Identification of key genes associated with polycystic ovary syndrome (PCOS) and ovarian cancer using an integrated bioinformatics analysis.

ABSTRACT:

Background

Accumulating evidence suggests a strong association between polycystic ovary syndrome (PCOS) and ovarian cancer (OC), but the potential molecular mechanism remains unclear. In this study, we identified previously unrecognized genes that are significantly correlated with PCOS and OC via bioinformatics.

Materials and methods

Multiple bioinformatic analyses, 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

TCGA-OC, GSE140082 (for OC) and GSE34526 (for PCOS) datasets were downloaded. Twelve

SUBMITTER: Zou J 

PROVIDER: S-EPMC8886837 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature