Identification of Potential Biomarkers of Polycystic Ovary Syndrome via Integrated Bioinformatics Analysis

2020 ◽  
Author(s):  
Dongyong Yang ◽  
Na Li ◽  
Aiping Ma ◽  
Fangfang Dai ◽  
Yajing Zheng ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Bioinformatics Analysis ◽  
Polycystic Ovary ◽  
Ovary Syndrome ◽  
Potential Biomarkers

scholarly journals Bioinformatics analysis of the molecular mechanism of obesity in polycystic ovary syndrome

Aging ◽  
2021 ◽  
Author(s):  
Jiaojiao Zhou ◽  
Xiaolin Huang ◽  
Bingshuang Xue ◽  
Yuhe Wei ◽  
Fei Hua
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Molecular Mechanism ◽  
Bioinformatics Analysis ◽  
Polycystic Ovary ◽  
Ovary Syndrome

scholarly journals Metabolomic Insight into Polycystic Ovary Syndrome—An Overview

2020 ◽  
Vol 21 (14) ◽  
pp. 4853 ◽  
Author(s):  
Anna Rajska ◽  
Magdalena Buszewska-Forajta ◽  
Dominik Rachoń ◽  
Michał Jan Markuszewski
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Metabolic Pathways ◽  
Polycystic Ovary ◽  
Review Article ◽  
Endocrine Disorders ◽  
Crucial Aspect ◽  
Ovary Syndrome ◽  
Metabolic Markers ◽  
Insight Into ◽  
Potential Biomarkers

Searching for the mechanisms of the polycystic ovary syndrome (PCOS) pathophysiology has become a crucial aspect of research performed in the last decades. However, the pathogenesis of this complex and heterogeneous endocrinopathy remains unknown. Thus, there is a need to investigate the metabolic pathways, which could be involved in the pathophysiology of PCOS and to find the metabolic markers of this disorder. The application of metabolomics gives a promising insight into the research on PCOS. It is a valuable and rapidly expanding tool, enabling the discovery of novel metabolites, which may be the potential biomarkers of several metabolic and endocrine disorders. The utilization of this approach could also improve the process of diagnosis and therefore, make treatment more effective. This review article aims to summarize actual and meaningful metabolomic studies in PCOS and point to the potential biomarkers detected in serum, urine, and follicular fluid of the affected women.

scholarly journals Potential biomarkers for predicting preterm birth in women with polycystic ovary syndrome†

2013 ◽  
Vol 19 (6) ◽  
pp. 603-603 ◽  
Author(s):  
Nicolas Galazis ◽  
Nikolina Docheva ◽  
Kypros H. Nicolaides ◽  
William Atiomo
Keyword(s):  
Preterm Birth ◽  
Polycystic Ovary Syndrome ◽  
Polycystic Ovary ◽  
Ovary Syndrome ◽  
Potential Biomarkers

scholarly journals Identification of the core ontologies and signature genes of polycystic ovary syndrome (PCOS): A bioinformatics analysis

2020 ◽  
Vol 18 ◽  
pp. 100304 ◽  
Author(s):  
Md Rakibul Islam ◽  
Md Liton Ahmed ◽  
Bikash Kumar Paul ◽  
Touhid Bhuiyan ◽  
Kawsar Ahmed ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Bioinformatics Analysis ◽  
Polycystic Ovary ◽  
Ovary Syndrome ◽  
The Core ◽  
Signature Genes ◽  
Core Ontologies

scholarly journals Identification of key pathways and genes in polycystic ovary syndrome via integrated bioinformatics analysis and prediction of small therapeutic molecules

2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Praveenkumar Devarbhavi ◽  
Lata Telang ◽  
Basavaraj Vastrad ◽  
Anandkumar Tengli ◽  
Chanabasayya Vastrad ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Gene Network ◽  
Mirna Target ◽  
Target Gene ◽  
Bioinformatics Analysis ◽  
Polycystic Ovary ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Ovary Syndrome

AbstractTo enhance understanding of polycystic ovary syndrome (PCOS) at the molecular level; this investigation intends to examine the genes and pathways associated with PCOS by using an integrated bioinformatics analysis. Based on the expression profiling by high throughput sequencing data GSE84958 derived from the Gene Expression Omnibus (GEO) database, the differentially expressed genes (DEGs) between PCOS samples and normal controls were identified. We performed a functional enrichment analysis. A protein-protein interaction (PPI) network, miRNA- target genes and TF - target gene networks, were constructed and visualized, with which the hub gene nodes were identified. Validation of hub genes was performed by using receiver operating characteristic (ROC) and RT-PCR. Small drug molecules were predicted by using molecular docking. A total of 739 DEGs were identified, of which 360 genes were up regulated and 379 genes were down regulated. GO enrichment analysis revealed that up regulated genes were mainly involved in peptide metabolic process, organelle envelope and RNA binding and the down regulated genes were significantly enriched in plasma membrane bounded cell projection organization, neuron projection and DNA-binding transcription factor activity, RNA polymerase II-specific. REACTOME pathway enrichment analysis revealed that the up regulated genes were mainly enriched in translation and respiratory electron transport and the down regulated genes were mainly enriched in generic transcription pathway and transmembrane transport of small molecules. The top 10 hub genes (SAA1, ADCY6, POLR2K, RPS15, RPS15A, CTNND1, ESR1, NEDD4L, KNTC1 and NGFR) were identified from PPI network, miRNA - target gene network and TF - target gene network. The modules analysis showed that genes in modules were mainly associated with the transport of respiratory electrons and signaling NGF, respectively. We find a series of crucial genes along with the pathways that were most closely related with PCOS initiation and advancement. Our investigations provide a more detailed molecular mechanism for the progression of PCOS, detail information on the potential biomarkers and therapeutic targets.

scholarly journals Serum metabolomic profiling reveals potential biomarkers in assessing the management of women with polycystic ovary syndrome

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Xue-Song Ding ◽  
Yan Deng ◽  
Yan-Fang Wang ◽  
Wei Xue ◽  
Shi-Yang Zhu ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Polycystic Ovary ◽  
Ovary Syndrome ◽  
Metabolomic Profiling ◽  
Potential Biomarkers

scholarly journals Identification of Three Potential circRNA Biomarkers of Polycystic Ovary Syndrome by Bioinformatics Analysis and Validation

2021 ◽  
Vol Volume 14 ◽  
pp. 5959-5968
Author(s):  
Pengyu Huang ◽  
Shengrong Du ◽  
Yunhong Lin ◽  
Zhiqing Huang ◽  
Haiyan Li ◽  
...  
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Bioinformatics Analysis ◽  
Polycystic Ovary ◽  
Ovary Syndrome

scholarly journals Serum Metabolomic Profiling Reveals Potential Biomarkers in Assessing The Management of Women with Polycystic Ovary Syndrome: A Randomized Controlled Trial

2020 ◽  
Author(s):  
Xuesong Ding ◽  
Yan Deng ◽  
Yan-fang Wang ◽  
Wei Xue ◽  
Shi-yang Zhu ◽  
...  
Keyword(s):  
Randomized Controlled Trial ◽  
Polycystic Ovary Syndrome ◽  
Polycystic Ovary ◽  
Controlled Trial ◽  
Medical Intervention ◽  
Childbearing Age ◽  
Ovary Syndrome ◽  
Diagnosis And Management ◽  
Randomized Controlled ◽  
Potential Biomarkers

Abstract Background: As one of the most common endocrinal disorder for the women at childbearing age, the diagnostic criteria of polycystic ovary syndrome (PCOS) have been defined distinctly among different international health organizations. The diverse manifestations and heterogenic etiology of PCOS also bring about difficulties for its diagnosis and management assessment. Though the elevated androgen level is acknowledged to be the most obvious biomarker for PCOS diagnosis and management evaluation, it is not always present in patients and the mechanism of hyperandrogenism has not been fully elucidated. Therefore, more efficient biomarkers representing progression of PCOS are expected to be integrated into monitoring the management process using metabolomic approach. Methods: In this prospective randomized controlled trial, classical diagnostic parameters, blood glucose, and metabolome were measured in 123 PCOS patients simultaneously before and at 2 and 3 months of different medical interventions. The metabolite fingerprints were detected filtered out by comparing data at baseline and 3 months based on multivariate statistical analysis, followed by validation with receiver operating characteristic (ROC) curve on data collected at 2 months. Results: A set of metabolites including glutamic acid, aspartic acid, 1-methylnicotinamide, acetylcarnitine, glycerophosphocholine, and oleamide, were filtered out with high performance in representing the improvement through three-month management of PCOS with high sensitivity and specificity in ROC analysis. Conclusions: The 6 metabolites were representative for the remission of PCOS through medical intervention, making them a set of potential biomarkers on assessing the outcome of PCOS management. Trial registration: ClinicalTrials.gov, NCT03264638. Registered 29 August 2017 - Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03264638

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