Toxicogenomic Characterization of Perfluorooctanoic Acid–Associated Bladder Carcinogenesis

Overview

This study integrates computational toxicological target prediction with large-scale transcriptomic data from bladder cancer to investigate molecular mechanisms underlying perfluorooctanoic acid (PFOA)-associated carcinogenesis. The research establishes a computational framework linking environmental chemical exposure to cancer-related gene expression patterns at a systems level.

Methods and approach

The investigation employs integrative bioinformatics methodology combining toxicological target prediction algorithms with transcriptomic datasets from bladder cancer cohorts. A nine-gene classifier was developed to capture molecular signatures reflecting intersections between predicted PFOA-associated toxicological pathways and bladder cancer transcriptional programs. Validation was performed across independent patient cohorts to assess classifier robustness and consistency.

Key Findings

The nine-gene classifier demonstrated strong and reproducible performance across independent bladder cancer cohorts. The identified transcriptional features capture molecular signatures that overlap with predicted PFOA-associated toxicological targets, establishing a quantifiable link between chemical exposure-related molecular pathways and bladder cancer biology. These findings provide systems-level characterization of transcriptional programs potentially dysregulated by PFOA exposure.

Implications

The computational framework establishes a methodological approach for bridging environmental chemical toxicology with cancer genomics. The identified transcriptional signatures may facilitate risk stratification and molecular subtyping of bladder cancers potentially associated with PFOA exposure. This systems-level perspective enables hypothesis generation regarding mechanisms by which persistent organic pollutants influence cancer-related gene expression programs.