Explore the potential molecular mechanism of polycystic ovarian syndrome by protein-protein interaction network analysis - Qingfen Chen; Beihong Zheng; Shengrong Du; Yunhong Lin (Elsevier, 2021)
Polycystic ovary syndrome (PCOS) is one of the most common endocrine disorders prevailing in reproductive age women, present in 3-15% population of women worldwide. Although there are many studies on PCOS, its underlying mechanism remains to be determined. The present study was to construct protein-protein interaction networks based on the potential disease-causing genes for PCOS and characterize the underlying molecular mechanisms of PCOS using the networks. PCOS-associated genes were extracted from DisGeNet and the protein-protein interaction networks (PPIN) of PCOS were constructed using the String Database. Then we utilized MCODE algorithm to analyse the hub-gene modules from the PPIN. Finally, the major biological functions and signaling pathways involved in the hub modules were explored by functional enrichment analysis. A total of 522 candidate genes associated to PCOS were extracted from DisGeNET database. The PPIN constructed using the genes we have collected above included 488 genes and 2767 interaction relationships. Moreover, seven major gene modules were obtained after analyzing the PPIN with the use of MCODE plug-in. The major modules generated were enriched in certain biological functions such as cancer and cell proliferation and apoptosis, regulation of lipid and glucose metabolism, cell cycle and so on. The integrated analysis performed in the current study revealed that these hub modules and their related genes are closely associated to the pathogenesis of PCOS, which may probably provide novel insights for the treatment of PCOS and the study of its latent pathogenic mechanism. The relationship between several of the key genes including ALB, TOP2A, PTGER3, NPB and BRD2 in the modules and PCOS has not been investigated previously and it remains to be verified by further research of large sample, multi-center and multi-ethnic.