Sum-Based Meta-Analytical Enrichment of Gene Expression Data to Identify Pathway Signatures of Cancers

Abstract

A new method for analysis of microarray gene expression experiments referred to as Sum-based Meta-analytical Enrichment(SME) is proposed in this manuscript. SME is a combined enrichment and meta-analytical approach to inferon the association of gene sets with particular phenotypes. SME allows enrichment to be performed across datasets,which to our knowledge was not earlier possible. As a proof of concept study, this technique is applied to datasets fromOncomine, a publicly available cancer microarray database. The genes that are significantly up-/down-regulated(p-value ≤ 10-4) in various cancer types in Oncomine were listed. These genes were assigned to biological processesusing GO annotations. The SME algorithm was applied to identify a list of GO processes most deregulated in 4 majorcancer types. For validation we examined whether the processes predicted by SME were already documented in literature.SME method identified several known processes for the 4 cancer types and identified several novel processeswhich are biologically plausible. Nearly all the pathways identified by SME as common to the 4 cancers were found tocontribute to processes which are widely regarded as cancer hallmarks. SME provides an intuitive yet objective ‘process-centric’ interpretation of the ‘gene-centric’ output of individual microarray comparison studies. The methods describedhere should be applicable in the next-generation sequencing based gene expression analysis as well