Comprehensive analysis of tripterine anti-ovarian cancer effects using weighted gene co-expression network analysis and molecular docking

Abstract

Background: Ovarian cancer has the highest mortality of gynecological cancers worldwide. The aim of this study was to identify the role of tripterine against ovarian cancer. Material/Methods: GSE18520 and GSE12470 data sets were downloaded from the GEO database. WGCNA was used to analyze gene modules and hub genes related to ovarian cancer. These hub genes were intersected with tripterine targets, and GO and KEGG enrichment analyses were performed. HPA and GEPIA determined the expression of tripterine anti-ovarian hub genes in tumor tissues. Kaplan-Meier plotter was used to explore the role of hub genes in ovarian cancer prognosis. AutoDock was used to conduct molecular docking of tripterine and hub genes to observe whether the combination was stable. Results: By differential analysis of gene expression and the construction of WGCNA co-expression network, 5 hub genes, ARHGAP11A, MUC1, HBB, RUNX1T1, and FUT8, were screened by module gene screening. Seven biological processes and 20 KEGG-related pathways were obtained by gene enrichment. The expression of tripterine anti-ovarian hub genes ARHGAP11A, MUC1, and FUT8 were obtained by HPA and GEPIA. Using Kaplan-Meier plotter, the survival of ovarian cancer was negatively correlated with ARHGAP11A, MUC1, and FUT8. Molecular docking showed the combination of tripterine and FUT8 was most stable, having the greatest potential role. Conclusions: Tripterine may be involved in megakaryocyte development and platelet production through potential genes ARHGAP11A, MUC1, HBB, RUNX1T1, and FUT8 and may have an anti-ovarian cancer effect in immune factors signaling, transporting and exchanging oxygen pathways, and autophagy pathways, through these 5 key genes.