Cell biology and toxicology · 2026
Toxicogenomic Characterization of Perfluorooctanoic Acid-Associated Bladder Carcinogenesis.
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Liu Yang, Tang Aifa, Wang Han
Аннотация
OBJECTIVE: This 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.
METHODS: Bioinformatics 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.
RESULTS: A 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.
CONCLUSION: By 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.
METHODS: Bioinformatics 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.
RESULTS: A 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.
CONCLUSION: By 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.