From Food Contaminant to Therapeutic Target: Identification of KCNE2 and 5-Azacytidine for Gastric Cancer via Multi-Omics, Machine Learning, and In Vitro Validation.

Background: Benzo[a]pyrene (BaP), a common food contaminant, is a recognized gastric carcinogen. This study aimed to identify therapeutic targets and repurposed drugs for gastric cancer (GC) using BaP as a network toxicology query. Methods: An integrated strategy combining network toxicology, multi-omics, machine learning (Random Forest, LASSO, SVM-RFE), and experimental validation was applied. Results: By intersecting GC-associated genes with BaP-related targets and machine learning, we identified three hub genes. The logistic regression model further revealed KCNE2 as a protective factor (OR = 0.515, 95% CI: 0.383-0.692), while SULF1 (OR = 2.940, 95% CI: 1.399-6.179) and TIMP1 (OR = 5.351, 95% CI: 2.020-16.743) were identified as potential risk factors. Survival analysis confirmed their prognostic significance. Single-cell transcriptomics descriptively showed TIMP1 and SULF1 enrichment in malignant/stromal cells and fibroblasts, respectively, whereas KCNE2 was restricted to normal epithelial cells and silenced in tumors. GSVA implicated epigenetic regulation, ECM remodeling, and TGF-β signaling. Molecular docking and dynamics simulations suggested that BaP can form stable complexes with DNMT1 and DNMT3A. Accordingly, drug enrichment analysis identified DNMT inhibitor 5-azacytidine as a top candidate. Cellular experiments confirmed that 5-azacytidine selectively inhibited GC cells and was associated with modulation of the DNMT3A-KCNE2 axis. Conclusions: Our findings provide a novel molecular target and a repurposed drug for GC from the perspective of a food contaminant.