Identification of Driver Genes and miRNAs in Ovarian Cancer through an Integrated In-Silico Approach

SIMPLE SUMMARY: Ovarian cancer ranks among the most frequent causes of death in women since the prognosis is greatly influenced by the disease’s stage. Since ovarian cancer is largely asymptomatic in its early stages, it is frequently detected in its late stages. One of the hallmarks of ovarian cancer is genomic instability. Although ovarian cancer is divided into several clinical subtypes, each subtype exhibits significant genetic and progressive diversity. In this paper, we concentrate on epithelial ovarian cancer, which is typically discovered when it is already advanced because there is no reliable screening method. Although numerous biomarkers have been researched and used to monitor its status and progression, we still lack drug therapy effectiveness in this subtype. Network biology has recently offered unprecedented opportunities for understanding disease mechanisms from integrative angles. The dysfunctions caused by diseased genes are carried out by a complex network of physical and metabolic interactions. The topological characteristics of these disease genes in the interactome are of particular importance to the systematic comprehension of their activity. We present a systems biology approach to finding miRNAs and complicated disease genes in an integrated biomolecular network in this paper. ABSTRACT: Ovarian cancer is the eighth-most common cancer in women and has the highest rate of death among all gynecological malignancies in the Western world. Increasing evidence shows that miRNAs are connected to the progression of ovarian cancer. In the current study, we focus on the identification of miRNA and its associated genes that are responsible for the early prognosis of patients with ovarian cancer. The microarray dataset GSE119055 used in this study was retrieved via the publicly available GEO database by NCBI for the analysis of DEGs. The miRNA GSE119055 dataset includes six ovarian carcinoma samples along with three healthy/primary samples. In our study, DEM analysis of ovarian carcinoma and healthy subjects was performed using R Software to transform and normalize all transcriptomic data along with packages from Bioconductor. Results: We identified miRNA and its associated hub genes from the samples of ovarian cancer. We discovered the top five upregulated miRNAs (hsa-miR-130b-3p, hsa-miR-18a-5p, hsa-miR-182-5p, hsa-miR-187-3p, and hsa-miR-378a-3p) and the top five downregulated miRNAs (hsa-miR-501-3p, hsa-miR-4324, hsa-miR-500a-3p, hsa-miR-1271-5p, and hsa-miR-660-5p) from the network and their associated genes, which include seven common genes (SCN2A, BCL2, MAF, ZNF532, CADM1, ELAVL2, and ESRRG) that were considered hub genes for the downregulated network. Similarly, for upregulated miRNAs we found two hub genes (PRKACB and TAOK1).