ArticleNature communications2024
Integrating muti-omics data to identify tissue-specific DNA methylation biomarkers for cancer risk.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Causal inference in the diagnosis and prognosis of ovarian cancer: current state and future directions.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Pooled it
- Population epigenetics: deciphering DNA methylation diversity and its implications for health, disease, and evolution.Molecular biology and evolution · 2026Review
- Why are Some Tissues More Vulnerable? Revisiting Tissue Specificity in Hereditary Cancer Syndromes.Molecular diagnosis & therapy · 2026Review
- TiSMeD: A tissue-specific methylation and expression database for biomarker and translational applications.Molecular therapy. Nucleic acids · 2026Article
- Identifying expression and DNA methylation biomarkers for lung adenocarcinoma risk in East Asia.Journal of the National Cancer Institute · 2026Article
- From Spatial Epigenomes to Clinical Diagnostics: Integrative Methylomics Across Scales and Modalities.International journal of molecular sciences · 2026Review
- Circulating Tumor DNA as a Biomarker in Pancreatic Cancer: Clinical Applications and Challenges.Clinical and translational gastroenterology · 2026Review
- CA-CAE: A deep learning-based multi-omics model for pan-cancer subtype classification and prognosis prediction.PLoS computational biology · 2026Article
- Mendelian randomization analysis of CpG methylation and immune phenotypes in epithelial ovarian cancer outcomes.Epigenetics · 2025Article
- Function of epigenetic modifications in wound healing and potential therapies (Review).International journal of molecular medicine · 2025Review
- HINN: Hierarchical Input Neural Network identifies multi-omics biomarker for cognitive decline.Research square · 2025Article
- Artificial intelligence for comprehensive DNA methylation analysis: overview, challenges, and future directions.Briefings in bioinformatics · 2025Review
- Development and validation of anTranslational cancer research · 2025Article
- The expression and prognosis for Aurora kinases in human non-small cell lung cancer.Discover oncology · 2025Article
- Integrating muti-omics data to identify tissue-specific DNA methylation biomarkers for cancer risk.Nature communications · 2024Article
- Contemplating the prognostic and therapeutic potential of CD19: a comprehensive analysis across diverse cancer types.American journal of translational research · 2024Article
Corrections and comments
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Authors and funding
23 authors.
Funding
Abstract
The relationship between tissue-specific DNA methylation and cancer risk remains inadequately elucidated. Leveraging resources from the Genotype-Tissue Expression consortium, here we develop genetic models to predict DNA methylation at CpG sites across the genome for seven tissues and apply these models to genome-wide association study data of corresponding cancers, namely breast, colorectal, renal cell, lung, ovarian, prostate, and testicular germ cell cancers. At Bonferroni-corrected P < 0.05, we identify 4248 CpGs that are significantly associated with cancer risk, of which 95.4% (4052) are specific to a particular cancer type. Notably, 92 CpGs within 55 putative novel loci retain significant associations with cancer risk after conditioning on proximal signals identified by genome-wide association studies. Integrative multi-omics analyses reveal 854 CpG-gene-cancer trios, suggesting that DNA methylation at 309 distinct CpGs might influence cancer risk through regulating the expression of 205 unique cis-genes. These findings substantially advance our understanding of the interplay between genetics, epigenetics, and gene expression in cancer etiology.
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