From Target Prediction to Mechanistic Insights: Revealing Air Pollution-Driven Mechanisms in Endometrial Cancer via Interpretable Machine Learning and Molecular Docking - Hongyao Liu; Yueqing Zou (Atmosphere)
Air pollution is a known contributor to cancer risk, although its specific impact on endometrial cancer (EC) remains unclear. This study integrates network toxicology, transcriptomics, molecular docking, and machine learning to investigate pollutant–gene interactions in EC. We identify 83 air pollution-associated EC genes (APECGs), with TNF, ESR1, IL1B, NFKB1, and PTGS2 as the hub genes. A 13-gene RSF-SuperPC model, including CCNE1, SLC2A1, AHCY, and CDC25C, shows effective prognostic stratification. Molecular docking reveals strong binding between pollutants (e.g., benzene, toluene, and ethylbenzene) and key APECGs. The enrichment and SHAP analyses suggest that pollutant-driven EC progression involves DNA damage, metabolic reprogramming, epigenetic dysregulation, immune suppression, and inflammation. These findings reveal potential mechanisms linking air pollution to EC and support the development of biomarkers for high-exposure populations. Further experimental and epidemiological validation is needed to enable clinical translation.