Dataset Information

Bioinformatics identification of microRNAs involved in polycystic ovary syndrome based on microarray data.

ABSTRACT: Polycystic ovary syndrome (PCOS) is the most common endocrine disease in women of reproductive age. MicroRNAs (miRNAs or miRs) serve important roles in the physiological and pathological process of PCOS. To identify PCOS?associated miRNAs, the dataset GSE84376 was extracted from the Gene Expression Omnibus database. Differentially expressed miRNAs (DE?miRNAs) were obtained from Gene?Cloud Biotechnology Information and potential target genes were predicted using TargetScan, DIANA?microT?CDS, miRDB and miRTarBase tools. Gene Ontology enrichment analysis was performed using Metascape and a protein?protein interaction network was constructed using Cytoscape. Transcription factors were obtained from FunRich. DE?miRNAs were verified by reverse transcription?quantitative PCR. At the screening phase, there were seven DE?miRNAs in the PCOS group not present in the control group. In total, 935 target genes were identified, which are involved in the development and maturation of oocytes. Mitogen?activated protein kinase 1, phosphatase and tensin homolog, cAMP responsive element binding protein 1, signal transducer and activator of transcription 3, interferon ?, Fms?related tyrosine kinase 1, transcription factor p65, insulin receptor substrate 1, DnaJ homolog superfamily C member 10 and casein kinase 2 ? 1 were identified as the top 10 hub genes in the protein?protein interaction network. Specificity protein 1 was the most enriched transcription factor. At the validation phase, the levels of Homo sapiens (hsa)?miR?3188 and hsa?miR?3135b were significantly higher in the PCOS group than in the control group. In addition, the expression level of hsa?miR?3135b was significantly correlated with the number of oocytes retrieved, the fertilization rate and the cleavage rate (P<0.05). The present bioinformatics study on miRNAs may offer a novel understanding of the mechanism of PCOS, and may serve to identify novel miRNA therapeutic targets.

SUBMITTER: Hou Y

PROVIDER: S-EPMC6579986 | biostudies-literature | 2019 Jul

REPOSITORIES: biostudies-literature

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Bioinformatics identification of microRNAs involved in polycystic ovary syndrome based on microarray data.

Hou Yan Y Wang Yaoqin Y Xu Suming S Qi Gaimei G Wu Xueqing X

Molecular medicine reports 20190516 1

Polycystic ovary syndrome (PCOS) is the most common endocrine disease in women of reproductive age. MicroRNAs (miRNAs or miRs) serve important roles in the physiological and pathological process of PCOS. To identify PCOS‑associated miRNAs, the dataset GSE84376 was extracted from the Gene Expression Omnibus database. Differentially expressed miRNAs (DE‑miRNAs) were obtained from Gene‑Cloud Biotechnology Information and potential target genes were predicted using TargetScan, DIANA‑microT‑CDS, miRD ...[more]

PMID: 31115532

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Project description:BackgroundIn recent years, clinical studies have found that there is a close relationship between osteoporosis and polycystic ovary syndrome. However, there are few literature on the pathogenesis of osteoporosis and polycystic ovary syndrome. In order to clarify their common pathogenic mechanism and provide potential targets for drugs to regulate them at the same time, bioinformatics methods are used to explore, so as to provide a new direction for the study of the relationship between diseases in the future.MethodsTo screen the targets of osteoporosis and polycystic ovary syndrome by Genecards, Online Mendelian Inheritance in Man databases and Therapeutic Target Database to take the intersection of the two mappings and upload the intersection targets to the STRING database to construct protein-protein interaction network; to screen the core targets by degree value and import them to Metascape database for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis; and finally, to construct the visualization network of core targets and pathways by Cytoscape software. Ethical approval and informed consent of patients are not required because the data used in this study is publicly available and does not involve individual patient data or privacy.ResultsThe core targets of polycystic ovary syndrome and osteoporosis were insulin gene, insulin-like growth factor 1, CTNNB1, serine/threonine kinase 1, signal transducer and activator of transcription 3, LEP, etc. The biological processes involved include the regulation of protein phosphorylation, cell proliferation and differentiation, hormone endocrine, reproductive system and skeletal system. The related pathways were concentrated in Foxo signaling pathway, HTLV-I infection, PI3K-AKT signaling pathway, MAPK signaling pathway and AGE-RAGE signaling pathway in diabetic complications.ConclusionsThere is a close relationship between osteoporosis and polycystic ovary syndrome in terms of target and molecular mechanism. This study used bioinformatics to clarify their targets and mechanisms, providing potential targets for drugs to regulate both diseases simultaneously and providing new directions to explore the relationship between the diseases.

Dataset Information

Bioinformatics identification of microRNAs involved in polycystic ovary syndrome based on microarray data.

Publications

Bioinformatics identification of microRNAs involved in polycystic ovary syndrome based on microarray data.

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