Meta-analysis of gene expression data in adipose tissue reveals new obesity associated genes.

Abstract

High-throughput transcriptomic and proteomic data like microarray data are deposited in public databases such as Gene Expression Omnibus (GEO). Omics data integration and processing from different and independent studies is achieved by using efficient and effective computational tools through meta-analysis. Meta-analysis is a statistical powerful tool combining data from numerous studies, minimizes bias and increases statistical power by increasing sample size compared to individual studies. Therefore, we performed a meta-analysis of gene expression data in adipose tissue to identify genes that are differentially expressed between obese and non-obese subjects as well as to detect gene expression signatures, pathways and networks associated with obesity. We identified 821 differentially expressed genes (DEGs) in adipose tissue of obese subjects compared to non-obese. A protein-protein interactions (PPIs) network was reconstructed consisting of 168 proteins. Functional enrichment analysis in the network revealed proteins involved in RNA and energy metabolism. The KEGG pathway analysis revealed 15 enriched pathway terms. Furthermore, multiple testing correction methods identified five statistically significant obesity associated genes (NDUFA12, SFI1, SSB, FAR2 and LACE1) that require further investigation.