Abstract

In this study, we sought to identify the crucial genes and pathways involved in the dynamic development of hepatocellular carcinoma. In matched normal and hepatocellular carcinoma samples, differentially expressed genes were found using the R tool (DESeq). The R package "weighted gene co-expression network analysis" was then used to build a co-expression network for the obtained gene expression matrix. We used the "survival" and "survminer" R packages to compute the survival endpoints of overall survival and recurrence-free survival. Using the "clusterProfiler" R package, gene ontology, Kyoto encyclopedia of genes and genomes, and reactome enrichment studies of the co-expression modules were carried out. Cytoscape was used to gain a deeper understanding of the network. We discovered that 1237 genes were down-regulated in cancer tissues as compared to healthy controls, while 3443 genes were up-regulated (fold change >2 or fold change-2, padjust<0.05). Then, using gene set enrichment analysis to compare the tumor sample with healthy controls, we looked for pathway enrichments. The results revealed five significantly enriched pathways, including the cell cycle, oocyte meiosis, G2M checkpoint, mitotic spindle, and base excision repair, which is partially in line with differentially expressed genes-Kyoto encyclopedia of genes and genomes pathway enrichment findings. Nine genes were investigated, including RPL8, SLC27A5, KIF23, BUB1B, TRIM16, SCNM1, TKT, RFXANK, and GINS1. The gene expression omnibus dataset was also used to further validate the top five genes with the highest membership within each module. The gene expression omnibus dataset’s findings largely agreed with earlier research showing that these important genes were substantially correlated with tumor grade, tumor, node, metastasis/cancer of the liver Italian program/Barcelona clinic liver cancer stages, and overall survival/ relapse-free-survival. Finally, using quantitative real-time reverse transcription-polymerase chain reaction and immunohistochemistry, we identified the selected six genes (RPL8, SLC27A5, KIF23, BUB1B, TRIM16, and SCNM1) and discovered that they were compatible with earlier findings. These pathways and genes that bioinformatics and clinical samples found and validated may be involved in the course of hepatocellular carcinoma and may be used as possible biomarkers of hepatocellular carcinoma patients to aid in diagnosis and prognostication. It provides a new idea and direction for drug therapy of hepatocellular carcinoma.