Epigenomics-Guided Multi-Omics Integration Uncovers a Lipid-Metabolic Signature with Translational Utility in Bladder...

Background: Bladder cancer (BLCA) exhibits marked heterogeneity, and current classifiers provide limited guidance for prognosis or treatment. Because epigenetic reprogramming and metabolic rewiring jointly shape BLCA biology, we sought to identify epigenomically informed biomarkers with functional relevance. Methods: Epigenome (genome-wide promoter DNA methylation) and matched transcriptome (RNA sequencing) profiles from tumor and adjacent normal samples were integrated to identify genes with concordant differential methylation and expression patterns. A survival-oriented machine learning framework distilled these candidates into a 25-gene signature. The prognostic performance was evaluated in 4 independent BLCA cohorts. Multilayer characterization included the computational inference of tumor-infiltrating immune cells and in silico drug sensitivity prediction. The functional relevance of key lipid metabolic hub genes was confirmed by pharmacological inhibition in BLCA cell line models, followed by colony formation and migration assays. Results: The signature, enriched for cell cycle regulation and lipid metabolism, stratified patients into high- and low-risk groups across the discovery and 4 validation datasets. The prognostic value remained independent of age, pathological stage, and common genomic alterations. Low-risk tumors exhibited computationally inferred immune-inflamed phenotypes, whereas high-risk tumors exhibited lower immune engagement and lower half-maximal inhibitory concentration values for several drugs. Network analysis identified fatty acid synthase and stearoyl-coenzyme A desaturase as central nodes; their inhibition reduced BLCA cell proliferation and migration, supporting pathway-level functional relevance. Conclusion: By integrating epigenomic and transcriptomic layers with machine learning, we delineated a lipid-centric 25-gene signature that delivers stage-independent prognostication, illuminates tumor-immune interactions, and nominates actionable therapeutic targets. This experimentally vetted multi-omics framework advances precision oncology for BLCA.