Unveiling Berberine's Therapeutic Mechanisms Against Hepatocellular Carcinoma via Integrated Computational Biology and Machine Learning Approaches: AURKA and CDK1 as Principal Targets.

Hepatocellular carcinoma continues to be a predominant contributor to oncological fatalities, characterized by restricted treatment alternatives. Although berberine exhibits anti-neoplastic capabilities, the underlying molecular pathways in hepatic malignancy require clarification. A comprehensive computational framework was established, incorporating transcriptomic data analysis, multiple machine learning methodologies, weighted gene co-expression network analysis (WGCNA), and molecular simulation techniques to elucidate berberine's therapeutic pathways. Transcriptomic datasets from the Cancer Genome Atlas (TCGA) underwent examination to detect differentially expressed genes (DEGs). Ten machine learning methodologies screened critical targets, subsequently validated through molecular docking and 100 ns molecular dynamics simulations. Transcriptomic examination revealed 531 DEGs (341 exhibiting upregulation, 190 demonstrating downregulation) alongside 173 putative berberine interaction targets, yielding 17 intersecting candidates. Machine learning approaches consistently recognized AURKA and CDK1 as principal targets, subsequently confirmed by WGCNA as central genes. Elevated expression of both targets demonstrated correlation with unfavorable survival outcomes (