CDK members and ribosomal proteins, a crucial role players in serous capillary ovarian cancer: Insights from gene expression and protein interaction networks

Abstract

Ovarian cancer (OC) is an appalling disease of the female reproductive tract and among the most challenging gynecological malignancies. It is often referred to as the “silent killer” because its signs and symptoms are absent, in most cases, until it reaches advanced stages, wherein there is a low probability to cure it. The number of disease markers that have been detected via genome wide expression profiles has increased vastly. One such impressive and encouraging tool to discover therapeutic targets in this kind of complex disease conditions is microarray gene expression profiling. The study aims to perform a comprehensive analysis of differentially expressed genes (DEGs) and associated biological networks in ovarian cancer using bioinformatics tools. This research aims to examine differentially expressed genes (DEGs) in ovarian cancer using the GSE14407 dataset from the GEO database. It will investigate the biological functions and pathways of these DEGs through GO and KEGG enrichment analyses and build a protein-protein interaction (PPI) network to identify key hub genes involved in ovarian cancer development. Genes expressed differentially in ovarian cancer dataset (GSE14407) retrieved from a GEO database were evaluated by the limma package (R language) and via cluster analysis. The function of the DEGs and their pathway analysis was augmented via gene ontology (GO) and Kyoto Encyclopedia Genes and Genomes (KEGG) database, respectively. Additionally, the protein-protein interaction network of the DEGs was built and the topological metrics of the central network generating clusters of highly correlated genes and the hub genes in the network were evaluated. The total of 2225 Ovarian cancer probes were found in GEO dataset. Gene and pathway enrichment analysis shows that up-regulated DEGs were found to be, cell cycle (15 genes), pathways in cancer (14 genes) and p53 signaling pathway (8 genes), meanwhile the down- regulated DEGs were also enriched in pathways in cancer (55 genes), PI3K-Akt signaling pathway(48genes), proteoglycans in cancer (41 genes) and the hub proteins (12 genes). Our findings showed that AURKA, CCNB1, CDK1, CHEK1, CCNA2, CCNB2 were involved in the cell cycle and p53 signaling pathway and their critical role in ovarian cancer. The findings of the present study provide a comprehensive bioinformatics analysis of DEGs that might be involved in pathogenesis of ovarian cancer. The functional network-based analysis that incorporates gene ontology and pathway based information revealed protein kinase as the key player in establishing the ovarian cancer.