Exploring the Potential Molecular Mechanisms of Eight Environmental Pollutants in Lung Adenocarcinoma through Network Toxicology, Machine Learning, and Multi-Omics Analysis

• • Identified 1,536 common EP-LUAD target genes from intersection of 4,971 GEO, 4,488 TCGA, and 24,860 toxicology database targets, providing a comprehensive molecular basis for environmental carcinogenesis. • • Machine learning pinpointed five key genes (AGER, CAV1, CD44, CEP55, GNB3) with high diagnostic and prognostic value, enabling potential early detection biomarkers for LUAD. • • Molecular docking revealed PAH binds CD44 with binding energy −9.32 kcal·mol−1 and GNB3 with −8.32 kcal·mol−1, indicating strong carcinogenic interactions and specific pollutant-gene affinities. • • Single-cell RNA sequencing demonstrated epithelial cell-specific expression of key genes, linking pollutant exposure to cell-type-specific vulnerabilities in lung adenocarcinoma.