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

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.

DOI: 10.4236/jct.2010.11006                

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 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.

Keywords

Gene Expression, Microarray, Enrichment, Meta-Analysis

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	D. R. Rhodes and A. M. Chinnaiyan, “Integrative analysis of the cancer transcriptome,” Nature Genetics, supplement Vol. 37, pp. S31–S37, 2005.
[2]	A. A. Alizadeh, et al., “Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling,” Nature, Vol. 403, No. 6769, pp. 503–511, 2000.
[3]	L. J. van 't Veer, et al., “Gene expression profiling predicts clinical outcome of breast cancer,” Nature, Vol. 415, No. 6871, pp. 530–536, 2002.
[4]	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.
[5]	P. Khatri and S. Draghici, “Ontological analysis of gene expression data: Current tools, limitations, and open problems,” Bioinformatics, Vol. 21, No. 18, pp. 3587– 3595, 2005.
[6]	J. J. Goeman, et al., “A global test for groups of genes: Testing association with a clinical outcome,” Bioinformatics, Vol. 20, No. 1, pp. 93–99, 2004.
[7]	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.
[8]	V. K. Mootha, et al., “PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately down regulated in human diabetes,” Nature Genetics, Vol. 34, No. 3, pp. 267–273, 2003.
[9]	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.
[10]	R. Barriot, D. J. Sherman, and I. Dutour, “How to decide which are the most pertinent overly-represented features during gene set enrichment analysis,” BMC Bioinformatics, Vol. 8, pp. 332, 2007.
[11]	G. S. Eichler, et al., “The LeFE algorithm: Embracing the complexity of gene expression in the interpretation of microarray data,” Genome Biology, Vol. 8, No. 9, pp. R187, 2007.
[12]	Z. Wei and H. Li, “Nonparametric pathway-based regression models for analysis of genomic data,” Biostatistics, Vol. 8, No. 2, pp. 265-284, 2007.
[13]	S. Draghici, et al., “A systems biology approach for pathway level analysis,” Genome Research, Vol. 17, No. 10, pp. 1537–1545, 2007.
[14]	D. R. Rhodes, et al., “Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles,” Neoplasia, Vol. 9, No. 2, pp. 166–180, 2007.
[15]	J. Quackenbush, “Computational analysis of microarray data,” Nature Reviews Genetics, Vol. 2, No. 6, pp. 418– 427, 2001.
[16]	D. Stekel, Microarray Bioinforamtics, Cambridge University Press, Cambridge, 2003.
[17]	Oncomine, Available from: http://www.oncomine.org. 2007.
[18]	M. A. Harris, et al., “The Gene Ontology database and informatics resource,” Nucleic Acids Research, Vol. 32 (Database issue), pp. D258–D261, 2004.
[19]	Y. Komada and M. Sakurai, “Shedding of CD9 antigen in acute lymphoblastic leukemia,” Leukemia and Lymphoma, Vol. 12, No. 5-6, pp. 365–372, 1994.
[20]	C. Ricci, F. Onida, and R. Ghidoni, “Sphingolipid players in the leukemia arena,” Biochimica et Biophysica Acta, Vol. 1758, No. 12, pp. 2121–2132, 2006.
[21]	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.
[22]	H. Kulbe, et al., “The chemokine network in cancer--much more than directing cell movement,” International Journal of Developmental Biology, Vol. 48, No. 5-6, pp. 489–496, 2004.
[23]	J. Meijer, et al., “The CXCR5 chemokine receptor is expressed by carcinoma cells and promotes growth of colon carcinoma in the liver,” Cancer Research, Vol. 66, No. 19, pp. 9576–9582, 2006.
[24]	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.
[25]	J. L. Lauer-Fields, D. Juska, and G. B. Fields, “Matrix metalloproteinases and collagen catabolism,” Biopolymers, Vol. 66, No. 1, pp. 19–32, 2002.
[26]	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.
[27]	Y. Tang, et al., “Role of Rho GTPases in breast cancer,” Frontiers in Bioscience, Vol. 13, pp. 759–776, 2008.
[28]	J. S. Ross, et al., “The HER-2 receptor and breast cancer: Ten years of targeted anti-HER-2 therapy and personalized medicine,” Oncologist, Vol. 14, No. 4, pp. 320–368, 2009.
[29]	M. L. Zhu and N. Kyprianou, “Androgen receptor and growth factor signaling cross-talk in prostate cancer cells,” Endocrine-Related Cancer, Vol. 15, No. 4, pp. 841 –849, 2008.
[30]	S. Varambally, et al., “Integrative genomic and proteomic analysis of prostate cancer reveals signatures of metastatic progression,” Cancer Cell, Vol. 8, No. 5, pp. 393– 406, 2005.
[31]	D. Hanahan and R. A. Weinberg, “The hallmarks of cancer,” Cell, Vol. 100, No. 1, pp. 57–70, 2000.
[32]	A. C. Pfeifer, J. Timmer and U. Klingmuller, “Systems biology of JAK/STAT signaling,” Essays in Biochemistry, Vol. 45, pp. 109–120, 2008.
[33]	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.
[34]	J. M. Argilés, et al., “Catabolic mediators as targets for cancer cachexia,” Drug Discovery Today, Vol. 8, No. 18, pp. 838–844. 2003.
[35]	S. Cheng and S. Balk, Steroid Hormone Receptor Signaling in Cancer.