Evidence map›Paper›PMID 42168757›Full record

ArticleDiscover oncology2026

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

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Xinzhao ZhaoDepartment of Urology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Ruize QinDepartment of Urology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Chengquan ShenDepartment of Urology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Ding HuDepartment of Urology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Cheng LiDepartment of Urology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Changxue LiuDepartment of Urology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Huaixi GeDepartment of Urology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China.
Yonghua WangDepartment of Urology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, China. wangyonghua@qdu.edu.cn.

Funding

National Key Research and Development Program of China 2023YFF0714404Taishan Scholar Program of Shandong province tsqn202306394
6 · The paper itself

Abstract

Benzo[a]pyrene (BaP), a polycyclic aromatic hydrocarbon from tobacco smoke, exhaust, and pollutants, is linked to bladder cancer (BLCA). We systematically analyzed GEO datasets to identify BaP-related differentially expressed genes (DEBRGs). By integrating network toxicology, machine learning, molecular docking, molecular dynamics, and single-cell transcriptomics, we identified 19 significant genes, among which 7 key DEBRGs were prioritized (GSK3B, SKP2, AURKB, EPHB4, KIT, NR3C2, and CA2) in BaP-mediated BLCA. SHAP analysis highlighted GSK3B and SKP2 as important genes contributing to the predictive model. Single-cell data revealed their cell-type specific expression in the tumor microenvironment. Molecular simulations detailed interactions between BaP and target proteins. This study identified critical genes in BaP-induced bladder carcinogenesis, offering insights into underlying molecular mechanisms and potential therapeutic targets.

Indexed as

Benzo[a]pyreneBladder cancerMachine learningMolecular dockingMolecular dynamics simulations

Identifiers

PMID42168757
PMCPMC13500443

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.