Increasing evidence shows that epistasis, defined as interactive effects between genetic loci, may contribute to the missing heritability of cancer. However, systematic genome-wide epistasis identification in cancer remains challenging. Here, by leveraging genotype and clinical data from 380,983 samples in the UK Biobank, we identified 202,032 candidate epistatic single nucleotide polymorphism (epiSNP) pairs associated with cancer risk across 16 cancer types. Notably, multivariable Cox regression identified 123 epiSNP pairs with significant interaction effects on overall survival, suggesting that interaction-level genetic signals can provide prognostic information beyond individual SNP effects. Through functional analysis of the 202,032 candidate epiSNP pairs, we identified 7152 pairs supported by gene co-expression data and 12,326 pairs with protein–protein interaction (PPI) evidence. By mapping epiSNP pairs to corresponding gene pairs and then linking these gene pairs to drug–target databases, we identified 1040 epistatic gene pairs with FDA-approved drug–target records. Additionally, through KM survival analysis of the candidate epiSNP pairs, we detected 7068 pairs significantly associated with patient overall survival. Finally, we constructed an open-access database, EpiSNPdb, to facilitate cancer epistasis research.

Pleiotropic genetic loci have been increasingly reported in cancer, and identifying genetic variants with pleiotropic associations can reveal shared biological pathways influencing multiple cancers. Using summary statistics from genome-wide association studies for 37 cancer types (N = 433 836), we identified extensive genome-wide and local genetic correlations among cancers. Through pairwise pleiotropic analysis, we identified 75 243 significant pleiotropic single nucleotide polymorphisms (SNPs) across 372 cancer pairs, among which 3472 were lead SNPs with potential regulatory functions. Using FUMA and MAGMA, we identified 2527 pleiotropic risk loci and 4272 candidate pleiotropic genes. Notably, genes such as TERT (5p15.33), POU5F1B (8q24.21), and FANCA (16q24.3) exhibited widespread pleiotropy across multiple cancer types. Pathway enrichment analysis highlighted the critical roles of pigment synthesis, metabolism, and apoptosis in skin-related cancers, while cross-cancer enrichment analysis emphasized pathways related to apoptosis, chromatin structure, and intermediate filaments. We also identified 33 novel functional genes harboring previously unreported cancer risk variants. Drug-gene interaction analysis revealed several repositionable FDA-approved drugs. Importantly, drug sensitivity assays demonstrated that bosutinib and cobimetinib exhibited promising therapeutic potential in breast cancer cell lines. Finally, we developed the PleioCancer database (https://gonglab.hzau.edu.cn/PleioCancer/), providing a comprehensive resource for cancer pleiotropy research. These findings have important implications for carcinogenesis cancer, prevention and treatment.

The identification of cancer prognostic biomarkers is crucial for predicting disease progression, optimizing personalized therapies, and improving patient survival. Molecular biomarkers are increasingly being identified for cancer prognosis estimation. However, existing studies and databases often focus on single-type molecular biomarkers, deficient in comprehensive multi-omics data integration, which constrains the comprehensive exploration of biomarkers and underlying mechanisms. To fill this gap, we conducted a systematic prognostic analysis using over 10,000 samples across 33 cancer types from The Cancer Genome Atlas (TCGA). Our study integrated nine types of molecular biomarker-related data: single-nucleotide polymorphism (SNP), copy number variation (CNV), alternative splicing (AS), alternative polyadenylation (APA), coding gene expression, DNA methylation, lncRNA expression, miRNA expression, and protein expression. Using log-rank tests, univariate Cox regression (uni-Cox), and multivariate Cox regression (multi-Cox), we evaluated potential biomarkers associated with four clinical outcome endpoints: overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). As a result, we identified 4,498,523 molecular biomarkers significantly associated with cancer prognosis. Finally, we developed SurvDB, an interactive online database for data retrieval, visualization, and download, providing a comprehensive resource for biomarker discovery and precision oncology research.