Background
Accumulating evidence suggested a strong association between polycystic ovary syndrome (PCOS) and ovarian cancer (OC), but the potential molecular mechanism is still unclear. In this study, we identify unrecognized but significant genes correlated to PCOS and OC via bioinformatics.
Materials and methods
Multiple bioinformatic analysis, such as Differential expression analysis, Univariate Cox analysis, functional and pathway enrichment analysis, protein–protein interaction (PPI) network construction, survival analysis, and Immune infiltration analysis were utilized. We further evaluated the effect of OGN on FSHR expression via immunofluorescence.
Results
The TCGA-OC dataset, GSE140082 (for OC) and GSE34526 (for PCOS) dataset were downloaded. 12 genes, RNF144B, LPAR3, CRISPLD2, JCHAIN, OR7E14P, IL27RA, PTPRD, STAT1, NR4A1, OGN, GALNT6 and CXCL11, were recognized as signature genes. Drug sensitive analysis was showed that OGN might be a hub gene in the progression of PCOS and OC. Experimental analysis found OGN could increase the FSHR expression, indicating OGN could regulate the hormonal response in PCOS and OC. Furthermore, correlation analysis indicated that the function of OGN might be closely related with m6A and ferroptosis.
Conclusions
Our study indicated 12 signatures that might involved in the prognosis significance of OC, and closely related the correlation between OC and PCOS. Furthermore, the hub gene, OGN, was a significant gene in the OC and PCOS progression via regulating the hormonal response.