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预测类风湿关节炎患者抗TNF治疗反应的基因表达谱;GEO数据集分析

Gene expression profile predicting the response to anti-TNF treatment in patients with rheumatoid arthritis; analysis of GEO datasets.

作者信息

Kim Tae-Hwan, Choi Sung Jae, Lee Young Ho, Song Gwan Gyu, Ji Jong Dae

机构信息

Hanyang University Hospital for Rheumatic Diseases, Seoul, South Korea.

Division of Rheumatology, College of Medicine, Korea University, 126-1, Anam-Dong 5-Ga, Sungbuk-Ku, Seoul 136-705, South Korea.

出版信息

Joint Bone Spine. 2014 Jul;81(4):325-30. doi: 10.1016/j.jbspin.2014.01.013. Epub 2014 Feb 20.

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.

摘要

目的

对于标准抗风湿药物治疗无效的类风湿关节炎(RA)患者,抗肿瘤坏死因子(TNF)治疗是首选治疗方法。然而,相当一部分接受抗TNF药物治疗的RA患者并未表现出显著的临床反应。因此,需要能够预测抗TNF药物反应的生物标志物。最近,基因表达谱分析已应用于此类生物标志物的研发研究中。

方法

我们比较了先前关于RA患者抗TNF治疗反应性研究报告的基因表达谱,并试图识别区分抗TNF治疗反应者和无反应者的差异表达基因(DEG)。我们使用了美国国立生物技术信息中心(NCBI)基因表达综合数据库(GEO)中可用的微阵列数据集。

结果

该分析纳入了6项研究和5组微阵列数据,这些数据使用外周血样本识别预测抗TNF治疗反应的DEG。我们发现每项研究中排名靠前的DEG几乎没有重叠。包括IL2RB、SH2D2A和G0S2在内的3个DEG出现在不止1项研究中。此外,一项旨在提高统计效力的荟萃分析通过Fisher方法发现了一个DEG,即G0S2。

结论

我们的发现提示G0S2有可能作为预测类风湿关节炎患者抗TNF治疗反应的生物标志物。因此,需要基于更大规模研究进行进一步调查,以确认G0S2在预测抗TNF治疗反应中的意义。

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