Aristolochic acid (AA) is a naturally occurring toxin widely present in traditional herbal medicines and is well known for its nephrotoxic and carcinogenic effects. Its association with clear cell renal cell carcinoma (ccRCC) has attracted increasing attention, yet the key molecular targets and underlying mechanisms of AA-induced carcinogenesis remain poorly understood. In this study, 62 intersection genes related to both AA exposure and ccRCC were identified by integrating toxicogenomic databases with ccRCC-associated gene profiles. Transcriptomic analysis and weighted gene co-expression network analysis further narrowed this list to six critical candidates. Diagnostic models built using 13 machine learning algorithms demonstrated robust and consistent performance across multiple datasets. Immune infiltration and functional enrichment analyses suggested that several of these genes may contribute to immune remodeling and metabolic dysregulation. Among them, JAK3 stood out due to its significant upregulation, negative correlation with immunosuppressive cell subsets, and strong association with poor prognosis. Although JAK3 exhibited strong binding affinity to AA in docking analysis, molecular dynamics simulations revealed reduced conformational stability and increased flexibility in its kinase domain, suggesting ligand-induced structural perturbation and potential toxic interference. Collectively, these findings identify JAK3 as a critical toxicological target of AA in ccRCC and demonstrate the power of toxicogenomic and multi-omics integration in uncovering environment-related carcinogenic mechanisms.
Keywords: Aristolochic acid; Clear cell renal cell carcinoma; Immune microenvironment; Machine learning; Molecular docking; Network toxicology; WGCNA analysis.
© 2025. The Author(s), under exclusive licence to Springer Nature Switzerland AG.