Multiple pathways are responsible for transducing mechanical and hormonal stimuli into changes in gene expression during heart failure. In this study our goals were (i) to develop a sound statistical method to establish a comprehensive cutoff point for identification of differentially expressed genes, (ii) to identify a gene expression fingerprint for heart failure, (iii) to attempt to distinguish different etiologies of heart failure by their gene expression fingerprint, and (iv) to identify gene clusters that show coordinated up- or down-regulation in human heart failure. We used oligonucleotide microarrays to profile seven nonfailing (NF) and eight failing (F) human hearts with a diagnosis of end-stage dilated cardiomyopathy. Biological and experimental variability of the hybridization data were analyzed, and then a statistical analysis procedure was developed, including Student's t test after log-transformation and Wilcoxon Mann-Whitney test. A comprehensive cutoff point composed of fold change, average difference, and absolute call was then established and validated by TaqMan PCR. Of 6,606 genes on the GeneChip, 103 genes in 10 functional groups were differentially expressed between F and NF hearts. A dendrogram identified a gene expression fingerprint of F and NF hearts and also distinguished two F hearts with distinct etiologies (familial and alcoholic cardiomyopathy, respectively) with different expression patterns. K means clustering also revealed two potentially novel pathways associated with up-regulation of atrial natriuretic factor and brain natriuretic peptide and with increased expression of extracellular matrix proteins. Gene expression fingerprints may be useful indicators of heart failure etiologies.