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(Arteriosclerosis, Thrombosis, and Vascular Biology. 2006;26:1226.)
© 2006 American Heart Association, Inc.

Brief Reviews

Genome-Wide Expression Studies of Atherosclerosis

Critical Issues in Methodology, Analysis, Interpretation of Transcriptomics Data

A.P.J.J. Bijnens; E. Lutgens; T. Ayoubi; J. Kuiper; A.J. Horrevoets; M.J.A.P. Daemen

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.

Correspondence to Ann-Pascale Bijnens, Department of Pathology, P. Debeyelaan 25, 6229 HX Maastricht, The Netherlands. E-mail ann-pascal.bijnens{at}path.unimaas.nl

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.

This review discusses some critical issues in the methodology, analysis, and interpretation of gene expression studies using vascular specimens from animals and humans. Our analysis demonstrates that future studies may benefit from recent developments in statistical and bioinformatical analysis methods to exploit the full potential of transcriptomics data.

Key Words: atherosclerosis • gene expression • genetically altered mice • pathology • vascular biology

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