Integrating single-cell transcriptomics and machine learning reveals 4-aminobiphenyl exposure signatures and novel diagnostic biomarkers in bladder cancer.

backgroundBladder cancer ranks among the top four most common malignancies in men worldwide. Despite therapeutic advancements, metastatic cases remain associated with dismal survival rates, highlighting the critical importance of studying environmental carcinogenic factors such as 4-aminobiphenyl (4-ABP). Classified as a Group 1 carcinogen by IARC, this compound-found in tobacco smoke and industrial chemicals-induces DNA damage through aryl amine metabolism. However, its cell-type-specific molecular mechanisms in bladder carcinogenesis remain poorly characterized, particularly at single-cell resolution.

methodsOur study combined single-cell RNA sequencing data (n = 95,136 cells) with publicly available bulk transcriptomic datasets from the GEO repository. The computational workflow incorporated Harmony algorithm for batch effect correction, WGCNA for co-expression network construction, and a machine learning framework utilizing LASSO, SVM, and Random Forest algorithms for biomarker identification. Additionally, we performed molecular docking simulations to investigate 4-ABP-protein interactions and employed ssGSEA to characterize immune cell infiltration patterns in the tumor microenvironment.

resultsSingle-cell profiling uncovered distinct fibroblast and mast cell subpopulations exhibiting significant 4-ABP-associated transcriptional signatures (adjusted p = 2.22 × 10

conclusionThrough integrated multi-omics analyses, this investigation systematically elucidates 4-ABP's carcinogenic mechanisms while identifying clinically actionable biomarkers and molecular targets for precision medicine applications in bladder cancer prevention and treatment. These findings provide critical insights for developing targeted strategies to mitigate environmental carcinogenesis in susceptible populations. However, this study is limited by its retrospective nature and reliance on public sequencing data, which restricted access to detailed clinical metadata and precluded survival analysis for the identified subtypes.