Abstract
BackgroundA 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.MethodsIn 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.ResultsDifferentially 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 metasignature to a fifth independent data set.ConclusionsThe novel pathway-based meta-analysis approach proved to be a useful technique for grouping disparate microarray data sets. This technique allowed for validated conclusions to be drawn across four different data sets and confirmed by an independent fifth data set. The metasignature and pathways identified by using this approach may serve as a source for identifying therapeutic targets for SLE and may possibly be used for diagnostic and monitoring purposes. Moreover, the meta-analysis approach provides a simple, intuitive solution for combining disparate microarray data sets to identify a strong metasignature.Please see Research Highlight: http://genomemedicine.com/content/3/5/30
Highlights
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
Biological pathways identified in SLE patients through the leave one data set out permutation method After applying the leave one data set out approach for each of the four scenarios (Table 2), commonly enriched biological pathways were identified using Ingenuity Pathway Analysis (IPA) software (Table 3)
The results suggest that the Differentially expressed genes (DEGs) signatures derived by using the leave one data set out permutation approach in the four scenarios (Table 2) can potentially identify a robust gene expression signature for SLE
Summary
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. Microarray technology has matured over the past 15 years and is employed for the study of gene expression signatures associated with disease [1,2,3]. Some of the challenges associated with identification of gene expression signatures that differentiate the disease state from healthy controls are the availability of samples, sample size, heterogeneous data sets, and reproducibility. Several studies with relatively small sample sizes are often used to identify gene expression signatures. In these circumstances, it is beneficial to combine the results of several individual studies using meta-analysis. It is beneficial to combine the results of several individual studies using meta-analysis This process enhances statistical power in identifying more robust and reliable gene signatures
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