Open Access

Identification of the lncRNA‑miRNA‑mRNA network associated with gastric cancer via integrated bioinformatics analysis

  • Authors:
    • Xiao‑Yu Ma
    • Yu Ma
    • Huan Zhou
    • Hui‑Jing Zhang
    • Ming‑Jun Sun
  • View Affiliations

  • Published online on: September 25, 2019     https://doi.org/10.3892/ol.2019.10922
  • Pages: 5769-5784
  • Copyright: © Ma et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

The aim of the present study was to investigate the long non‑coding RNA (lncRNA)‑microRNA (miRNA)‑mRNA regulatory network in gastric cancer (GC) using bioinformatics analysis. Two mRNA gene expression profiles, GSE79973 and GSE54129, and two miRNA expression profiles, GSE93415 and GSE78091, were downloaded from the Gene Expression Omnibus database. The differentially expressed mRNAs (DEMs) and the differentially expressed miRNAs (DEMis) were merged separately. Gene ontology and pathway enrichment analysis were conducted using the Database for Annotation, Visualization and Integrated Discovery. A protein‑protein interaction (PPI) network was then constructed and the 10 top hub genes in the network were analyzed using the Search Tool for the Retrieval of Interacting Genes. The lncRNA‑miRNA‑mRNA networks were visualized using Cytoscape software. As a result, 158 shared DEMs (40 upregulated and 118 downregulated) were identified from two mRNA datasets. A total of 30 upregulated miRNAs and 1 downregulated miRNA functioned as DEMis. The PPI network consisted of 129 nodes and 572 interactions. The 10 top hub genes were selected by degree using Cytohubba, including Jun proto‑oncogene, mitogen‑activated protein kinase (MAPK)3, transforming growth factor‑β1, Fos proto‑oncogene, AP‑1 transcription factor subunit, interleukin (IL)‑8, MAPK1, RELA proto‑oncogene nuclear factor‑κB subunit, interferon regulatory factor 7, ubiquitin like modifier and vascular endothelial growth factor A. In the lncRNA‑miRNA‑mRNA network, a total of 1,215 regulatory associations were constructed using Cytoscape. In conclusion, the present study provides a novel perspective of the molecular mechanisms underlying GC by identifying the lncRNA‑miRNA‑mRNA regulatory network via bioinformatics analysis.

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December 2019
Volume 18 Issue 6

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

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APA
Ma, X., Ma, Y., Zhou, H., Zhang, H., & Sun, M. (2019). Identification of the lncRNA‑miRNA‑mRNA network associated with gastric cancer via integrated bioinformatics analysis. Oncology Letters, 18, 5769-5784. https://doi.org/10.3892/ol.2019.10922
MLA
Ma, X., Ma, Y., Zhou, H., Zhang, H., Sun, M."Identification of the lncRNA‑miRNA‑mRNA network associated with gastric cancer via integrated bioinformatics analysis". Oncology Letters 18.6 (2019): 5769-5784.
Chicago
Ma, X., Ma, Y., Zhou, H., Zhang, H., Sun, M."Identification of the lncRNA‑miRNA‑mRNA network associated with gastric cancer via integrated bioinformatics analysis". Oncology Letters 18, no. 6 (2019): 5769-5784. https://doi.org/10.3892/ol.2019.10922