Protein–Protein Interaction Network Construction and Differential miRNA Target Gene Prediction in Ovarian Cancer by Bioinformatics Analysis

Abstract

Our research focused on investigating genetic changes in ovarian cancer (OV) by constructing a protein–protein interaction network. In addition, we utilized data mining techniques that were specifically tailored for OV. To gather differentially expressed miRNAs, we accessed the GEO database. The differential expression was administrated using R language. We used three different bioinformatics algorithms to identify the candidate genes of the altered microRNAs. Using Cytoscape, we created a vision constructure between these miRNAs and the corresponding goals. This allowed us to identify specific hub genes. To validate our findings, we confirmed the presence of essential genes and autophagy-related genes in both the GEPIA and TCGA databases. Through this process, we were able to pinpoint the connection between them. In total, we identified nine miRNAs that showed differential expression. Together, these miRNAs predicted the presence of 488 objective gene. Among them, the FOS demonstrated statistical significance when evaluated in both the GEPIA and TCGA. Importantly, it should be highlighted that FOS has been linked to ovarian cancer prognosis.