Gene expression analysis of ischemic and nonischemic cardiomyopathy: Shared and distinct genes in the development of heart failure

Michelle M. Kittleson, Khalid M. Minhas, Rafael A. Irizarry, Shui Q. Ye, Gina Edness, Elayne Breton, John V. Conte, Gordon Tomaselli, Joe G.N. Garcia, Joshua M. Hare

Research output: Contribution to journalArticlepeer-review

150 Scopus citations


Cardiomyopathy can be initiated by many factors, but the pathways from unique inciting mechanisms to the common end point of ventricular dilation and reduced cardiac output are unclear. We previously described a microarray-based prediction algorithm differentiating nonischemic (NICM) from ischemic cardiomyopathy (ICM) 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 samples with that of 6 nonfailing (NF) hearts using Affymetrix U133A GeneChips and significance analysis of microarrays. Compared with 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 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.

Original languageEnglish (US)
Pages (from-to)299-307
Number of pages9
JournalPhysiological genomics
StatePublished - Jul 2005

All Science Journal Classification (ASJC) codes

  • Physiology
  • Genetics


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