on March 30, 2006
Published online before print March 30, 2006, doi: 10.1161/01.ATV.0000219289.06529.f1
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Submitted on July 4, 2005
Accepted on February 23, 2006
Genome-Wide Expression Studies of Atherosclerosis. Critical Issues in Methodology, Analysis, and Interpretation of Transcriptomics Data
A. P.J.J. Bijnens *;
From the Department of Pathology (A.P.J.J.B., E.L., M.J.A.P.D.), Cardiovascular Research Institute Maastricht (CARIM), University of Maastricht; the Department of Population Genetics (T.A.), Cardiovascular Research Institute Maastricht (CARIM), University of Maastricht; the Division of Biopharmaceutics (J.K.), Leiden/Amsterdam Center for Drug Research, Gorlaeus Laboratories, Leiden; and the Department of Medical Biochemistry (A.J.H.), Academic Medical Center, University of Amsterdam, The Netherlands.
* To whom correspondence should be addressed. E-mail: ann-pascal.bijnens{at}path.unimaas.nl.
Abstract--During the past 6 years, gene expression profiling of atherosclerosis has been used to identify genes and pathways relevant in vascular (patho)physiology. This review discusses some critical issues in the methodology, analysis, and interpretation of the data of gene expression studies that have made use of vascular specimens from animal models and humans. Analysis of gene expression studies has evolved toward the genome-wide expression profiling of large series of individual samples of well-characterized donors. Despite the advances in statistical and bioinformatical analysis of expression data sets, studies have not yet fully exploited the potential of gene expression data sets to obtain novel insights into the molecular mechanisms underlying atherosclerosis. To assess the potential of published expression data, we compared the data of a CC chemokine gene cluster between 18 murine and human gene expression profiling articles. Our analysis revealed that an adequate comparison is mainly hindered by the incompleteness of available data sets. The challenge for future vascular genomic profiling studies will be to further improve the experimental design, statistical, and bioinformatical analysis and to make data sets freely accessible.
Key words: atherosclerosis • gene expression • genetically altered mice • pathology • vascular biology
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