Deciphering the role of air pollution in hepatocellular carcinogenesis: insights from network toxicology and machine learning approaches

Air pollution has emerged as a major public health concern, being implicated in the pathogenesis of diverse diseases. This study investigates the underlying associations between air pollutants and hepatocellular carcinoma (HCC). Target genes associated with both air pollutants and HCC were obtained through integration of multiple bioinformatics databases. Protein-protein interaction (PPI) network construction and visualization were performed on overlapping genes, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to elucidate potential mechanistic pathways. A predictive model was developed using LASSO regression and validated on external datasets. Core genes were identified through consensus across multiple topological algorithms, with their clinical relevance further explored. Finally, molecular docking analysis was conducted to investigate interaction patterns between core genes and airborne pollutants at the structural level. Integrated multi-database analyses identified 48 air pollutant-HCC associated genes. Functional enrichment through GO and KEGG revealed these targets predominantly regulate apoptosis and oncogenic pathways. Survival analysis demonstrated robust predictive capacity of the LASSO-derived prognostic model, which maintained strong stability and reliability in external validation cohorts. CDK1 and TERT emerged as core genes from multi-algorithm screening, strongly correlated with HCC pathological progression. Molecular docking simulations further confirmed stable binding conformations between particulate matter components and these core targets at atomic resolution. Our study elucidates the critical roles of CDK1 and TERT between air pollutants and hepatocellular carcinoma, thereby providing novel molecular insights into air pollution-driven hepatocarcinogenesis.