Integrating machine learning and structural analysis to decipher benzo[a]pyrene-induced bladder cancer networks
Xinzhao Zhao, Ruize Qin, Chengquan Shen, Ding Hu, Cheng Li, Changxue Liu, Huaixi Ge, Yonghua Wang · 2026
Read Integrating machine learning and structural analysis to decipher benzo[a]pyrene-induced bladder cancer networks by Xinzhao Zhao, Ruize Qin, Chengquan Shen, Ding Hu, Cheng Li, Changxue Liu, Huaixi Ge, Yonghua Wang (2026) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-13500443.txt.
Description
Xinzhao Zhao and co-authors map, in twenty twenty-six, the gene networks through which benzo[a]pyrene, a carcinogen in tobacco smoke and exhaust, may drive bladder cancer. They mined public gene expression data sets for genes changed by the compound. Network toxicology, machine learning, docking, dynamics simulations, and single-cell data were combined. Nineteen significant genes emerged, and seven were prioritized as key players. An explanation method ranked two genes, GSK3B and SKP2, as top contributors to the predictive model.
Single-cell data showed where those genes act in the tumor microenvironment. Simulations detailed how the compound binds its target proteins. The study names candidate mechanisms and therapeutic targets for this exposure-driven cancer.
Catalog record
| CREATOR | Xinzhao Zhao, Ruize Qin, Chengquan Shen, Ding Hu, Cheng Li, Changxue Liu, Huaixi Ge, Yonghua Wang |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 38 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |