Author(s)

Kavishwar Wagholikar, Prasanna Venkatraman, Sundararajan Vijayraghavan, Chandan Kumar-Sinha

Advanced Centre for Treatment, Research and Education in Cancer, Navi Mumbai, India.
Interdisciplinary School of Scientific Computing, University of Pune, Pune, India.
Michigan Center for Translational Pathology, Department of Pathology, University of Michigan Ann Arbor, MI, USA.

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 infer on 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 from Oncomine, 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 processes using GO annotations. The SME algorithm was applied to identify a list of GO processes most deregulated in 4 major cancer 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 processes which are biologically plausible. Nearly all the pathways identified by SME as common to the 4 cancers were found to contribute 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 described here should be applicable in the next-generation sequencing based gene expression analysis as well.

Gene Expression, Microarray, Enrichment, Meta-Analysis

K. Wagholikar, P. Venkatraman, S. Vijayraghavan and C. Kumar-Sinha, "Sum-Based Meta-Analytical Enrichment of Gene Expression Data to Identify Pathway Signatures of Cancers," Journal of Cancer Therapy, Vol. 1 No. 1, 2010, pp. 36-42. doi: 10.4236/jct.2010.11006.