Differential co-expression analysis of rheumatoid arthritis with microarray data

Wang,Kunpeng; Zhao,Liqiang; Liu,Xuefeng; Hao,Zhenyong; Zhou,Yong; Yang,Chuandong; Li,Hongqiang

doi:10.3892/mmr.2014.2491

Differential co-expression analysis of rheumatoid arthritis with microarray data

Authors:
- Kunpeng Wang
- Liqiang Zhao
- Xuefeng Liu
- Zhenyong Hao
- Yong Zhou
- Chuandong Yang
- Hongqiang Li
View Affiliations

Affiliations: Department of Orthopedics, The Fourth Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang 150001, P.R. China, Department of Orthopedics, The Harbin Fifth Hospital, Harbin, Heilongjiang 150001, P.R. China
Published online on: August 14, 2014 https://doi.org/10.3892/mmr.2014.2491
Pages: 2421-2426

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

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

Cited By (CrossRef): 0 citations Loading Articles...

Abstract

The aim of the present study was to investigate the underlying molecular mechanisms of rheumatoid arthritis (RA) using microarray expression profiles from osteoarthritis and RA patients, to improve diagnosis and treatment strategies for the condition. The gene expression profile of GSE27390 was downloaded from Gene Expression Omnibus, including 19 samples from patients with RA (n=9) or osteoarthritis (n=10). Firstly, the differentially expressed genes (DEGs) were obtained with the thresholds of |logFC|>1.0 and P<0.05, using the t‑test method in LIMMA package. Then, differentially co-expressed genes (DCGs) and differentially co-expressed links (DCLs) were screened with q<0.25 by the differential coexpression analysis and differential regulation analysis of gene expression microarray data package. Secondly, pathway enrichment analysis for DCGs was performed by the Database for Annotation, Visualization and Integrated Discovery and the DCLs associated with RA were selected by comparing the obtained DCLs with known transcription factor (TF)-targets in the TRANSFAC database. Finally, the obtained TFs were mapped to the known TF-targets to construct the network using cytoscape software. A total of 1755 DEGs, 457 DCGs and 101988 DCLs were achieved and there were 20 TFs in the obtained six TF-target relations (STAT3-TNF, PBX1‑PLAU, SOCS3-STAT3, GATA1-ETS2, ETS1-ICAM4 and CEBPE‑GATA1) and 457 DCGs. A number of TF-target relations in the constructed network were not within DCLs when the TF and target gene were DCGs. The identified TFs may have an important role in the pathogenesis of RA and have the potential to be used as biomarkers for the development of novel diagnostic and therapeutic strategies for RA.

View Figures

View References

1	Raza K: The Michael Mason prize: early rheumatoid arthritis - the window narrows. Rheumatology (Oxford). 49:406–410. 2010. View Article : Google Scholar : PubMed/NCBI
2	Nell V, Machold K, Eberl G, Stamm T, Uffmann M and Smolen J: Benefit of very early referral and very early therapy with disease-modifying anti-rheumatic drugs in patients with early rheumatoid arthritis. Rheumatology (Oxford). 43:906–914. 2004. View Article : Google Scholar : PubMed/NCBI
3	Bigelow RL, Jen EY, Delehedde M, Chari NS and McDonnell TJ: Sonic hedgehog induces epidermal growth factor dependent matrix infiltration in HaCaT keratinocytes. J Invest Dermatol. 124:457–465. 2005. View Article : Google Scholar : PubMed/NCBI
4	Iwasaki A and Medzhitov R: Regulation of adaptive immunity by the innate immune system. Science. 327:291–295. 2010. View Article : Google Scholar : PubMed/NCBI
5	Hoebe K, Janssen E and Beutler B: The interface between innate and adaptive immunity. Nat Immunol. 5:971–974. 2004. View Article : Google Scholar : PubMed/NCBI
6	Wijbrandts CA, Vergunst CE, Haringman JJ, Gerlag DM, Smeets TJ and Tak PP: Absence of changes in the number of synovial sublining macrophages after ineffective treatment for rheumatoid arthritis: implications for use of synovial sublining macrophages as a biomarker. Arthritis Rheum. 56:3869–3871. 2007. View Article : Google Scholar
7	Firestein GS, Nguyen K, Aupperle KR, Yeo M, Boyle DL and Zvaifler NJ: Apoptosis in rheumatoid arthritis: p53 overexpression in rheumatoid arthritis synovium. Am J Pathol. 149:2143–2151. 1996.PubMed/NCBI
8	Yamanishi Y, Boyle DL, Pinkoski MJ, et al: Regulation of joint destruction and inflammation by p53 in collagen-induced arthritis. Am J Pathol. 160:123–130. 2002. View Article : Google Scholar : PubMed/NCBI
9	Salvador G, Sanmarti R, Garcia-Peiró A, Rodríguez-Cros J, Muñoz-Gómez J and Cañete J: p53 expression in rheumatoid and psoriatic arthritis synovial tissue and association with joint damage. Ann Rheum Dis. 64:183–187. 2005. View Article : Google Scholar : PubMed/NCBI
10	McGovern DP, Hysi P, Ahmad T, et al: Association between a complex insertion/deletion polymorphism in NOD1 (CARD4) and susceptibility to inflammatory bowel disease. Hum Mol Genet. 14:1245–1250. 2005. View Article : Google Scholar : PubMed/NCBI
11	Porcelli S, Yockey CE, Brenner MB and Balk SP: Analysis of T cell antigen receptor (TCR) expression by human peripheral blood CD4-8-alpha/beta T cells demonstrates preferential use of several V beta genes and an invariant TCR alpha chain. J Exp Med. 178:1–16. 1993. View Article : Google Scholar
12	Sen M: Wnt signalling in rheumatoid arthritis. Rheumatology (Oxford). 44:708–713. 2005. View Article : Google Scholar : PubMed/NCBI
13	Deon D, Ahmed S, Tai K, et al: Cross-talk between IL-1 and IL-6 signaling pathways in rheumatoid arthritis synovial fibroblasts. J Immunol. 167:5395–5403. 2001. View Article : Google Scholar : PubMed/NCBI
14	Feldmann M and Maini RN; Lasker Clinical Medical Research Award. TNF defined as a therapeutic target for rheumatoid arthritis and other autoimmune diseases. Nat Med. 9:1245–1250. 2003. View Article : Google Scholar : PubMed/NCBI
15	Nakano K, Whitaker JW, Boyle DL, Wang W and Firestein GS: DNA methylome signature in rheumatoid arthritis. Ann Rheum Dis. 72:110–117. 2013. View Article : Google Scholar : PubMed/NCBI
16	Lee HK, Hsu AK, Sajdak J, Qin J and Pavlidis P: Coexpression analysis of human genes across many microarray data sets. Genome Res. 14:1085–1094. 2004. View Article : Google Scholar : PubMed/NCBI
17	Stuart JM, Segal E, Koller D and Kim SK: A gene-coexpression network for global discovery of conserved genetic modules. Science. 302:249–255. 2003. View Article : Google Scholar : PubMed/NCBI
18	Lee H-M, Sugino H, Aoki C, et al: Abnormal networks of immune response-related molecules in bone marrow cells from patients with rheumatoid arthritis as revealed by DNA microarray analysis. Arthritis Res Ther. 13:R892011. View Article : Google Scholar : PubMed/NCBI
19	Irizarry RA, Hobbs B, Collin F, et al: Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 4:249–264. 2003. View Article : Google Scholar
20	Diboun I, Wernisch L, Orengo CA and Koltzenburg M: Microarray analysis after RNA amplification can detect pronounced differences in gene expression using limma. BMC Genomics. 7:2522006. View Article : Google Scholar : PubMed/NCBI
21	Yu H, Liu B-H, Ye ZQ, Li C, Li YX and Li YY: Link-based quantitative methods to identify differentially coexpressed genes and gene pairs. BMC Bioinformatics. 12:3152011. View Article : Google Scholar : PubMed/NCBI
22	Liu BH, Yu H, Tu K, Li C, Li YX and Li YY: DCGL: an R package for identifying differentially coexpressed genes and links from gene expression microarray data. Bioinformatics. 26:2637–2638. 2010. View Article : Google Scholar : PubMed/NCBI
23	Kanehisa M and Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28:27–30. 2000. View Article : Google Scholar : PubMed/NCBI
24	Huang da W, Sherman BT, Tan Q, Collins JR, Alvord G, Roayaei J, Stephens R, Baseler MW, Lane HC and Lempicki RA: The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome Biol. 8:R1832007.
25	Wingender E, Chen X, Hehl R, et al: TRANSFAC: an integrated system for gene expression regulation. Nucleic Acids Res. 28:316–319. 2000. View Article : Google Scholar : PubMed/NCBI
26	Smoot ME, Ono K, Ruscheinski J, Wang P-L and Ideker T: Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 27:431–432. 2011. View Article : Google Scholar : PubMed/NCBI
27	Zhang ML, Huang ZZ and Zheng Y: Estimates and prediction on incidence, mortality and prevalence of breast cancer in China, 2008. Zhonghua Liu Xing Bing Xue Za Zhi. 33:1049–1051. 2012.(In Chinese).
28	Hu F, Mu R, Zhu J, et al: Hypoxia and hypoxia-inducible factor-1α provoke toll-like receptor signalling-induced inflammation in rheumatoid arthritis. Ann Rheum Dis. May 3–2013.(Epub ahead of print).
29	Xue F, Zhang C, He Z, Ding L and Xiao H: Analysis of critical molecules and signaling pathways in osteoarthritis and rheumatoid arthritis. Mol Med Rep. 7:603–607. 2013.PubMed/NCBI
30	Plantinga TS, Fransen J, Knevel R, et al: Role of NOD1 polymorphism in susceptibility and clinical progression of rheumatoid arthritis. Rheumatology (Oxford). 52:806–814. 2013. View Article : Google Scholar : PubMed/NCBI
31	Alexander WS: Suppressors of cytokine signalling (SOCS) in the immune system. Nat Rev Immunol. 2:410–416. 2002.PubMed/NCBI
32	Kubo M, Hanada T and Yoshimura A: Suppressors of cytokine signaling and immunity. Nat Immunol. 4:1169–1176. 2003. View Article : Google Scholar : PubMed/NCBI
33	Egan PJ, Lawlor KE, Alexander WS and Wicks IP: Suppressor of cytokine signaling-1 regulates acute inflammatory arthritis and T cell activation. J Clin Invest. 111:915–924. 2003. View Article : Google Scholar : PubMed/NCBI
34	de Hooge AS, van de Loo FA, Koenders MI, et al: Local activation of STAT-1 and STAT-3 in the inflamed synovium during zymosan-induced arthritis: exacerbation of joint inflammation in STAT-1 gene-knockout mice. Arthritis Rheum. 50:2014–2023. 2004.PubMed/NCBI
35	Isomäki P, Alanärä T, Isohanni P, et al: The expression of SOCS is altered in rheumatoid arthritis. Rheumatology (Oxford). 46:1538–1546. 2007.PubMed/NCBI
36	Nakajima K, Yamanaka Y, Nakae K, et al: A central role for Stat3 in IL-6-induced regulation of growth and differentiation in M1 leukemia cells. EMBO J. 15:3651–3658. 1996.PubMed/NCBI
37	Feldmann M, Brennan FM and Maini RN: Role of cytokines in rheumatoid arthritis. Annu Rev Immunol. 14:397–440. 1996. View Article : Google Scholar : PubMed/NCBI
38	Elliott MJ, Maini R, Feldmann M, et al: Randomised double-blind comparison of chimeric monoclonal antibody to tumour necrosis factor alpha (cA2) versus placebo in rheumatoid arthritis. Lancet. 344:1105–1110. 1994. View Article : Google Scholar : PubMed/NCBI
39	Elliott MJ, Maini R, Feldmann M, et al: Repeated therapy with monoclonal antibody to tumour necrosis factor alpha (cA2) in patients with rheumatoid arthritis. Lancet. 344:1125–1127. 1994. View Article : Google Scholar : PubMed/NCBI
40	Mihara M, Moriya Y, Kishimoto T and Ohsugi Y: Interleukin-6 (IL-6) induces the proliferation of synovial fibroblastic cells in the presence of soluble IL-6 receptor. Br J Rheumatol. 34:321–325. 1995. View Article : Google Scholar
41	Tamura T, Udagawa N, Takahashi N, et al: Soluble interleukin-6 receptor triggers osteoclast formation by interleukin 6. Proc Natl Acad Sci USA. 90:11924–11928. 1993. View Article : Google Scholar : PubMed/NCBI