Integrated multi-omics data and machine learning approaches to decipher the molecular network and gene signatures of renal cell carcinoma induced by aristolochic acid.

OBJECTIVE: This study aims to delineate the molecular mechanisms through which aristolochic acid (AA) exposure drives renal cell carcinoma (RCC) pathogenesis, leveraging an integrated machine learning (ML) and multi-omics approach to systematically identify and validate key targets linking AA to RCC development. METHODS: By conducting differential expression analysis on multiple relevant datasets, we precisely screened for target genes closely associated with RCC. A multi-disciplinary approach was adopted, incorporating machine learning algorithms, network toxicology and molecular docking techniques, in order to elucidate the binding interactions between AA and its target proteins. RESULTS: Machine learning analysis identified eight core genes (HPD, SORD, CYP4F2, ERBB4, ALAD, PLAU, PYGL, KCNK5) as key regulatory factors. As core regulatory hub genes, molecular docking simulation results demonstrated predicted potential binding interactions between AA and target proteins. Cytological validation experiments have demonstrated that AA exhibits concentration gradient-dependent inhibition of renal cell viability, while simultaneously downregulating the expression of SORD. Clinical findings reveal that compared with normal kidney tissues, SORD is highly expressed in the cytoplasm of renal cancer tissues. CONCLUSION: This is the first study to systematically establish a comprehensive link between AA exposure and RCC tumorigenesis using an ML-guided multi-omics framework. We identified SORD as a key mechanistic player and demonstrated its functional and clinical relevance, providing insights into the molecular pathogenesis of AA-induced RCC. Our integrative approach offers a powerful strategy for elucidating environmental carcinogen-related mechanisms and identifying novel therapeutic targets.