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

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.