Introduction: One of the serious cancers on a global scale is Hepatocellular Carcinoma (HCC). HCC is the most prevalent form of primary liver cancer and one of the leading causes of cancer-related death globally. Therefore, early recognition of this issue is essential. This study aimed to identify promising biomarkers for early detection and timely treatment and finding potential approaches to save more patients struggling with this morbidity.
Methods: First, the TCGA dataset named LIHC was acquired from TCGA database and expression matrix was generated using a function called assay in R programming language. Expression matrix was normalized with TMM method of edgeR and limma packages and afterwards, criteria of |log2FC|>2 and P.value< 0.05 were set to select target genes. Differentially Expressed Genes (DEGs) were used to perform Protein-Protein Interaction (PPI) network and its outcome was utilized to visualize and for further analysis with two plug-ins of Cytoscape software, CytoHubba and MCODE. Furthermore, Cytoscape analysis revealed the highly ranked genes which were calculated by several algorithms of CytoHubba plug-in, as our hub genes. Besides, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis conducted using Enrichr database and finally, the value of hub genes were evaluated through the Gene Expression Profiling Interactive Analysis (GEPIA) web-based survival analysis tool.
Results: Overall, 434 genes comprising 67 up-regulated and 367 down-regulated genes were detected. According to the results of PPI network analysis utilizing Cytoscape, the first and top-ranked cluster (35,833) was selected due to the MCODE plug-in and Top 5 genes ranked by MCC, Degree, MNC, EcCetricity and Closeness methods of CytoHubba plug-in were distinguished. In the next step, Survival analysis ascertained that up-regulation of KIFC1, CDK1, CCNB1, KIF2C and AURKA were strongly involved in hepatocellular carcinoma progression. Further analysis revealed an outstanding enrichment of target genes in Retinol metabolism in KEGG pathway. In addition, DEGs were significantly engaged in Arachidonic Acid Monooxygenase Activity (GO:0008391), Collagen-Containing Extracellular Matrix (GO:0062023) and Epoxygenase P450 Pathway (GO:0019373) in molecular function, cellular component and biological process, respectively.
Conclusion: In conclusion, the results of our research and survival analysis demonstrated a remarkable association between the expression of CCNB1, KIF2C, CDK1, AURKA and KIFC1 and the mortality in hepatocellular carcinoma cases.