Abstract
Polycystic ovarian syndrome (PCOS) is an endocrine disorder that is characterized by hyperandrogenism. Therefore, information about androgen-induced molecular changes can be obtained using the tissues of patients with PCOS. We analyzed two microarray datasets of normal and PCOS muscle samples (GSE8157 and GSE6798) to identify androgen-responsive genes (ARGs). Differentially expressed genes were determined using the t-test and a meta-analysis of the datasets. The overlap between significant results of the meta-analysis and ARGs predicted from an external database was determined, and differential coexpression analysis was then applied between these genes and the other genes. We found 313 significant genes in the meta-analysis using the Benjamini−Hochberg multiple testing correction. Of these genes, 61 were in the list of predicted ARGs. When the differential coexpression between these 61 genes and 13,545 genes filtered by variance was analyzed, 540 significant gene pairs were obtained using the Benjamini−Hochberg correction. While no significant results were obtained regarding the functional enrichment of the differentially expressed genes, top-level gene ontology terms were significantly enriched in the list of differentially coexpressed genes, which indicates that a broad range of cellular processes is affected by androgen administration. Our findings provide valuable information for the identification of ARGs.