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

Research output: Contribution to journalArticle

28 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)325-330
Number of pages6
JournalJoint Bone Spine
Volume81
Issue number4
DOIs
Publication statusPublished - 2014 Jan 1

Fingerprint

Transcriptome
Rheumatoid Arthritis
Tumor Necrosis Factor-alpha
Gene Expression
Biomarkers
Genes
Therapeutics
Information Centers
Antirheumatic Agents
Datasets
Gene Expression Profiling
Biotechnology
Meta-Analysis
Research

Keywords

  • Anti-TNF
  • Microarray
  • Response
  • Rheumatoid arthritis

ASJC Scopus subject areas

  • Rheumatology

Cite this

@article{48bd692655614c48933ee9eb1cd83ca2,
title = "Gene expression profile predicting the response to anti-TNF treatment in patients with rheumatoid arthritis; analysis of GEO datasets",
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.",
keywords = "Anti-TNF, Microarray, Response, Rheumatoid arthritis",
author = "Kim, {Tae Hwan} and Sungjae Choi and Lee, {Young Ho} and Song, {Gwan Gyu} and Ji, {Jong Dae}",
year = "2014",
month = "1",
day = "1",
doi = "10.1016/j.jbspin.2014.01.013",
language = "English",
volume = "81",
pages = "325--330",
journal = "Revue du Rhumatisme (English Edition)",
issn = "1169-8446",
publisher = "Elsevier Masson",
number = "4",

}

TY - JOUR

T1 - Gene expression profile predicting the response to anti-TNF treatment in patients with rheumatoid arthritis; analysis of GEO datasets

AU - Kim, Tae Hwan

AU - Choi, Sungjae

AU - Lee, Young Ho

AU - Song, Gwan Gyu

AU - Ji, Jong Dae

PY - 2014/1/1

Y1 - 2014/1/1

N2 - 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.

AB - 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.

KW - Anti-TNF

KW - Microarray

KW - Response

KW - Rheumatoid arthritis

UR - http://www.scopus.com/inward/record.url?scp=84904135761&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84904135761&partnerID=8YFLogxK

U2 - 10.1016/j.jbspin.2014.01.013

DO - 10.1016/j.jbspin.2014.01.013

M3 - Article

VL - 81

SP - 325

EP - 330

JO - Revue du Rhumatisme (English Edition)

JF - Revue du Rhumatisme (English Edition)

SN - 1169-8446

IS - 4

ER -