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 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 Let-7i Regulates KGN Proliferation and Estradiol Biosynthesis By Directly Targeting IMP2 in Polycystic Ovary Syndrome

2021 ◽  
Author(s):  
Xiao Xu ◽  
Hao-Ran Shen ◽  
Min Yu ◽  
Mei-Rong Du ◽  
Xue-Lian Li
Keyword(s):  
Cell Cycle ◽  
Polycystic Ovary Syndrome ◽  
Bioinformatics Analysis ◽  
Polycystic Ovary ◽  
Luciferase Reporter Assay ◽  
Luciferase Reporter ◽  
Functional Study ◽  
Reporter Assay ◽  
Ovary Syndrome ◽  
Qrt Pcr

Abstract Background: Increased granulosa cell division is associated with abnormal folliculogenesis in polycystic ovary syndrome (PCOS). As the most abundant microRNA molecule in the development of follicles, let-7i was found to be differentially expressed in PCOS patients and controls. This study aimed to investigate the role of let-7i in PCOS and explore its related mechanisms.Methods: The expression of let-7i was measured in GCs from women with PCOS and without PCOS. An immortalized human granulose cell line, KGN, was used for the functional study. Let-7i mimics, let-7i inhibitors, lentiviruses expressing IMP2 and small-interfering RNA were transfected respectively into KGN cells. KGN cell proliferation was determined by an EdU assay kit and cck-8. Cell cycle and apoptosis were determined by PI staining and flow cytometry analysis. Quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting were utilized to evaluate genes expression. Estradiol biosynthesis was determined by ELISA analysis. Bioinformatics analysis and luciferase reporter assay were applied to confirm the target gene of let-7i.Results: let-7i was down-regulated in PCOS GCs. Let-7i mimics (150 nM) inhibited KGN proliferation, arrested cell cycle progression, and decreased aromatase expression and estradiol production, while the let-7i inhibitors (150 nM) had the opposite effect. Bioinformatics analysis and qRT-PCR identified IMP2 was a target of let-7i. QRT-PCR and western blot analysis indicated that IMP2 was up-regulated in PCOS GCs and the expression of IMP2 was suppressed by let-7i in KGN. The further luciferase reporter assay combined with rescue assay validated that let-7i inhibited KGN proliferation and estradiol production by directly targeting IMP2 mRNA.Conclusions: Let-7i was down-regulated in PCOS GCs. Let-7i overexpression inhibited KGN proliferation and decreased estradiol production in an IMP2-dependent manner, providing insights into the pathogenesis of PCOS.

scholarly journals Exploring the Pharmacological Mechanism of Quercetin-Resveratrol Combination for Polycystic Ovary Syndrome: A Systematic Pharmacological Strategy-Based Research

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Kailin Yang ◽  
Liuting Zeng ◽  
Tingting Bao ◽  
Zhiyong Long ◽  
Bing Jin
Keyword(s):  
Polycystic Ovary Syndrome ◽  
Molecular Mechanism ◽  
Polycystic Ovary ◽  
Pathway Enrichment Analysis ◽  
Biological Processes ◽  
Ppi Network ◽  
Ovary Syndrome ◽  
Potential Target ◽  
Target Network ◽  
Compound Target

AbstractResveratrol and quercetin have effects on polycystic ovary syndrome (PCOS). Hence, resveratrol combined with quercetin may have better effects on it. However, because of the limitations in animal and human experiments, the pharmacological and molecular mechanism of quercetin-resveratrol combination (QRC) remains to be clarified. In this research, a systematic pharmacological approach comprising multiple compound target collection, multiple potential target prediction, and network analysis was used for comparing the characteristic of resveratrol, quercetin and QRC, and exploring the mechanism of QRC. After that, four networks were constructed and analyzed: (1) compound-compound target network; (2) compound-potential target network; (3) QRC-PCOS PPI network; (4) QRC-PCOS-other human proteins (protein-protein interaction) PPI network. Through GO and pathway enrichment analysis, it can be found that three compounds focus on different biological processes and pathways; and it seems that QRC combines the characteristics of resveratrol and quercetin. The in-depth study of QRC further showed  more PCOS-related biological processes and pathways. Hence, this research not only offers clues to the researcher who is interested in comparing the differences among resveratrol, quercetin and QRC, but also provides hints for the researcher who wants to explore QRC’s various synergies and its pharmacological and molecular mechanism.

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 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
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