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. 2024 Jan 5;52(D1):D1380-D1392.
doi: 10.1093/nar/gkad923.

MethMarkerDB: a comprehensive cancer DNA methylation biomarker database

Zhixian Zhu  1   2 , Qiangwei Zhou  1   2 , Yuanhui Sun  1   2 , Fuming Lai  1   2 , Zhenji Wang  1   2 , Zhigang Hao  1   2 , Guoliang Li  1   2
Affiliations

MethMarkerDB: a comprehensive cancer DNA methylation biomarker database

Zhixian Zhu et al. Nucleic Acids Res. .

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.

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Figures

Graphical Abstract
Graphical Abstract
Figure 1.
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.
Figure 2.
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).
Figure 3.
Figure 3.
Gene search analysis. (A) Gene search page. (B) Dot plot displaying the DMRs in the region 3 kb upstream and 3 kb downstream around the GSTP1 gene in prostate cancer. (C) Detailed information about the selected DMRs displayed in (B). The red box highlights the DMRs described in (D) and (E). (D) Box plot showing DNA methylation levels in selected prostate cancer and normal samples. (E) Bar plot displaying the DNA methylation levels in selected prostate cancer and normal samples. Colors indicate the DNA methylation levels. (F) Statistics of annotation information about the GSTP1 gene. Enh for enhancers, activeEnh for active enhancers, superEnh for super enhancers, cancerEnh for cancer enhancers, diseaseEnh for disease enhancers, TF for transcription factors, atac for assays for transposase-accessible chromatin, dhs for DNase hypersensitive sites. (G) The expression level of GSTP1 from the GEPIA2 database. The red box highlights the gene expression level of GSTP1 in prostate cancer (PRAD).
Figure 4.
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.
Figure 5.
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.
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