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Meta-analysis of Microarray Data Using a Pathway-based Approach Identifies a 37-Gene Expression Signature for Systemic Lupus Erythematosus in Human Peripheral Blood Mononuclear Cells by Dhivya Arasappan; Weida Tong; Padmaja Mummaneni; Hong Fang; Shashi Amur is a Medicine article available to read on EtoBox.

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## Background A number of publications have reported the use of microarray technology to identify gene expression signatures to infer mechanisms and pathways associated with systemic lupus erythematosus (SLE) in human peripheral blood mononuclear cells. However, meta-analysis approaches with microarray data have not been well-explored in SLE. ## Methods In this study, a pathway-based meta-analysis was applied to four independent gene expression oligonucleotide microarray data sets to identify gene expression signatures for SLE, and these data sets were confirmed by a fifth independent data set. ## Results Differentially expressed genes (DEGs) were identified in each data set by comparing expression microarray data from control samples and SLE samples. Using Ingenuity Pathway Analysis software, pathways associated with the DEGs were identified in each of the four data sets. Using the leave one data set out pathway-based meta-analysis approach, a 37-gene metasignature was identified. This SLE metasignature clearly distinguished SLE patients from controls as observed by unsupervised learning methods. The final confirmation of the metasignature was achieved by applying the metasignatur

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Author

Dhivya Arasappan; Weida Tong; Padmaja Mummaneni; Hong Fang; Shashi Amur

Publisher

BioMed Central; Springer (Biomed Central Ltd.); London: BioMed Central, 2003-; Springer Science and Business Media LLC; Society for Mining, Metallurgy and Exploration Inc. (ISSN 1741-7015)

Published

2011

Language

EN

Field

Medicine (Health Sciences)