Exploring The Molecular Mechanism, Biomarkers And Prognostic Values Of Hcc Based On Gene Expression Microarray

Liver cancer is a common malignant neoplasm worldwide, causing high morbidity and mortality globally. The molecular mechanisms of hepatocarcinogenesis remains unclear. The goal of our study is to elucidate the mechanism which could improve the prognosis of liver cancer. GSE121248 was downloaded from the GEO database, which is a gene expression profile data including 70 tumor samples and 37 adjacent normal samples from hepatocellular carcinoma (HCC). The differentially expressed genes (DEGs) between cancer tissues and normal tissues were screened. Subsequently, the enriched GO terms, KEGG pathways and Database for Annotation, Visualization and Integrated Discovery (DAVID) were analyzed by on-line tools. Finally, STRING database and Cytoscape software were used to construct protein-protein interactions (PPI) network and genes with high degree. Kaplan-Meier plotter (KM plotter) was used to explore the predictive prognostic value of gene expression to overall survival (OS). The results showed that there were 202 DEGs between the case samples and control samples, including 59 up-regulated genes and 143 down-regulated genes in HCC tissue samples. The study obtained a total of 132 enriched GO terms and 16 KEGG pathways, such as extracellular region, organelle membrane, extracellular space, epoxygenase P450 pathway, retinol metabolism, metabolic pathways, caffeine metabolism, tryptophan metabolism and drug metabolism-cytochrome P450. PPI suggests that BUB1B, CCNB1, EZH2, NUSAP1 and CDC20 were the top 5 core genes. The patients with high expression of 5 core genes had poor OS according to online database. Using comprehensive bioinformatics analyses, our study attempts to identify DEGs and find potential biomarkers to predict the occurrence and development of HCC.