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The incidence of hepatocellular carcinoma (HCC) has risen significantly in recent years, while current diagnostic and therapeutic approaches remain suboptimal. This study aimed to identify novel biomarkers and therapeutic targets to improve early detection and treatment outcomes. We conducted a comprehensive analysis of HCC-related gene expression datasets (GSE101685, GSE14520, and TCGA-LIHC). Differentially expressed genes (DEGs) were identified, followed by weighted gene co-expression network analysis (WGCNA) on the training cohort. A total of 313 shared genes were identified by intersecting 691 DEGs with 1653 genes from the "MEturquoise" module. Functional enrichment analyses, including gene ontology and Kyoto Encyclopedia of Genes and Genomes, were performed to explore the biological roles of these genes. Subsequently, 109 combinations of 12 machine learning algorithms were applied to identify HCC-specific feature genes. Gene set enrichment analysis and CIBERSORT were used to explore functional pathways and immune infiltration, respectively. Functional analyses revealed that the shared genes were primarily involved in cell cycle regulation and cell division. A total of 96 HCC feature genes were identified through 109 combinations of 12 machine learning algorithms. Among them, 5 novel genes (DNAJC12, KBTBD11, SEC24B, PLSCR4, SH3YL1) with no prior association with HCC were found to have significantly lower expression in tumor samples and were validated for their diagnostic value using receiver operating characteristic analysis. Gene set enrichment analysis further showed their association with immune responses, metabolic processes, and cell cycle regulation. Immune infiltration linked DNAJC12, KBTBD11, and SEC24B to the HCC immune microenvironment. Our study identified 5 previously unreported genes as potential diagnostic biomarkers and therapeutic targets for HCC. These findings provide a new perspective for the molecular characterization and clinical management of hepatocellular carcinoma.