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Title

 

 

 

 

 

Leaving out control groups: an internal contrast analysis of gene expression profiles in atrial fibrillation patients - A systems biology approach to clinical categorization

 

Authors

 

Kurt Vanhoutte1, 2, *, Carlo de Asmundis2, Anna Francesconi2, Jurgen Figys1, Griet Steurs1, Tim Boussy2, Markus Roos2, Andreas Mueller2, Lucio Massimo2, Gaetano Paparella2, Kristien Van Caelenberg2, Gian Battista Chierchia2, Andrea Sarkozy2, Pedro Brugada Y Terradellas2 and Martin Zizi1, 2

 

Affiliation

 

 

1Faculty of Medicine and Pharmacy, Dept of Physiology, Vrije Universiteit Brussel; 2Heart Rhythm Management Unit, Dept of Cardiology, UZ

 

Email

 

kurt.vanhoutte@vub.ac.be;* Corresponding author

 

Article Type

 

Prediction model

 

Date

 

received September 05, 2008; accepted September 14, 2008; published January 12, 2009

 

Abstract

Atrial fibrillation (AF) is a frequent chronic dysrythmia with an incidence that increases with age (>40). Because of its medical and socio-economic impacts it is expected to become an increasing burden on most health care systems. AF is a multi-factorial disease for which the identification of subtypes is warranted. Novel approaches based on the broad concepts of systems biology may overcome the blurred notion of normal and pathological phenotype, which is inherent to high throughput molecular arrays analysis. Here we apply an internal contrast algorithm on AF patient data with an analytical focus on potential entry pathways into the disease. We used a RMA (Robust Multichip Average) normalized Affymetrix micro-array data set from 10 AF patients (geo_accession #GSE2240). Four series of probes were selected based on physiopathogenic links with AF entryways: apoptosis (remodeling), MAP kinase (cell remodeling), OXPHOS (ability to sustain hemodynamic workload) and glycolysis (ischemia). Annotated probe lists were polled with Bioconductor packages in R (version 2.7.1). Genetic profile contrasts were analysed with hierarchical clustering and principal component analysis. The analysis revealed distinct patient groups for all probe sets. A substantial part (54% till 67%) of the variance is explained in the first 2 principal components. Genes in PC1/2 with high discriminatory value were selected and analyzed in detail. We aim for reliable molecular stratification of AF. We show that stratification is possible based on physiologically relevant gene sets. Genes with high contrast value are likely to give pathophysiological insight into permanent AF subtypes.

 

Keywords

atrial fibrillation; gene expression; Robust Multichip Average; genetic profile

 

Citation

Vanhoutte, Bioinformation 3(6): 275-278 (2009)

Edited by

P. Kangueane

 

ISSN

0973-2063

 

Publisher

Biomedical Informatics

 

License

 

 

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.

 

Leaving out control groups: an internal contrast analysis of gene expression profiles in atrial fibrillation patients - A systems biology approach to clinical categorization Leaving out control groups: an internal contrast analysis of gene expression profiles in atrial fibrillation patients - A systems biology approach to clinical categorization