Identification of microRNA and protein interaction networks in human ovarian cancer

Abstract

Ovarian cancer is one of the deadliest tumors in women, with a high mortality rate brought on by the lack of early detection. In this work, our main aim is to find promising biomarkers and pertinent mechanisms. GSE36668 was chosen from the Gene Expression Omnibus (GEO) to identify the differentially expressed genes (DEGs) using the GEO2R tool. To forecast gene ontology (GO) and pathway enrichment, online tools from ToppGene, FunRich and DAVID were employed. The protein-protein interaction (PPI) network is built via STRING v.11.5 and Cytoscape v.3.9.1. Following the detection of the hub genes, a Kaplan-Meire plotter was used to conduct additional validation survival analyses. A total of 1556 DEGs were identified using GEO2R, out of which 697 were upregulated and 859 were downregulated. According to GO analysis, DEGs were much more common in the online tools DAVID and ToppGene for cell adhesion, axoneme assembly and cilium assembly in the biological processs whereas cell surface is an essential component of the plasma membrane and extracellular matrix in the cellular component. In contrast, the plasma membranes are present in DAVID and FunRich. The DEGs are mostly linked to the MAPK, PI3K-Akt and RAP1 signaling pathways in KEGG and in the Reactome pathway, they are involved in cell-cell communication, cell and cell-cell junction organization The PPI network construct was used to find the gene clusters and to identify the hub genes MAPK1, CDH1, CBL and CCND1 by Cytoscape. The survival analysis of this hub gene CBL showed high expression in ovarian cancer which led to fewer survival chances. According to this study, ovarian cancer biomarkers are crucial to understand the molecular causes of the disease.