An exploration of the correlation between ovarian cancer and miRNA based on bioinformatics

Objective: The study aims to explore the correlation between the changes in miRNA in the occurrence and development of ovarian cancer with ovarian cancer using bioinformatics technology. Methods: Based on Platform GPL570 of the GEO database, the gene chip associated with ovarian cancer was searched through the comparison between normal women and cancerous women. The total genes were screened according to the set conditions (P<0.05, vertical bar Log FC vertical bar>2), and the disease genes were compared with the data in the GeneCards database. A Venn diagram was plotted. A PPI network construction and a DAVID enrichment analysis were performed with the screened core genes. A gene-miRNA correlation analysis was performed with the core genes using WebGestalt, and the resulting miRNA was imported into a Kaplan Meier-Plotter for a survival analysis. Results: The data from the GSE23391, GSE29450, and GSE38666 gene chips were screened according to the set conditions (P<0.05, vertical bar Log FC vertical bar>2). The obtained 803 significant genes were compared with the 6,657 pathogenic genes in the GeneCards database, and a total of 346 core genes were obtained. The PPI network map and DAVID enrichment analysis showed that the core genes were closely related to each other and mostly related to cancer pathways. In addition, the core genes were imported into WebGestalt for the gene-miRNA correlation analysis, and the miRNAs of a total of 19 core genes were selected for up-regulation and down-regulation analyses. Then the obtained miRNAs were imported into the Kaplan Meier-plotter for the online survival analysis, and it was found that almost all the obtained core miRNA genes were closely related to the occurrence and development of ovarian cancer. Conclusion: The obtained miRNAs of the core genes were closely related to ovarian cancer and other cancers. MiR-138, miR-98, miR-124, miR-9, miR-367, and miR-429 can be used as prediction genes for ovarian cancer.