Comparative transcriptomics and computational drug discovery identify ASPM as a key oncogenic driver and therapeutic...

Hepatocellular carcinoma (HCC) is a highly heterogeneous malignancy that necessitates the identification of robust biomarkers across diverse populations to enhance diagnostic and prognostic precision. This study aimed to identify clinically relevant biomarkers and potential therapeutic targets through integrative transcriptomic and computational analyses.

A comparative transcriptomic analysis was performed on 724 HCC samples obtained from four independent cohorts (United States, South Korea, France, and Taiwan). Differential expression and survival analyses, including Kaplan-Meier estimation, were conducted to evaluate clinical significance. Functional enrichment analysis was used to explore biological roles. Structural modeling, molecular docking, 100-ns molecular dynamics (MD) simulations, MM-GBSA binding energy calculations, and in silico ADMET profiling were employed to assess ligand-target interactions.

Abnormal Spindle Microtubule Assembly (ASPM) was consistently overexpressed across all cohorts and significantly associated with poor overall survival. Functional analyses indicated its involvement in mitotic spindle organization and homologous recombination-mediated DNA repair. Among screened compounds, Mol-7424 exhibited stable binding within the ASPM calponin domain, favorable binding free energy, and promising pharmacokinetic properties. Lipid bilayer simulations further supported its membrane permeability and potential cellular uptake.

These findings highlight ASPM as a prognostic biomarker and potential therapeutic target in HCC. Mol-7424 emerges as a promising lead compound; however, its efficacy requires validation through in vitro and in vivo studies. Overall, this study underscores the utility of multi-population transcriptomics integrated with computational approaches for advancing precision oncology in HCC.