Network-based discovery of gene-miRNA interactions associated with hepatocellular carcinoma

Hepatocellular carcinomaAQ1 (HCC), the most prevalent form of liver cancer, poses a significant health burden due to its aggressive nature and poor prognosis. This study employed a comprehensive integrative bioinformatics approach to uncover putative regulatory axes involved in HCC pathogenesis. Transcriptomic data from twelve GEO microarray datasets were analyzed to identify differentially expressed genes(DEGs) and differentially expressed miRNAs (DEmİRs) between tumor and adjacent non-tumorous liver tissues. To interpret the determined biological information and determine the hub genes, a protein-protein interaction (PPI)network was created, and gene ontology and pathway enrichment analysis were performed. Furthermore, in silico analyses were performed to verify the gene and protein expression levels of the identified hub genes and to assess their prognostic impact on HCC. The findings identified three hub genes (AURKA, CCNB1, and KIF11) that were consistently shared across all datasets and were significantly associated with cancer progression and reduced overall survival. Pathway analysis indicated that the hub genes predominantly display protein kinase binding activity and participate in several pathways linked to cancer progression, including the p53 signaling pathway. Concurrently, three commonly downregulated miRNAs(hsa-miR-335-3p, hsa-miR-2355-5p, and hsa-miR-3163) were identified, all of which target the hub genes and contribute to their regulation. Collectively, these findings highlight bioinformatically prioritized gene–miRNA candidates associated with HCC and provide a rational basis for future experimental validation and functional studies.