Open Access

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.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


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.

References

1 

McDougall C, Hurd K and Barnabe C: Systematic review of rheumatic disease epidemiology in the indigenous populations of Canada, the United States, Australia, and New Zealand. Semin Arthritis Rheum. 46:675–686. 2016. View Article : Google Scholar : PubMed/NCBI

2 

Bijlsma JW, Berenbaum F and Lafeber FP: Osteoarthritis: An updata with relevance for clinical practice. Lancet. 377:2115–2126. 2011. View Article : Google Scholar : PubMed/NCBI

3 

Sulzbacher I: Osteoarthritis: Histology and pathogenesis. Wien Med Wochenschr. 163:212–219. 2013. View Article : Google Scholar : PubMed/NCBI

4 

Hashimoto S, Ochs RL, Komiya S and Lotz M: Linkage of chondrocyte apoptosis and cartilage degradation in human osteoarthritis. Arthritis Rheum. 41:1632–1638. 1998. View Article : Google Scholar : PubMed/NCBI

5 

Schroeppel JP, Crist JD, Anderson HC and Wang J: Molecular regulation of articular chondrocyte function and its significance in osteoarthritis. Histol Histopathol. 26:377–394. 2011.PubMed/NCBI

6 

Kleine SA and Budsberg SC: Synovial membrane receptors as therapeutic targets: A review of receptor localization, structure, and function. J Orthop Res. 2017. View Article : Google Scholar : PubMed/NCBI

7 

Wang X, Hunter DJ, Jin X and Ding C: The importance of synovial inflammation in osteoarthritis: Current evidence from imageing assessments and clinical trial. Osteoarthritis Cartilage. 26:165–167. 2018. View Article : Google Scholar : PubMed/NCBI

8 

Bhattaram P and Chandrasekharan U: The joint synovium: A critical determinant of articular cartilage fate in inflammatory joint diseases. Semin Cell Dev Biol. 62:86–93. 2017. View Article : Google Scholar : PubMed/NCBI

9 

Scanzello CR and Goldring SR: The role of synovitis in osteoarthritis pathogenesis. Bone. 51:249–257. 2012. View Article : Google Scholar : PubMed/NCBI

10 

Goldenberg DL and Cohen AS: Synovial membrane histopathology in the differential diagnosis of rheumatoid arthritis, gout, pseudogout, systemic lupus erythematosus, infectious arthritis and degenerative joint disease. Medicine (Baltimore). 57:239–252. 1978. View Article : Google Scholar : PubMed/NCBI

11 

Sellam J and Berenbaum F: The role of synovitis in pathophysiology and clinical symptoms of osteoarthritis. Nat Rev Rheumatol. 6:625–635. 2010. View Article : Google Scholar : PubMed/NCBI

12 

Myers SL, Brandt KD, Ehlich JW, Braunstein EM, Shelbourne KD, Heck DA and Kalasinski LA: Synovial inflammation in patients with early osteoarthritis of the knee. J Rheumatol. 17:1662–1669. 1990.PubMed/NCBI

13 

Ayral X, Pickering EH, Woodworth TG, Mackillop N and Dougados M: Synovitis: A potential predictive factor of structural progression of medial tibiofemoral knee osteoarthritis-results of a 1 year longitudinal arthroscopic study in 422 patients. Osteoarthritis Cartilage. 13:361–367. 2005. View Article : Google Scholar : PubMed/NCBI

14 

Fernandez-Madrid F, Karvonen RL, Teitge RA, Miller PR, An T and Negendank WG: Synovial thickening detected by MR imaging in osteoarthritis of the knee confirmed by biopsy as synovitis. Magn Reson Imaging. 13:177–183. 1995. View Article : Google Scholar : PubMed/NCBI

15 

Felson DT, Niu J, Neoqi T, Goggins J, Nevitt MC, Roemer F, Torner J, Lewis CE and Guermazi A: MOST Investigators Group. Synovitis and the risk of knee osteoarthritis: The MOST Study. Osteoarthritis Cartilage. 24:458–464. 2016. View Article : Google Scholar : PubMed/NCBI

16 

Chen D, Shen J, Zhao W, Wang T, Han L, Hamilton JL and Lm HJ: Osteoarthritis: Toward a comprehensive understanding of pathological mechanism. Bone Res. 5:160442017. View Article : Google Scholar : PubMed/NCBI

17 

Li M, Zhi L, Zhang Z, BIan W and Qiu Y: Identification of potential target genes associated with the pathogenesis of osteoarthritis using microarray based analysis. Mol Med Rep. 16:2799–2806. 2017. View Article : Google Scholar : PubMed/NCBI

18 

Kong R, Gao J, Si Y and Zhao D: Combination of circulating miR-19b-3p, miR-122-5p and miR-486-5p expressions correlates with risk and disease severity of knee osteoarthritis. Am J Transl Res. 9:2852–2864. 2017.PubMed/NCBI

19 

Lambert C, Dubuc JE, Montell E, Verges J, Munaut C, Noel A and Henrotin Y: Gene expression pattern of cell from inflamed and normal areas of osteoarthritis synovial membrane. Arthritis Rheumatol. 66:960–968. 2014. View Article : Google Scholar : PubMed/NCBI

20 

Huang DW, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009. View Article : Google Scholar : PubMed/NCBI

21 

Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P, et al: The STRING database in2017: Quality-controlled protein-protein association networks, made broadly accessible. Nucleic Acids Res. 45:(Database Issue). D362–D368. 2017. View Article : Google Scholar : PubMed/NCBI

22 

Bader GD and Hogue CW: An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics. 4:22003. View Article : Google Scholar : PubMed/NCBI

23 

Li YH, Yu CY, Li XX, Zhang P, Tang J, Yang Q, Fu T, Zhang X, Cui X, Tu G, et al: Therapeutic target database update 2018: Enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res. 46:D1121–D1127. 2018.PubMed/NCBI

24 

Johnson VL and Hunter DJ: The epidemiology of osteoarthritis. Best Pract Res Clin Rheumatol. 28:5–15. 2014. View Article : Google Scholar : PubMed/NCBI

25 

Benito MJ, Veale DJ, FitzGerald O, van den Berg WB and Bresnihan B: Synovial tissue inflammation in early and late osteoarthritis. Ann Rheum Dis. 64:1263–1267. 2005. View Article : Google Scholar : PubMed/NCBI

26 

Lopes EBP, Filiberti A, Husain SA and Humphrey MB: Immune contributions to osteoarthritis. Curr Osteoporos Rep. 15:593–600. 2017. View Article : Google Scholar : PubMed/NCBI

27 

Kalaitzoglou E, Griffin TM and Humphrey MB: Innate immune responses and osteoarthritis. Curr Rheumatol Rep. 19:452017. View Article : Google Scholar : PubMed/NCBI

28 

Silawal S, Triebel J, Bertsch T and Schulze-Tanzil G: Osteoarthritis and the complement cascade. Clin Med Insights Arthritis Musculoskelet Disord. 11:11795441177514302018. View Article : Google Scholar : PubMed/NCBI

29 

Syx D, Tran PB, Miller RE and Malfait AM: Peripheral mechanisms contributing to osteoarthritis pain. Curr Rheumatol Rep. 20:92018. View Article : Google Scholar : PubMed/NCBI

30 

Nguyen LT, Sharma AR, Chakraborty C, Saibaba B, Ahn ME and Lee SS: Review of prospects of biological fluid biomarkers in osteoarthritis. Int J Mol Sci. 18:pii: E601. 2017. View Article : Google Scholar

31 

So AK, Varisco PA, Kemkes-Matthes B, Herkenne-Morard C, Chobaz-Peclat V, Gerster JC and Busso N: Arthritis is linked to local and systemic activation of coagulation and fibrinolysis pathways. J Thromb Haemost. 1:2510–2515. 2003. View Article : Google Scholar : PubMed/NCBI

32 

Brocker C, Thompson D, Matsumoto A, Nebert DW and Vasiliou V: Evolutionary divergence and functions of the human interlrukin (IL) gene family. Hum Genomics. 5:30–55. 2010. View Article : Google Scholar : PubMed/NCBI

33 

Yang F, Zhou S, Wang C, Huang Y, Li H, Wang Y, Zhu Z, Tang J and Yan M: Epigenetic modifications of interleukin-6 in synovial fibroblasts from osteoarthritis patients. Sci Rep. 7:435922017. View Article : Google Scholar : PubMed/NCBI

34 

Nair A, Gan J, Bush-Joseph C, Verma N, Tetreault MW, Saha K, Margulis A, Fogg L and Scanzello CR: Synovial chemokine expression and relationship with knee symptoms in patients with meniscal tears. Osteoarthritis Cartilage. 23:1158–1164. 2015. View Article : Google Scholar : PubMed/NCBI

35 

Rohani MG and Parks WC: Matrix remodeling by MMps during wound repair. Matrix Biol. 44–46:113–121. 2015. View Article : Google Scholar

36 

Kanyama M, Kuboki T, Kojima S, Fujisawa T, Hattori T, Takigawa M and Yamashita A: Matrix metalloproteinases and tissue inhibitors of metalloproteinases in synovial fluids of patients with temporomandibular joint osteoarthritis. J Orofac Pain. 14:20–30. 2000.PubMed/NCBI

37 

Yang CC, Lin CY, Wang HS and Lyu SR: Matrix metalloproteases and tissue inhibitors to knee osteoarthritis progression. PLoS One. 8:e796622013. View Article : Google Scholar : PubMed/NCBI

38 

Meyers MJ, Pelc M, Kamtekar S, Day J, Poda GI, Hall MK, Michener ML, Reitz BA, Mathis KJ, Pierce BS, et al: Structure-based drug design enables conversion of a DFG-in bingding CSF-1R kinase inhibitor to a DFG-out binding mode. Bioorg Med Chem Lett. 20:1543–1547. 2010. View Article : Google Scholar : PubMed/NCBI

39 

Campbell IL, Rich MJ, Bioschof RJ and Hamilton JA: The colony-stimulating factors and collagen-induced arthritis: Exacerbation of disease by M-CSF and G-CSF and requirement for endogenous M-CSF. J Leukoc Biol. 68:144–150. 2000.PubMed/NCBI

40 

Chung L: A brief introduction to the transduction of neural activity into fos signal. Dev Reprod. 19:61–67. 2015. View Article : Google Scholar : PubMed/NCBI

41 

Kinne RW, Boehm S, Iftner T, Aigner T, Vornehm S, Weseloh G, Bravo R, Emmrich F and Kroczek RA: Synovial fibroblast-like cells strongly express jun-B and C-fos proto-oncogenes in rheumatoid- and osteoarthritis. Scand J Rheumatol Suppl. 101:121–125. 1995. View Article : Google Scholar : PubMed/NCBI

42 

Annunziata M, Granata R and Ghiqo E: The IGF system. Acta Diabetol. 48:1–9. 2011. View Article : Google Scholar : PubMed/NCBI

43 

Schneiderman R, Rosenberg N, Hiss J, Lee P, Liu F, Hintz RL and Maroudas A: Concentration and size distribution of insulin-like growth factor-1 in human normal and osteoarthritis synovial fluid and cartilage. Arch Biochem Biophys. 324:173–188. 1995. View Article : Google Scholar : PubMed/NCBI

44 

Tomasello E and Vivier E: KARAP/DAP12/TYROBP: Three names and a multiplicity of biological functions. Eur J Immunol. 35:1670–1677. 2005. View Article : Google Scholar : PubMed/NCBI

45 

Crotti YN, Dharmapatni AA, Alias E, Zannettino AC, Smith MD and Haynes DR: The immunoreceptor tyrosine-based activation motif (IYAM)-related factors are increased in synovial tissue and vasculature of rheumatoid arthritic joints. Arthritis Res Ther. 14:R2452012. View Article : Google Scholar : PubMed/NCBI

46 

Daghestani HN, Pieper CF and Kraus VB: Solube macrophage biomarkers indicate inflammatory phenotypes in patients with knee osteoarthritis. Arthritis Rheumatol. 67:956–965. 2015. View Article : Google Scholar : PubMed/NCBI

47 

Nair A, Kanda V, Bush-Joseph C, Verma N, Chubinskaya S, Mikecz K, Glant TT, Malfair AM, Crow MK, Spear GT, et al: Synovial fluid from patients with early osteoarthritis modulates fibroblast-like synoviocyte responses to toll-like recepyor 4 and toll-like receptor 2 ligands via soluble CD14. Arthritis Rheum. 64:2268–2277. 2012. View Article : Google Scholar : PubMed/NCBI

48 

Fabriek BO, Dijkstra CD and van den Berg TK: The macrophage scavenger receptor CD163. Immunobiology. 210:153–160. 2005. View Article : Google Scholar : PubMed/NCBI

49 

Kim JR, Yoo JJ and Kim HA: Therapeutics in osteoarthritis based on an understanding of its molecular pathogenesis. Int J Mol Sci. 19:E6742018. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

December 2018
Volume 18 Issue 6

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

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
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