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Integrated machine learning and experimental validation reveal S6K2 as a key target of 6PPD-quinone in bladder cancer
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Researchers used machine learning across 113 model combinations to identify S6K2 as the key molecular target linking the tire-wear chemical 6PPD-quinone to bladder cancer progression, then confirmed in cell experiments that 6PPD-Q upregulates S6K2 to drive tumor proliferation, migration, and invasion — effects reversed by silencing the gene.
Tire and Road Wear Particles (TRWP) are pervasive environmental contaminants, yet the molecular mechanisms linking their toxic derivative, 6PPD-quinone (6PPD-Q), to bladder cancer (BLCA) progression remain obscure. This study integrates network toxicology with experimental validation to elucidate this complex pathogenicity. We screened six representative TRWP compounds and utilized a comprehensive machine learning framework involving 113 model combinations, identifying the Gradient Boosting Machine (GBM) as the optimal classifier. Crucially, SHAP interpretability analysis revealed RPS6KB2 (S6K2) as a pivotal risk driver, while molecular docking demonstrated that 6PPD-Q exhibits superior binding affinity (Binding energy = -7.405 kcal/mol) to S6K2 compared to its parent compound. In vitro assays confirmed that S6K2 is upregulated in BLCA and essential for malignancy. Exposure of BLCA cells to 6PPD-Q dose-dependently upregulated S6K2, significantly (p < 0.05) promoting proliferation, migration, and invasion as evidenced by EdU and Transwell assays. Notably, S6K2 silencing effectively reversed these 6PPD-Q-induced malignant phenotypes. These findings provide the first evidence that 6PPD-Q drives BLCA progression via the specific upregulation of S6K2, offering a novel theoretical basis for assessing the health risks of TRWP exposure.
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