Evaluation of miRNA-derived Extracellular Vesicles as Biomarkers in Ovarian Cancer Stem Cells

Background and objectives: Ovarian cancer (OvCa) is the most deadly gynecological cancer. This study aimed to determine which genes and miRNAs play an important role in OvCa. Methods: We accessed the Gene Expression Omnibus database and downloaded the mRNA microarray dataset. Through the use of GEO2R, we were able to collect data on differentially expressed genes (DEGs) and microRNAs (DEMs). By querying the Enrichr database, we were able to conduct functional and pathway enrichment analysis on DEGs. STRING was used to create a network of protein–protein interactions, and Cytoscape was used to display the networks. Gene Expression Profiling Interactive Analysis and The Cancer Genome Atlas were then used to conduct overall survival and clinical data analyses of hub genes. DEM target predictions were also made using miRnet. For extracellular vesicles confirmation, Exocarta and Vesiclepedia were used. Results: There were a total of 1,778 DEGs found, and most of them were enriched for terms associated with the cell cycle, mitosis, and the ovulation cycle. There were 141 nodes used in the creation of the protein–protein interaction network. There were 10 genes with a lot of connections between them. Patients with OvCa had a shorter overall survival if they had high expression of four of the 10 genes tested: ATF3 , ZEB1 , CSF1R , and HSPA8 . We found out that the protein–protein interaction network has a significant module. The cell cycle, extracellular matrix receptor, and cell invasion were among the enriched functions and pathways. In addition, we found a total of 20 DEMs. The hsa-let-7 family (hsa-let-15a-3p, hsa-let-18a-5p, and hsa-let-615-5p) may target ZEB1 because its expression is inversely correlated with that of ZEB1