Construction of a polycystic ovarian syndrome (PCOS) pathway based on the interactions of PCOS-related proteins retrieved from bibliomic data

Abstract

Polycystic ovary syndrome (PCOS) is a complex but frequently occurring endocrine abnormality. PCOS has become one of the leading causes of oligo-ovulatory infertility among premenopausal women. The definition of PCOS remains unclear because of the heterogeneity of this abnormality, but it is associated with insulin resistance, hyperandrogenism, obesity and dyslipidaemia. The main purpose of this study was to identify possible candidate genes involved in PCOS. Several genomic approaches, including linkage analysis and microarray analysis, have been used to look for candidate PCOS genes. To obtain a clearer view of the mechanism of PCOS, we have compiled data from microarray analyses. An extensive literature search identified seven published microarray analyses that utilized PCOS samples. These were published between the year of 2003 and 2007 and included analyses of ovary tissues as well as whole ovaries and theca cells. Although somewhat different methods were used, all the studies employed cDNA microarrays to compare the gene expression patterns of PCOS patients with those of healthy controls. These analyses identified more than a thousand genes whose expression was altered in PCOS patients. Most of the genes were found to be involved in gene and protein expression, cell signaling and metabolism. We have classified all of the 1081 identified genes as coding for either known or unknown proteins. Cytoscape 2.6.1 was used to build a network of protein and then to analyze it. This protein network consists of 504 protein nodes and 1408 interactions among those proteins. One hypothetical protein in the PCOS network was postulated to be involved in the cell cycle. BiNGO was used to identify the three main ontologies in the protein network: molecular functions, biological processes and cellular components. This gene ontology analysis identified a number of ontologies and genes likely to be involved in the complex mechanism of PCOS. These include the insulin receptor signaling pathway, steroid biosynthesis, and the regulation of gonadotropin secretion among others.

Systematic review: association of polycystic ovary syndrome with metabolic syndrome and non-alcoholic fatty liver disease

“…41 In addition, some other genes were found to be differentially expressed in relevant tissues of the PCOS patients, including ones encoding for insulin signalling components (IRS-1, pIRS-1Y612, GLUT4), sex hormone-binding globulin (SHBG), adiponectin 45 and inflammatory cytokines (IL-6, IL-18, TNFa). 42,46 Functions of PCOS related genes are summarised in Table 1.…”

“…44 As a large number of lypoxygenase-oxidised fatty acids become leukotrienes, which are natural chemical substances in the body that promote inflammatory response to damage, an impaired lipoxygenase function can theoretically contribute to the inflammatory condition seen in PCOS. 46 Incidentally, it was shown that the lipoxygenase encoded by ALOX15 augments the resistance to insulin, whereas its inhibition enhances the action of insulin in rat models of insulin resistance and type 2 diabetes. 77 12 ⁄ 15 lipoxigenase deficient mice are resistant to streptozotocin-induced diabetes.…”

“…The levels of SHBG have been shown to negatively correlate with insulin levels 46 and even were suggested as a surrogate marker of insulin resistance. In PCOS patients, due to their increased insulin levels, the SHBG levels are reduced significantly, leading to the release of more androgens, which causes the characteristic hyperandrogenemia of the disorder.…”

Aberrant expression of long noncoding RNAs in cumulus cells isolated from PCOS patients

“…These studies utilized cDNA microarrays in theca cells [9], whole ovaries [10,11], oocytes [12], and cumulus cells [13]. The data from these studies revealed a number of the genes associated with PCOS are primarily involved in the insulin receptor signaling pathway, steroid biosynthesis, and the regulation of gonadotropin secretion [14].…”

Construction of a Protein-Protein Interaction Network for Chronic Myelocytic Leukemia and Pathway Prediction of Molecular Complexes

“…There are three main categories to construct Biomolecular regulatory network : constructing gene regulatory networks through a mathematical model ; building a network through literature mining and integrating multiple data (Friedman et al, 2000;Hwang et al, 2005;Mohamed et al, 2009). Liu Y etc.…”