Corton Marta, Botella-Carretero Jose I, Benguria Alberto, Villuendas Gemma, Zaballos Angel, San Millan Jose L, Escobar-Morreale Hector F, Peral Belen

Instituto de Investigaciones Biomedicas, Consejo Superior de Investigaciones Cientificas and Universidad Autonoma de Madrid, E-28029 Madrid, Spain.

J Clin Endocrinol Metab. 2007 Jan;92(1):328-37. doi: 10.1210/jc.2006-1665. Epub

CONTEXT: The polycystic ovary syndrome (PCOS) is frequently associated with visceral obesity, suggesting that omental adipose tissue might play an important role in the pathogenesis of the syndrome.
OBJECTIVE: The objective was to study the expression profiles of omental fat biopsy samples obtained from morbidly obese women with or without PCOS at the time of bariatric surgery. DESIGN: This was a case-control study. SETTINGS: We conducted the study in an academic hospital. PATIENTS: Eight PCOS patients and seven nonhyperandrogenic women submitted to bariatric surgery because of morbid obesity. INTERVENTIONS: Biopsy samples of omental fat were obtained during bariatric surgery. MAIN OUTCOME MEASURE: The main outcome measure was high-density oligonucleotide arrays.
RESULTS: After statistical analysis, we identified changes in the expression patterns of 63 genes between PCOS and control samples. Gene classification was assessed through data mining of Gene Ontology annotations and cluster analysis of dysregulated genes between both groups. These methods highlighted abnormal expression of genes encoding certain components of several biological pathways related to insulin signaling and Wnt signaling, oxidative stress, inflammation, immune function, and lipid metabolism, as well as other genes previously related to PCOS or to the metabolic syndrome. CONCLUSION: The differences in the gene expression profiles in visceral adipose tissue of PCOS patients compared with nonhyperandrogenic women involve multiple genes related to several biological pathways, suggesting that the involvement of abdominal obesity in the pathogenesis of PCOS is more ample than previously thought and is not restricted to the induction of insulin resistance.

Mohamed-Hussein Zeti-Azura, Harun Sarahani

School of Biosciences and Biotechnology, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, 43600, UKM Bangi, Selangor, Malaysia. zeti@ukm.my

Theor Biol Med Model. 2009 Sep 1;6:18. doi: 10.1186/1742-4682-6-18.

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.