Network toxicology & molecular docking: endocrine-disrupting chemicals induce prostate cancer - a system study.

OBJECTIVE

To elucidate the molecular mechanisms by which endocrine-disrupting chemicals (EDCs) initiate and sustain prostate carcinogenesis, thereby establishing a mechanistic foundation for the early detection and targeted intervention of castration-resistant prostate cancer (CRPC).

MATERIALS AND

METHODS

A total of 402 transcriptomic profiles from public GEO cohorts were integrated. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and network toxicology were jointly applied to prioritize candidate targets. Subsequently, an explainable XGBoost-SHAP machine-learning framework was employed to distill the core gene signature. The interaction affinities between selected EDCs and the corresponding proteins were computationally validated by molecular docking, with binding free energy (ΔG) serving as the quantitative metric.

RESULTS

Five genes - NR3C1, CALM1, MET, STAT3 and CES1 - were identified as robust diagnostic biomarkers across multiple independent cohorts (AUC > 0.90). All five exhibited high-affinity binding to representative EDCs (ΔG < -7 kcal mol-1).

CONCLUSIONS

For the first time, a seamless "transcriptome-network toxicology-structural biology" causal chain was established. By integrating explainable artificial intelligence with structural biology, this study closes a critical knowledge gap in the systems-level mechanism linking EDC exposure to prostate cancer initiation and progression, and offers novel, actionable targets for risk stratification and precision prevention.