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Meta-analysis of gene expression profiles to predict response to biologic agents in rheumatoid arthritis

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Abstract

Our aim was to identify differentially expressed (DE) genes and biological processes that may help predict patient response to biologic agents for rheumatoid arthritis (RA). Using the INMEX (integrative meta-analysis of expression data) software tool, we performed a meta-analysis of publicly available microarray Gene Expression Omnibus (GEO) datasets that examined patient response to biologic therapy for RA. Three GEO datasets, containing 79 responders and 34 non-responders, were included in the meta-analysis. We identified 1,374 genes that were consistently differentially expressed in responders vs. non-responders (651 up-regulated and 723 down-regulated). The up-regulated gene with the smallest p value (p = 0.000192) was ASCC2 (Activating Signal Cointegrator 1 Complex Subunit 2), and the up-regulated gene with the largest fold change (average log fold change = −0.75869, p = 0.000206) was KLRC3 (Killer Cell Lectin-Like Receptor Subfamily C, Member 3). The down-regulated gene with the smallest p value (p = 0.000195) was MPL (Myeloproliferative Leukemia Virus Oncogene). Among the 236 GO terms associated with the set of DE genes, the most significantly enriched was “CTP biosynthetic process” (GO:0006241; p = 0.000454). Our meta-analysis identified genes that were consistently DE in responders vs. non-responders, as well as biological pathways associated with this set of genes. These results provide insight into the molecular mechanisms underlying responsiveness to biologic therapy for RA.

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Acknowledgements

This study was supported in part by a grant of the Korea Healthcare technology R&D project, Ministry for Health & Welfare, Republic of Korea (HI12C1834)

Conflict of interest statement

The authors have no conflict of interest to declare.

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Authors and Affiliations

  1. Division of Rheumatology, Department of Internal Medicine, Korea University Anam Hospital, Korea University College of Medicine, 126-1 5 ga, Anam-dong, Seongbuk-gu, Seoul, 136-705, Korea

    Young Ho Lee & Gwan Gyu Song

  2. The Hospital for Rheumatic Diseases, Hanyang University Medical Center, Seoul, Korea

    Sang-Cheol Bae

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  1. Young Ho Lee
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  2. Sang-Cheol Bae
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  3. Gwan Gyu Song
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Correspondence to Young Ho Lee.

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Lee, Y.H., Bae, SC. & Song, G.G. Meta-analysis of gene expression profiles to predict response to biologic agents in rheumatoid arthritis. Clin Rheumatol 33, 775–782 (2014). https://doi.org/10.1007/s10067-014-2547-9

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  • DOI: https://doi.org/10.1007/s10067-014-2547-9

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