Comprehensive identification and validation of hub genes targeted by quercetin in hepatocellular carcinoma via integrated bioinformatics and experimental approaches
Abstract
Hepatocellular carcinoma (HCC) is a common and highly lethal malignancy worldwide, characterized by complex, multi-step molecular mechanisms. Identifying novel pathogenic genes and potential natural compounds holds great promise for advancing HCC treatment. In this study, we analyzed the GSE65372 dataset from the GEO database using the limma method to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis (WGCNA) was applied to identify HCC-associated gene modules. The intersection of DEGs, WGCNA-derived genes, and HCC-related genes from the GeneCards database yielded a set of candidate genes. A protein-protein interaction (PPI) network was constructed using the STRING database, and four cytoHubba algorithms (MCC, MNC, degree, EPC) were used to identify hub genes. Functional enrichment analysis (GO and KEGG) was performed, and immune infiltration characteristics were evaluated using the CIBERSORT algorithm. In addition, single-gene GSEA was carried out for hub genes. Potential therapeutic compounds were predicted using the CMap platform and validated by molecular docking using AutoDock Vina. Finally, a quercetin treatment model was established, and CCK-8 assays were used to assess cell viability in LO2 (normal hepatocytes) and HepG2 (HCC cells). The expression of hub genes was further evaluated by RT-qPCR. A total of 1326 DEGs were identified, and 1003 key module genes were selected via WGCNA. Intersection with GeneCards yielded 95…
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