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.
 D. R. Rhodes, et al., “Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression,” Proceedings of the National Academy of Science, U S A, Vol. 101, No. 25, pp. 9309–9314, 2004.
 P. Pavlidis, et al., “Using the gene ontology for microarray data mining: a comparison of methods and application to age effects in human prefrontal cortex,” Neurochemical Research, Vol. 29, No. 6, pp. 1213–1222, 2004.
 A. Subramanian, et al., “From the Cover: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles,” Proceedings of the National Academy of Science, U S A, Vol. 102, No. 43, pp. 15545–15550, 2005.
 Oncomine, Available from: http://www.oncomine.org. 2007.
 R. Huang, A. Wallqvist, and D. G. Covell, “Targeting changes in cancer: Assessing pathway stability by comparing pathway gene expression coherence levels in tumor and normal tissues,” Molecular Cancer Therapeutics, Vol. 5, No. 9, pp. 2417–2427, 2006.
 G. Opdenakker and J. Van Damme, “The countercurrent principle in invasion and metastasis of cancer cells. Recent insights on the roles of chemokines,” International Journal of Developmental Biology, Vol. 48, No. 5-6, pp. 519–527, 2004.
 A. E. Kossakowska, S. J. Urbanski, and A. Janowska-Wieczorek, “Matrix metalloproteinases and their tissue inhibitors-expression, role and regulation in human malignant non-Hodgkin's lymphomas,” Leukemia and Lymphoma, Vol. 39, No. 5–6, pp. 485–493, 2000.
 J. T. Durham and I. M. Herman, “Systems biology of JAK/STAT signalling: Inhibition of angiogenesis in vitro: a central role for beta-actin dependent cytoskeletal remodeling,” Microvascular Research, Vol. 45, No. 3, pp. 109–120, 2008.