Tae-Hwan Kim1, Sung Jae Choi2, Young Ho Lee2, Gwan Gyu Song2, Jong Dae Ji3. 1. Hanyang University Hospital for Rheumatic Diseases, Seoul, South Korea. 2. Division of Rheumatology, College of Medicine, Korea University, 126-1, Anam-Dong 5-Ga, Sungbuk-Ku, Seoul 136-705, South Korea. 3. Division of Rheumatology, College of Medicine, Korea University, 126-1, Anam-Dong 5-Ga, Sungbuk-Ku, Seoul 136-705, South Korea. Electronic address: jjdjmesy@korea.ac.kr.
Abstract
OBJECTIVES: Anti-tumor necrosis factor (TNF) therapy is the treatment of choice for rheumatoid arthritis (RA) patients in whom standard disease-modifying anti-rheumatic drugs are ineffective. However, a substantial proportion of RA patients treated with anti-TNF agents do not show a significant clinical response. Therefore, biomarkers predicting response to anti-TNF agents are needed. Recently, gene expression profiling has been applied in research for developing such biomarkers. METHODS: We compared gene expression profiles reported by previous studies dealing with the responsiveness of anti-TNF therapy in RA patients and attempted to identify differentially expressed genes (DEGs) that discriminated between responders and non-responders to anti-TNF therapy. We used microarray datasets available at the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO). RESULTS: This analysis included 6 studies and 5 sets of microarray data that used peripheral blood samples for identification of DEGs predicting response to anti-TNF therapy. We found little overlap in the DEGs that were highly ranked in each study. Three DEGs including IL2RB, SH2D2A and G0S2 appeared in more than 1 study. In addition, a meta-analysis designed to increase statistical power found one DEG, G0S2 by the Fisher's method. CONCLUSION: Our finding suggests the possibility that G0S2 plays as a biomarker to predict response to anti-TNF therapy in patients with rheumatoid arthritis. Further investigations based on larger studies are therefore needed to confirm the significance of G0S2 in predicting response to anti-TNF therapy.
OBJECTIVES:Anti-tumor necrosis factor (TNF) therapy is the treatment of choice for rheumatoid arthritis (RA) patients in whom standard disease-modifying anti-rheumatic drugs are ineffective. However, a substantial proportion of RApatients treated with anti-TNF agents do not show a significant clinical response. Therefore, biomarkers predicting response to anti-TNF agents are needed. Recently, gene expression profiling has been applied in research for developing such biomarkers. METHODS: We compared gene expression profiles reported by previous studies dealing with the responsiveness of anti-TNF therapy in RApatients and attempted to identify differentially expressed genes (DEGs) that discriminated between responders and non-responders to anti-TNF therapy. We used microarray datasets available at the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO). RESULTS: This analysis included 6 studies and 5 sets of microarray data that used peripheral blood samples for identification of DEGs predicting response to anti-TNF therapy. We found little overlap in the DEGs that were highly ranked in each study. Three DEGs including IL2RB, SH2D2A and G0S2 appeared in more than 1 study. In addition, a meta-analysis designed to increase statistical power found one DEG, G0S2 by the Fisher's method. CONCLUSION: Our finding suggests the possibility that G0S2 plays as a biomarker to predict response to anti-TNF therapy in patients with rheumatoid arthritis. Further investigations based on larger studies are therefore needed to confirm the significance of G0S2 in predicting response to anti-TNF therapy.
Authors: Halima Moncrieffe; Mark F Bennett; Monica Tsoras; Lorie K Luyrink; Anne L Johnson; Huan Xu; Jason Dare; Mara L Becker; Sampath Prahalad; Margalit Rosenkranz; Kathleen M O'Neil; Peter A Nigrovic; Thomas A Griffin; Daniel J Lovell; Alexei A Grom; Mario Medvedovic; Susan D Thompson Journal: Rheumatology (Oxford) Date: 2017-09-01 Impact factor: 7.580
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