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1 Medicine, Division of Cardiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
2 Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
3 Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
4 Surgery, Cardiothoracic Division, Johns Hopkins University School of Medicine, Baltimore, MD, USA
* To whom correspondence should be addressed. E-mail: jhare{at}mail.jhmi.edu.
Cardiomyopathy can be initiated by many factors, but the pathways from unique inciting mechanisms to the common endpoint of ventricular dilation and reduced cardiac output are unclear. We previously described a microarray-based prediction algorithm differentiating nonischemic (NICM) from ischemic (ICM) cardiomyopathy using nearest shrunken centroids. Accordingly, we tested the hypothesis that NICM and ICM would have both shared and distinct differentially expressed genes relative to normal hearts and compared gene expression of 21 NICM and 10 ICM cardiomyopathy samples with that of 6 nonfailing (NF) hearts using Affymetrix U133A GeneChips and Significance Analysis of Microarrays. Compared to NF, 257 genes were differentially expressed in NICM and 72 genes in ICM. Only 41 genes were shared between the two comparisons, mainly involved in cell growth and signal transduction. Those uniquely expressed in NICM were frequently involved in metabolism, and those in ICM more often had catalytic activity. Novel genes included angiotensin-converting enzyme 2 (ACE2), which was upregulated in NICM but not ICM, suggesting that ACE2 may offer differential therapeutic efficacy in NICM and ICM. In addition, a tumor necrosis factor (TNF) receptor was downregulated in both NICM and ICM, demonstrating the different signaling pathways involved in heart failure pathophysiology. These results offer novel insight into unique disease-specific gene expression that exists between end-stage cardiomyopathy of different etiologies. This analysis demonstrates that transcriptome analysis offers insight into pathogenesis-based therapies in heart failure management, and complements studies using expression-based profiling to diagnose heart failure of different etiologies.
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