Identification of core genes and prediction of miRNAs associated with osteoporosis using a bioinformatics approach

  • Authors:
    • Yi Chai
    • Feng Tan
    • Sumin Ye
    • Feixiang Liu
    • Qiaoling Fan
  • View Affiliations

  • Published online on: September 26, 2018     https://doi.org/10.3892/ol.2018.9508
  • Pages: 468-481
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Abstract

Osteoporosis (OP) is an age‑related disease, and osteoporotic fracture is one of the major causes of disability and mortality in elderly patients (>70 years old). As the pathogenesis and molecular mechanism of OP remain unclear, the identification of disease biomarkers is important for guiding research and providing therapeutic targets. In the present study, core genes and microRNAs (miRNAs) associated with OP were identified. Differentially expressed genes (DEGs) between human mesenchymal stem cell specimens from normal osseous tissues and OP tissues were detected using the GEO2R tool of the Gene Expression Omnibus database and Morpheus. Network topological parameters were determined using NetworkAnalyzer. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed using the Database for Annotation, Visualization and Integrated Discovery, and ClueGO. Cytoscape with the Search Tool for the Retrieval of Interacting Genes and Molecular Complex Detection plug‑in was used to visualize protein‑protein interactions (PPIs). Additionally, miRNA‑gene regulatory modules were predicted using CyTargetLinker in order to guide future research. In total, 915 DEGs were identified, including 774 upregulated and 141 downregulated genes. Enriched GO terms and pathways were determined, including ‘nervous system development’, ‘regulation of molecular function’, ‘glutamatergic synapse pathway’ and ‘pathways in cancer’. The node degrees of DEGs followed power‑law distributions. A PPI network with 541 nodes and 1,431 edges was obtained. Overall, 3 important modules were identified from the PPI network. The following 10 genes were identified as core genes based on high degrees of connectivity: Albumin, PH domain leucine‑rich repeat‑containing protein phosphatase 2 (PHLPP2), DNA topoisomerase 2‑α, kininogen 1 (KNG1), interleukin 2 (IL2), leucine‑rich repeats and guanylate kinase domain containing, phosphatidylinositol‑4,5‑bisphosphate 3‑kinase catalytic subunit γ (PIK3CG), leptin, transferrin and RNA polymerase II subunit A (POLR2A). Additionally, 15 miRNA‑target interactions were obtained using CyTargetLinker. Overall, 7 miRNAs co‑regulated IL2, 3 regulated PHLPP2, 3 regulated KNG1, 1 regulated PIK3CG and 1 modulated POLR2A. These results indicate potential biomarkers in the pathogenesis of OP and therapeutic targets.
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January-2019
Volume 17 Issue 1

Print ISSN: 1792-1074
Online ISSN:1792-1082

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Spandidos Publications style
Chai Y, Tan F, Ye S, Liu F and Fan Q: Identification of core genes and prediction of miRNAs associated with osteoporosis using a bioinformatics approach. Oncol Lett 17: 468-481, 2019
APA
Chai, Y., Tan, F., Ye, S., Liu, F., & Fan, Q. (2019). Identification of core genes and prediction of miRNAs associated with osteoporosis using a bioinformatics approach. Oncology Letters, 17, 468-481. https://doi.org/10.3892/ol.2018.9508
MLA
Chai, Y., Tan, F., Ye, S., Liu, F., Fan, Q."Identification of core genes and prediction of miRNAs associated with osteoporosis using a bioinformatics approach". Oncology Letters 17.1 (2019): 468-481.
Chicago
Chai, Y., Tan, F., Ye, S., Liu, F., Fan, Q."Identification of core genes and prediction of miRNAs associated with osteoporosis using a bioinformatics approach". Oncology Letters 17, no. 1 (2019): 468-481. https://doi.org/10.3892/ol.2018.9508