Identification and Validation of the Most Prevalent Biomarkers in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) poses a significant challenge to global health due to the limited options for early prognosis, diagnosis, and treatment. To comprehend the intricate biological processes involved in early prognosis and diagnosis, biomarkers can be highly significant. In this study, advanced bioinformatics and machine learning techniques were used for the identification and validation of the key biomarkers associated with HCC. Analysis of two microarray datasets identified 357 upregulated and 283 downregulated differentially expressed genes (DEGs). Then, a protein-protein interaction (PPI) network was constructed using the STRING database. Subsequently, the PPI network was analyzed in Cytoscape and found 10 core hub genes (TOP2A, CDK1, KIF11, CDC20, BUB1, NUF2, MAD2L1, CCNA2, CCNB1, and BUB1B). To validate the relevance of these earmarked hub genes, their prognostic efficacy was assessed using two autonomous test datasets, achieving promising outcomes quantified by the area under the curve (AUC). Moreover, the Kaplan-Meier survival analysis demonstrated the prognostic relevance of these hub genes, highlighting a robust correlation with patient outcomes. These hub genes could serve as potent biomarkers for refined prognosis, precise diagnosis, and potential therapeutic targets in the domain of HCC.