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Bioinformatics analysis to identify key genes and pathways influencing synovial inflammation in osteoarthritis

  • Authors:
    • Jie Lin
    • Guangwen Wu
    • Zhongsheng Zhao
    • Yanfeng Huang
    • Jun Chen
    • Changlong Fu
    • Jinxia Ye
    • Xianxiang Liu

  • View Affiliations

  • Published online on: October 23, 2018     https://doi.org/10.3892/mmr.2018.9575
  • Pages: 5594-5602

  • Copyright: © Lin et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

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Abstract

Osteoarthritis (OA) is a chronic arthropathy that occurs in the middle‑aged and elderly population. The present study aimed to identify gene signature differences between synovial cells from OA synovial membrane with and without inflammation, and to explain the potential mechanisms involved. The differentially expressed genes (DEGs) between 12 synovial membrane with inflammation and 12 synovial membrane without inflammation from the dataset GSE46750 were identified using the Gene Expression Omnibus 2R. The DEGs were subjected to enrichment analysis, protein‑protein interaction (PPI) analysis and module analysis. The analysis results were compared with text‑mining results. A total of 174 DEGs were identified. Gene Ontology enrichment results demonstrated that functional molecules encoded by the DEGs primarily had extracellular location, molecular functions predominantly involving ‘chemokine activity’ and ‘cytokine activity’, and were associated with biological processes, including ‘inflammatory response’ and ‘immune response’. The Kyoto Encyclopedia of Genes and Genomes results demonstrated that DEGS may function through pathways associated with ‘rheumatoid arthritis’, ‘chemokine signaling pathway’, ‘complement and coagulation cascades’, ‘TNF signaling pathway’, ‘intestinal immune networks for IgA production’, ‘cytokine‑cytokine receptor interaction’, ‘allograft rejection’, ‘Toll‑like receptor signaling pathway’ and ‘antigen processing and presentation’. The top 10 hub genes [interleukin (IL)6, IL8, matrix metallopeptidase (MMP)9, colony stimulating factor 1 receptor, FOS proto‑oncogene, AP1 transcription factor subunit, insulin‑like growth factor 1, TYRO protein tyrosine kinase binding protein, MMP3, cluster of differentiation (CD)14 and CD163] and four gene modules were identified from the PPI network using Cytoscape. In addition, text‑mining was used to identify the commonly used drugs and their targets for the treatment of OA. It was initially verified whether the results of the present study were useful for the study of OA treatment targets and pathways. The present study provided insight for the molecular mechanisms of OA synovitis. The hub genes and associated pathways derived from analysis may be targets for OA treatment. IL8 and MMP9, which were validated by text‑mining, may be used as molecular targets for the OA treatment, while other hub genes require further validation.
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December-2018
Volume 18 Issue 6

Print ISSN: 1791-2997
Online ISSN:1791-3004

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  • Ranked Medicine Research and Experimental
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  • CiteScore: 6.9
  • CiteScore Rank: Q2: #98/328 Biochemistry, Genetics and Molecular Biology
  • Source Normalized Impact per Paper (SNIP): 0.721
  • SCImago Journal Rank (SJR): 0.65
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Spandidos Publications style
Lin J, Wu G, Zhao Z, Huang Y, Chen J, Fu C, Ye J and Liu X: Bioinformatics analysis to identify key genes and pathways influencing synovial inflammation in osteoarthritis. Mol Med Rep 18: 5594-5602, 2018
APA
Lin, J., Wu, G., Zhao, Z., Huang, Y., Chen, J., Fu, C. ... Liu, X. (2018). Bioinformatics analysis to identify key genes and pathways influencing synovial inflammation in osteoarthritis. Molecular Medicine Reports, 18, 5594-5602. https://doi.org/10.3892/mmr.2018.9575
MLA
Lin, J., Wu, G., Zhao, Z., Huang, Y., Chen, J., Fu, C., Ye, J., Liu, X."Bioinformatics analysis to identify key genes and pathways influencing synovial inflammation in osteoarthritis". Molecular Medicine Reports 18.6 (2018): 5594-5602.
Chicago
Lin, J., Wu, G., Zhao, Z., Huang, Y., Chen, J., Fu, C., Ye, J., Liu, X."Bioinformatics analysis to identify key genes and pathways influencing synovial inflammation in osteoarthritis". Molecular Medicine Reports 18, no. 6 (2018): 5594-5602. https://doi.org/10.3892/mmr.2018.9575