Molecular basis of AR and STK11 genes associated pathogenesis via AMPK pathway and adipocytokine signalling pathway in the development of metabolic disorders in PCOS women

Pavan Gollapalli,N Suchetha Kumari,Praveenkumar Shetty,Tamizh Selvan Gnanasekaran

doi:10.1186/s43088-022-00200-8

Abstract

BackgroundOne of the most common hormonal disorders in women of reproductive age is polycystic ovary syndrome (PCOS). In recent years, it has been found that insulin resistance is a common metabolic abnormality in women with PCOS and leads to an elevated risk of type 2 diabetes mellitus. To explore the differentially expressed genes (DEGs) that regulate these kinds of metabolic risks in PCOS women, we chose the gene expression profile of GSE8157 from the gene expression omnibus (GEO) database.ResultsUsing the GEO2R tool, we identified a total of 339 DEGs between the case and the control sample groups. Gene ontology and Kyoto encyclopedia of gene and genome pathway enrichment analysis were subsequently conducted. High connectivity, betweenness centrality, bottleneck centrality, closeness centrality, and radiality measures were used to rank the ten hub genes. Furthermore, the overlap of these genes resulted in the development of two key genes, AR and STK11. The AMPK and adipocytokine signaling pathways are the two main pathways that these DEGs are involved.ConclusionsThe backbone genes, hub genes and pathways identified would assist us in further exploring the molecular basis of developing risk of type 2 diabetes mellitus in PCOS women and thus provide diagnostic or therapeutic clues.

Highlights

One of the most common hormonal disorders in women of reproductive age is polycystic ovary syndrome (PCOS)
A total of 339 differentially expressed genes (DEGs) were identified that constituted extended network and subsequent giant network extracted from extended network is composed of 318 nodes connected via 340 edges
Down-regulated DEGs (Fig. 3B) are related to up-regulated DEGs are primarily enriched in poly(A) RNA binding in molecular function (MF) analyses, which refers to binding to a sequence of adenylyl residues in an RNA molecule, such as the poly(A) tail, a sequence of adenylyl residues at the 3’ end of eukaryotic mRNA

Summary

Introduction

One of the most common hormonal disorders in women of reproductive age is polycystic ovary syndrome (PCOS). Polycystic ovary syndrome (PCOS) is a metabolic and reproductive disorder that affects between 4 and 18% of reproductive-age women [1] and in the general population, it is estimated around 21.27% [2]. It is known as hyper-androgenism, and it’s the second most common symptom of PCOS This condition affects anywhere from 17 to 83% of women [9]. A systems biology method, which combines experimental and computational biology to better understand complex biological systems, could investigate several interacting genes and their products that contribute to PCOS. In the case of PCOS, one of the first studies to use a computational approach was published in 2009, when researchers built a protein network from seven transcriptomics data to understand better the disease’s mechanism [14, 15]. Recent study findings have indicated that genes (APCO3, ADCY2, C3AR1, HRH2, GRIA1, MLNR and TAAR2) played a crucial role in the formation and progression of PCOS and that microarray data may be used to identify new biomarkers and therapeutic targets for PCOS [16]

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