ArticleCell biology and toxicology2026
Toxicogenomic Characterization of Perfluorooctanoic Acid-Associated Bladder Carcinogenesis.
Article in Cell biology and toxicology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Exploring the mechanism by which triphenyl phosphate promotes malignant phenotypes in bladder and kidney cancer through MMP9 based on bioinformatics analysis and experimental validation.Clinical and experimental medicine · 2026Article
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3 authors.
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Abstract
objectiveThis study aims to bridge toxicological target prediction and bladder cancer transcriptomics by systematically identifying molecular signatures and pathways that converge between PFOA-associated toxicological effects and bladder cancer biology, using an integrative multi-cohort computational framework.
methodsBioinformatics strategies were applied to assemble 9,591 putative molecular targets associated with perfluorooctanoic acid (PFOA) from five independent databases. Differential expression analysis and weighted gene co-expression network analysis were then conducted across five bladder cancer cohorts to delineate tumor-related genes. Functional enrichment analyses, machine learning-based modeling with SHAP interpretation, and molecular docking were subsequently employed to explore affected biological pathways and evaluate predictive performance.
resultsA total of 69 candidate genes, predominantly associated with cell cycle control and DNA damage-related processes, were delineated. From these, a nine-gene classifier yielded excellent discriminatory performance, achieving an AUC of 0.986 in the training set and maintaining robust accuracy across multiple external cohorts (AUCs: 0.944-1.000). SHAP-based interpretability analyses identified MCM7 as the most influential contributor to bladder cancer classification. In silico docking further suggested a strong predicted interaction between PFOA and IGFBP2, with a binding energy of -13.0 kcal/mol.
conclusionBy integrating toxicological target prediction with large-scale bladder cancer transcriptomic analyses, this study provides a computational bridge between environmental chemical exposure and cancer-related molecular programs. The resulting nine-gene classifier demonstrates strong and consistent performance across independent cohorts and captures transcriptional features that intersect with PFOA-associated toxicological pathways, offering a systems-level, hypothesis-generating perspective on transcriptional programs that overlap between predicted PFOA-associated targets and bladder cancer biology.
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