426. [DOI] [PubMed] [Google Scholar]

[B70] 70. Weber L., Maßberg D., Becker C., Altmüller J., Ubrig B., Bonatz G., Wölk G., Philippou S., Tannapfel A., Hatt H.et al.. Olfactory receptors as biomarkers in human breast carcinoma tissues. Front. Oncol. 2018; 8:33. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B71] 71. Masjedi S., Zwiebel L.J., Giorgio T.D.. Olfactory receptor gene abundance in invasive breast carcinoma. Sci. Rep. 2019; 9:13736. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B72] 72. McLean C.Y., Bristor D., Hiller M., Clarke S.L., Schaar B.T., Lowe C.B., Wenger A.M., Bejerano G.. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 2010; 28:495–501. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B73] 73. Tanigawa Y., Dyer E.S., Bejerano G.. WhichTF is functionally important in your open chromatin data?. PLoS Comp. Biol. 2022; 18:e1010378. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B74] 74. Lu J., Johnston A., Berichon P., Ru K., Korbie D., Trau M.. PrimerSuite: a high-throughput web-based primer design program for multiplex bisulfite PCR. Sci. Rep. 2017; 7:41328. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B75] 75. Xu G., Zhou X., Xing J., Xiao Y., Jin B., Sun L., Yang H., Du S., Xu H., Mao Y.. Identification of RASSF1A promoter hypermethylation as a biomarker for hepatocellular carcinoma. Cancer Cell Int. 2020; 20:547. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B76] 76. Vega-Benedetti A.F., Loi E., Moi L., Blois S., Fadda A., Antonelli M., Arcella A., Badiali M., Giangaspero F., Morra I.et al.. Clustered protocadherins methylation alterations in cancer. Clin. Epigenetics. 2019; 11:100. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B77] 77. Huang H.-Y., Li J., Tang Y., Huang Y.-X., Chen Y.-G., Xie Y.-Y., Zhou Z.-Y., Chen X.-Y., Ding S.-Y., Luo M.-F.et al.. MethHC 2.0: information repository of DNA methylation and gene expression in human cancer. Nucleic Acids Res. 2021; 49:D1268–D1275. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B78] 78. Ding W., Chen J., Feng G., Chen G., Wu J., Guo Y., Ni X., Shi T.. DNMIVD: DNA methylation interactive visualization database. Nucleic Acids Res. 2020; 48:D856–D862. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B79] 79. Roy D., Tiirikainen M.. Diagnostic power of DNA methylation classifiers for early detection of cancer. Trends Cancer. 2020; 6:78–81. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B80] 80. Chen X., Gole J., Gore A., He Q., Lu M., Min J., Yuan Z., Yang X., Jiang Y., Zhang T.et al.. Non-invasive early detection of cancer four years before conventional diagnosis using a blood test. Nat. Commun. 2020; 11:3475. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B81] 81. Smallwood S.A., Lee H.J., Angermueller C., Krueger F., Saadeh H., Peat J., Andrews S.R., Stegle O., Reik W., Kelsey G.. Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity. Nat. Methods. 2014; 11:817–820. [DOI] [PMC free article] [PubMed] [Google Scholar]

[B82] 82. Huang Y., Wang P., Zhou W., Luo M., Xu Z., Cheng R., Xu C., Jin X., Li Y., Jiang Q.. Comprehensive analysis of partial methylation domains in colorectal cancer based on single-cell methylation profiles. Brief. Bioinform. 2021; 22:bbab267. [DOI] [PubMed] [Google Scholar]

PERMALINK

MethMarkerDB: a comprehensive cancer DNA methylation biomarker database

Zhixian Zhu

Qiangwei Zhou

Yuanhui Sun

Fuming Lai

Zhenji Wang

Zhigang Hao

Guoliang Li