Identification of Hub Genes and CDK1-Targeting Therapeutics in Hepatocellular Carcinoma: Bioinformatics and Simulation Study

Background Hepatocellular carcinoma (HCC) remains a significant global health challenge, often associated with chronic hepatitis B (HBV) and hepatitis C (HCV) infections. Novel therapeutic approaches are urgently needed to improve patient outcomes. Objective To identify and validate potential therapeutic targets and novel drug candidates for HBV, HCV, and associated HCC using integrative bioinformatics and molecular dynamics simulations. Methods Gene expression datasets from HCC studies were analyzed to identify dysregulated genes. Protein-protein interaction networks were constructed to determine hub genes. Virtual screening of kinase compound libraries against CDK1 was performed, followed by molecular docking and dynamics simulations. Drug-likeness and metabolic properties of hit compounds were evaluated. Results Seven hub genes were identified, including five upregulated (CDK1, AURKB, CCNA2, BUB1B, RRM2) and two downregulated (AGXT2, ESR1) genes. Virtual screening yielded three promising hit compounds (G213-0272, E130-0174, Z666960592) targeting CDK1. E130-0174 demonstrated the most favorable profile, with stable CDK1 binding and no inhibition of major cytochrome P450 enzymes. Two repurposed drugs, alvocidib and riviciclib, also showed potential as CDK1 inhibitors. Conclusions This study identified novel CDK1 inhibitors with promising profiles for HCC treatment. The integrative approach combining bioinformatics and molecular dynamics simulations provides a robust framework for identifying potential therapeutic targets and drug candidates in HCC and related liver diseases. Further experimental validation is warranted to confirm these findings and advance the development of new HCC treatments.