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Article Open Access

Exploring of the molecular mechanism of rhinitis via bioinformatics methods

  • Authors:
    • Yufen Song
    • Zhaohui Yan
  • View Affiliations / Copyright

    Affiliations: Department of Otolaryngology, The Third Central Hospital of Tianjin, Tianjin 300170, P.R. China
    Copyright: © Song et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Pages: 3014-3020
    |
    Published online on: December 7, 2017
       https://doi.org/10.3892/mmr.2017.8213
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Abstract

The aim of this study was to analyze gene expression profiles for exploring the function and regulatory network of differentially expressed genes (DEGs) in pathogenesis of rhinitis by a bioinformatics method. The gene expression profile of GSE43523 was downloaded from the Gene Expression Omnibus database. The dataset contained 7 seasonal allergic rhinitis samples and 5 non‑allergic normal samples. DEGs between rhinitis samples and normal samples were identified via the limma package of R. The webGestal database was used to identify enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of the DEGs. The differentially co‑expressed pairs of the DEGs were identified via the DCGL package in R, and the differential co‑expression network was constructed based on these pairs. A protein‑protein interaction (PPI) network of the DEGs was constructed based on the Search Tool for the Retrieval of Interacting Genes database. A total of 263 DEGs were identified in rhinitis samples compared with normal samples, including 125 downregulated ones and 138 upregulated ones. The DEGs were enriched in 7 KEGG pathways. 308 differential co‑expression gene pairs were obtained. A differential co‑expression network was constructed, containing 212 nodes. In total, 148 PPI pairs of the DEGs were identified, and a PPI network was constructed based on these pairs. Bioinformatics methods could help us identify significant genes and pathways related to the pathogenesis of rhinitis. Steroid biosynthesis pathway and metabolic pathways might play important roles in the development of allergic rhinitis (AR). Genes such as CDC42 effector protein 5, solute carrier family 39 member A11 and PR/SET domain 10 might be also associated with the pathogenesis of AR, which provided references for the molecular mechanisms of AR.
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Copy and paste a formatted citation
Spandidos Publications style
Song Y and Yan Z: Exploring of the molecular mechanism of rhinitis via bioinformatics methods. Mol Med Rep 17: 3014-3020, 2018.
APA
Song, Y., & Yan, Z. (2018). Exploring of the molecular mechanism of rhinitis via bioinformatics methods. Molecular Medicine Reports, 17, 3014-3020. https://doi.org/10.3892/mmr.2017.8213
MLA
Song, Y., Yan, Z."Exploring of the molecular mechanism of rhinitis via bioinformatics methods". Molecular Medicine Reports 17.2 (2018): 3014-3020.
Chicago
Song, Y., Yan, Z."Exploring of the molecular mechanism of rhinitis via bioinformatics methods". Molecular Medicine Reports 17, no. 2 (2018): 3014-3020. https://doi.org/10.3892/mmr.2017.8213
Copy and paste a formatted citation
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Spandidos Publications style
Song Y and Yan Z: Exploring of the molecular mechanism of rhinitis via bioinformatics methods. Mol Med Rep 17: 3014-3020, 2018.
APA
Song, Y., & Yan, Z. (2018). Exploring of the molecular mechanism of rhinitis via bioinformatics methods. Molecular Medicine Reports, 17, 3014-3020. https://doi.org/10.3892/mmr.2017.8213
MLA
Song, Y., Yan, Z."Exploring of the molecular mechanism of rhinitis via bioinformatics methods". Molecular Medicine Reports 17.2 (2018): 3014-3020.
Chicago
Song, Y., Yan, Z."Exploring of the molecular mechanism of rhinitis via bioinformatics methods". Molecular Medicine Reports 17, no. 2 (2018): 3014-3020. https://doi.org/10.3892/mmr.2017.8213
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