Machine learning-based identification of core regulatory genes in hepatocellular carcinoma: insights from lactylation modification and liver regeneration-related genes - Yang, Yu; Hou, Yiwei; Yi, Li; Chen, Chongyuan; Li, Xiang; Wang, Yashan; Fu, Yunxi; Hu, Mingzheng; Xing, Rongchun (Frontiers in Oncology, 2025)
IntroductionHepatocellular 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.MethodsWe 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.ResultsMulti-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+ T cells and M1 macrophages. Prognostic validation in HCC cohorts revealed significant overexpression of these genes in tumours, with elevated Kif2c and Ccna2 predicting poor survival. Crucially, Csrp2 exhibited superior diagnostic efficacy (AUC > 0.8) compared to conventional biomarkers. Experimental validation via qPCR and Western blot confirmed marked upregulation of all six genes at both mRNA and protein levels in clinical HCC specimens (p < 0.0001).DiscussionThis 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.