ArticleTranslational cancer research2025
Identification of the key gene for hepatocellular carcinoma based on bioinformatics and machine learning and experimental verification.
Article in Translational cancer research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Hepatocellular carcinoma (HCC) is a severe hazard to human health and has a high fatality rate. While deregulated gene expression has been widely linked to hepatocarcinogenesis, many details of how these alterations drive tumor initiation and progression remain to be elucidated. We therefore combined bioinformatics and machine learning strategies to screen for and validate candidate driver genes in HCC. Methods: Three datasets (GSE78737, GSE98383, and GSE121248) were obtained from the Gene Expression Omnibus (GEO) database. GSE78737 and GSE98383 were combined to form the training set, while GSE121248 was used as the validation set. Initially, differentially expressed genes (DEGs) between HCC and non-HCC (nHCC) in the training set were identified. Enrichment analysis of these DEGs was performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). To identify diagnostic genes, machine learning algorithms including support vector machine-recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) were applied. The validation set was employed to confirm the DEGs. Furthermore, immune cell infiltration differences between nHCC and HCC were analyzed using CIBERSORT. GEPIA2.0 was subsequently used to analyze the prognostic significance of the diagnostic genes in HCC, identifying key genes. Finally, the key genes were validated using data from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC), as well as through immunohistochemistry (IHC) experiments, single-cell, and spatial transcriptomics analysis. Results: A total of 80 DEGs were identified, with 8 upregulated and 72 downregulated. The GO pathways associated with these DEGs were primarily related to responses to alcohol, humoral immune response, vacuolar lumen, chemokine activity, and mannose binding. KEGG pathway analysis revealed that the DEGs were primarily focused on viral protein interactions with cytokines and cytokine receptors. GSEA indicated that the most active processes in HCC included DNA replication, cell cycle, and mismatch repair. Immune cell analysis showed significant overexpression of naive B cells, CD8 Conclusions:
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