A Predictive Framework Integrating Network Toxicology and Machine Learning Elucidates 6PPD-Quinone-Induced Glioblastoma Pathogenesis

Through machine learning analysis, we identified five core genes with special diagnostic performance, namely SCN2B, VIPR1, PAK1, MAP2K1 and SYNJ1. The AUC value of this integrated model in the validation queue reached 0.957, and its prediction accuracy is relatively high. From the analysis of SHAP interpretability, it can be found that MAP2K1 is the most influential predictor, and there are complex nonlinear relationships among core genes. Molecular docking simulations have provided structural evidence that can support the direct interaction between 6 PPD-Q and the target protein. The vina score ranges from − 5.9 to -8.2, and they have a strong specific binding affinity.