Open Access

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

  • Authors:
    • Yan Hou
    • Yaoqin Wang
    • Suming Xu
    • Gaimei Qi
    • Xueqing Wu
  • View Affiliations

  • Published online on: May 16, 2019     https://doi.org/10.3892/mmr.2019.10253
  • Pages: 281-291
  • Copyright: © Hou et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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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 γ, Fms‑related tyrosine kinase 1, transcription factor p65, insulin receptor substrate 1, DnaJ homolog superfamily C member 10 and casein kinase 2 α 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.
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July-2019
Volume 20 Issue 1

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Spandidos Publications style
Hou Y, Wang Y, Xu S, Qi G and Wu X: Bioinformatics identification of microRNAs involved in polycystic ovary syndrome based on microarray data. Mol Med Rep 20: 281-291, 2019
APA
Hou, Y., Wang, Y., Xu, S., Qi, G., & Wu, X. (2019). Bioinformatics identification of microRNAs involved in polycystic ovary syndrome based on microarray data. Molecular Medicine Reports, 20, 281-291. https://doi.org/10.3892/mmr.2019.10253
MLA
Hou, Y., Wang, Y., Xu, S., Qi, G., Wu, X."Bioinformatics identification of microRNAs involved in polycystic ovary syndrome based on microarray data". Molecular Medicine Reports 20.1 (2019): 281-291.
Chicago
Hou, Y., Wang, Y., Xu, S., Qi, G., Wu, X."Bioinformatics identification of microRNAs involved in polycystic ovary syndrome based on microarray data". Molecular Medicine Reports 20, no. 1 (2019): 281-291. https://doi.org/10.3892/mmr.2019.10253