TITLE:
Sum-Based Meta-Analytical Enrichment of Gene Expression Data to Identify Pathway Signatures of Cancers
AUTHORS:
Kavishwar Wagholikar, Prasanna Venkatraman, Sundararajan Vijayraghavan, Chandan Kumar-Sinha
KEYWORDS:
Gene Expression, Microarray, Enrichment, Meta-Analysis
JOURNAL NAME:
Journal of Cancer Therapy,
Vol.1 No.1,
March
25,
2010
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.