alexa Enhanced Meta-analysis Highlights Genes Involved in Metastasis from Several Microarray Datasets
ISSN: 0974-276X

Journal of Proteomics & Bioinformatics
Open Access

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Research Article

Enhanced Meta-analysis Highlights Genes Involved in Metastasis from Several Microarray Datasets

Michael Pierre1, Benoît DeHertogh1, Bertrand DeMeulder1, Eric Bareke1, Sophie Depiereux1, Carine Michiels2 and Eric Depiereux1*

1Molecular Biology Research Unit (URBM), University of Namur - FUNDP, Namur, Belgium

2Cell Biology Research Unit (URBC), NARILIS, University of Namur - FUNDP, Namur, Belgium

*Corresponding Author:
Dr. Eric Depiereux
Molecular Biology Research Unit (URBM)
University of Namur, 61 rue de Bruxelles
5000 Namur, Belgium
Tel: +32 81 72 44 15
Fax: +32 81 72 44 20
E-mail: [email protected]

Received Date: November 28, 2010; Accepted Date: February 14, 2011; Published Date: February 17, 2011

Citation: Pierre M, DeHertogh B, DeMeulder B, Bareke E, Depiereux S, et al. (2011) Enhanced Meta-analysis Highlights Genes Involved in Metastasis from Several Microarray Datasets. J Proteomics Bioinform 4: 036-043. doi: 10.4172/jpb.1000164

Copyright: © 2011 Pierre M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

 

Abstract

Metastasis is the final stage of cancer and is still associated with high mortality despite breakthroughs in recent years. Hypoxia at the center of the primary tumor is a major cause of metastasis. Here, we present a new metaanalysis- based methodology to pick out genes involved in one or two biological processes from several microarray datasets using a statistic that avoids the definition of an arbitrary threshold, providing statistically-significant results. Applied to metastasis and hypoxia datasets, this methodology was able to select genes already known to be involved in these phenomena as well as new candidates for further analyses. 165 genes of interest were selected, many of which were already known to be involved in cancer, metastasis and/ or hypoxia. Moreover, some could be classified into 42 pathways, including 12 cancer pathways and 5 proliferation and cell motility pathways. Negative tests performed with random genes failed to provide such results. In additional independent validations, expression profiles were generated for the 165 genes of interest from two other datasets with MDA-MB-231, MCF-7 and L3.6pl cells and the previous results were confirmed in most cases.

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