Integrative multi-omic analysis identifies genetically influenced DNA methylation biomarkers for breast and prostate cancers

Aberrant DNA methylation has emerged as a hallmark in several cancers and contributes to risk, oncogenesis, progression, and prognosis. In this study, we performed imputation-based and conventional methylome-wide association analyses for breast cancer (BrCa) and prostate cancer (PrCa). The imputation-based approach identified DNA methylation at cytosine-phosphate-guanine sites (CpGs) associated with BrCa and PrCa risk utilising genome-wide association summary statistics (N(BrCa) = 228,951, N(PrCa) = 140,254) and prebuilt methylation prediction models, while the conventional approach identified CpG associations utilising TCGA and GEO experimental methylation data (N(BrCa) = 621, N(PrCa) = 241). Enrichment analysis of the association results implicated 77 and 81 genetically influenced CpGs for BrCa and PrCa, respectively. Furthermore, analysis of differential gene expression around these CpGs suggests a genome-epigenome-transcriptome mechanistic relationship. Conditional analyses identified multiple independent secondary SNP associations (P(cond) < 0.05) around 28 BrCa and 22 PrCa CpGs. Cross-cancer analysis identified eight common CpGs, including a strong therapeutic target in SREBF1 (17p11.2)—a key player in lipid metabolism. These findings highlight the utility of integrative analysis of multi-omic cancer data to identify robust biomarkers and understand their regulatory effects on cancer risk.