Identification of prevention marker associated with DNA replication in ovarian cancer: Expression of MCM2 protein and bioinformatics analysis

Abstract

Ovarian cancer is one of the types of gynecological cancers that is considered to be particularly dangerous. Ovarian cancer treatment has come a long way in recent years, but the disease is still quite likely to spread to other parts of the body. In this line of research, our goal is to pinpoint the shifts in gene expression profiles that are responsible for the avoidance of ovarian cancer. The dataset GSE54388 which was deposited in the Gene Expression Omnibus (GEO) database was processed in order to find differentially expressed genes (DEGs) that were present between human ovarian surface epithelium samples and tumor epithelial component samples. The weighted gene correlation network analysis, also known as WGCNA, was performed on the modules that were associated with the ovarian cancer group. The Gene Ontology (GO), the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and the Gene Set Enrichment Analysis (GSEA) were used to compile a summary of the DEGs that were found in the Venn analysis of the Royalbule module. This analysis found 186 genes that overlapped in the royal blue module. Using the cytohubba plug-in that is included in the Cytoscape software, the Protein-protein Interaction (PPI) network was created and then searched to identify hub genes. Based on these findings, it seems that 10 genes have a role as hub genes in the prevention of ovarian cancer.