综合机器学习和实验验证揭示了S6K2是6PPD-醌在膀胱癌症中的关键靶点
Integrated machine learning and experimental validation reveal S6K2 as a key target of 6PPD-quinone in bladder cancer.
摘要(中文译文)
轮胎与道路磨损颗粒（TRWP）是普遍存在的环境污染物，然而其毒性衍生物6PPD-醌（6PPD-Q）与膀胱癌（BLCA）进展相关的分子机制仍不清楚。本研究将网络毒理学与实验验证相结合，以阐明这一复杂的致病机制。我们筛选了六种具有代表性的TRWP化合物，并利用包含113种模型组合的综合机器学习框架，确定梯度提升机（GBM）为最优分类器。至关重要的是，SHAP可解释性分析揭示RPS6KB2（S6K2）是关键的风险驱动因子，而分子对接表明，与母体化合物相比，6PPD-Q对S6K2具有更优的结合亲和力（结合能 = -7.405 kcal/mol）。体外实验证实，S6K2在BLCA中表达上调，且为恶性表型所必需。将BLCA细胞暴露于6PPD-Q可剂量依赖性地上调S6K2，EdU和Transwell实验证实其显著（p < 0.05）促进增殖、迁移和侵袭。值得注意的是，沉默S6K2可有效逆转这些6PPD-Q诱导的恶性表型。这些发现首次提供证据表明6PPD-Q通过特异性上调S6K2驱动BLCA进展，为评估TRWP暴露的健康风险提供了新的理论依据。
Abstract
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.
如何引用
AMA
Jirong Wang, Meng Lu, Nienie Qi, Meng Zhang, Zixiang Li, Hailong Li, et al. Integrated machine learning and experimental validation reveal S6K2 as a key target of 6PPD-quinone in bladder cancer.. Ecotoxicology and environmental safety. 2026; doi:10.1016/j.ecoenv.2026.120085.
APA
Jirong Wang, Meng Lu, Nienie Qi, Meng Zhang, Zixiang Li, Hailong Li, et al (2026). Integrated machine learning and experimental validation reveal S6K2 as a key target of 6PPD-quinone in bladder cancer.. Ecotoxicology and environmental safety. https://doi.org/10.1016/j.ecoenv.2026.120085
GB/T 7714
Jirong Wang, Meng Lu, Nienie Qi, Meng Zhang, Zixiang Li, Hailong Li, et al. Integrated machine learning and experimental validation reveal S6K2 as a key target of 6PPD-quinone in bladder cancer.[J]. Ecotoxicology and environmental safety, 2026 doi:10.1016/j.ecoenv.2026.120085.
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