Machine learning-based identification of core regulatory genes in hepatocellular carcinoma: insights from lactylation modification and liver regeneration-related genes.

Introduction: Hepatocellular carcinoma (HCC) progression shares metabolic-epigenetic features with physiological liver regeneration, yet the regulatory interplay remains poorly defined. We hypothesize that lactylation, a novel post-translational modification, serves as a key nexus linking these processes. Methods: We integrated lactylation modification profiles with transcriptomic data from three murine liver regeneration datasets (GSE20426, GSE70593, GSE4528). Machine learning algorithms, including LASSO regression and SVM-RFE, were employed to prioritize core regulatory genes. Functional characterization involved enrichment, immune infiltration, and correlation analyses. The prognostic and diagnostic value of the identified genes was validated in HCC cohorts, and their overexpression was confirmed in clinical HCC specimens using qPCR and Western blot. Results: Multi-omics analysis revealed 793 differentially expressed genes during liver regeneration, with 18 overlapping lactylation-related candidates. Machine learning prioritized six core genes (Ccna2, Csrp2, Ilf2, Kif2c, Racgap1, Vars) enriched in cell cycle regulation and DNA repair pathways. These genes demonstrated a strong correlation with immune microenvironment remodelling, particularly CD8 Discussion: This work uniquely establishes lactylation as a metabolic-epigenetic bridge linking physiological regenerative pathways to oncogenesis. By leveraging liver regeneration models and machine learning, we propose the identified gene panel as dual-purpose biomarkers for HCC diagnosis and therapeutic targeting, offering new insights into the metabolic-epigenetic regulation of HCC.