Exploring the Potential Molecular Mechanisms of Eight Environmental Pollutants in Lung Adenocarcinoma through Network Toxicology, Machine Learning, and Multi-Omics Analysis
Epidemiological studies have established a significant association between exposure to environmental pollutants (EP) and the risk of lung adenocarcinoma (LUAD). This study integrates network toxicology and multi-omics analysis to elucidate the EP-LUAD molecular regulatory network and identify key regulatory genes, thereby revealing novel mechanisms of environmental carcinogenesis. Transcriptomic data from GEO and TCGA databases yielded 4,971 and 4,488 disease-related targets, respectively. Integration of toxicology databases (TargetNet, Swiss Target Prediction, CTD, SEA) identified 24,860 potential targets for eight common pollutants (SO2, NO, CO, NO2, O3, benzene, toluene, and polycyclic aromatic hydrocarbons). Intersection of these datasets produced 1,536 EP-LUAD common target genes. Protein-protein interaction network analysis identified 247 core targets. Machine learning selected five key genes: AGER, CAV1, CD44, CEP55, and GNB3, which demonstrated robust diagnostic and prognostic efficacy. Their expression correlated with immune cell infiltration, including CD4+ memory T cells and macrophages. Single-cell RNA sequencing revealed epithelial cell-specific expression patterns. Molecular docking confirmed stable pollutant-target binding, with PAH showing highest affinity for CD44 (binding energy −9.32 kcal·mol−1) and GNB3 (−8.32 kcal·mol−1). These findings establish AGER, CAV1, CD44, CEP55, and GNB3 as core molecular mediators of pollution-related LUAD. The high-affinity binding of PAH to CD44 and GNB3 underscores its carcinogenic potential. This study constructs a multi-level regulatory network for EP-LUAD, revealing underlying molecular mechanisms and providing novel potential targets and theoretical basis for early warning and intervention.