Affordable Access

Access to the full text

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

Authors
  • Lee, Young Ho1
  • Bae, Sang-Cheol2
  • Song, Gwan Gyu1
  • 1 Korea University College of Medicine, Division of Rheumatology, Department of Internal Medicine, Korea University Anam Hospital, 126-1 5 ga, Anam-dong, Seongbuk-gu, Seoul, 136-705, Korea , Seoul (South Korea)
  • 2 Hanyang University Medical Center, The Hospital for Rheumatic Diseases, Seoul, Korea , Seoul (South Korea)
Type
Published Article
Journal
Clinical Rheumatology
Publisher
Springer-Verlag
Publication Date
Mar 05, 2014
Volume
33
Issue
6
Pages
775–782
Identifiers
DOI: 10.1007/s10067-014-2547-9
Source
Springer Nature
Keywords
License
Yellow

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.

Report this publication

Statistics

Seen <100 times