4.6 Article

The peripheral blood mononuclear cell microRNA signature of coronary artery disease

期刊

出版社

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.bbrc.2010.03.075

关键词

Angina pectoris leukocytes; MicroRNA; Profiling; Coronary artery disease; Acute coronary syndromes

资金

  1. TIPharma [T2-110]
  2. Netherlands Heart Foundation [2008T070, 2003T201]

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Background MicroRNAs are being used in the oncology field to characterize tumors and predict the survival of cancer patients Here, we explored the potential of microRNAs as biomarkers for coronary artery disease (CAD) and acute coronary syndromes. Methods and results Using real-time PCR-based profiling, we determined the microRNA signature of peripheral blood mononuclear cells (PBMCs) from stable and unstable CAD patients and unaffected controls. 129 of 157 microRNAs measured were expressed by PBMCs and low variability between separate PBMC pools was observed The presence of CAD in general coincided with a marked 5-fold increase (P < 0 001) in the relative expression level of miR-135a, while the expression of miR-147 was 4-fold decreased (P < 0 05) in PBMCs from CAD patients as compared to controls, resulting in a 19-fold higher miR-135a/miR-147 ratio (P < 0 001) in CAD MicroRNA/target gene/biological function linkage analysis suggested that the change in PBMC microRNA signature in CAD patients is probably associated with a change in intracellular cadherin/Wnt signaling Interestingly, unstable angina pectoris patients could be discriminated from stable patients based upon their relatively high expression level of a cluster of three microRNAs including miR-198, and miR-370, suggesting that the microRNA signatures can be used to identify patients at risk for acute coronary syndromes Conclusions The present study is the first to show that microRNA signatures can possibly be utilized to identify patients exhibiting atherosclerotic CAD in general and those at risk for acute coronary syndromes. Our findings highlight the importance of microRNAs signatures as novel tool to predict clinical disease outcomes (C) 2010 Elsevier Inc All rights reserved

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