Open AccessMolecular risk stratification in advanced heart failure patients
Author(s) -
Lamirault Guillaume,
Meur Nolwenn Le,
Roussel JeanChristian,
Cunff MarieFrance Le,
Baron Daniel,
Bihouée Audrey,
Guisle Isabelle,
Raharijaona Mahatsangy,
Ramstein Gérard,
Teusan Raluca,
Chevalier Catherine,
Gueffet JeanPierre,
Trochu JeanNoël,
Léger Jean J.,
Houlgatte Rémi,
Steenman Marja
Publication year - 2010
Publication title -
journal of cellular and molecular medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.44
H-Index - 130
eISSN - 1582-4934
pISSN - 1582-1838
DOI - 10.1111/j.1582-4934.2009.00913.x
Subject(s) - medicine , heart failure , heart transplantation , gene expression profiling , risk stratification , ventricle , cardiology , transplantation , gene expression , gene , biology , genetics
Risk stratification in advanced heart failure (HF) is crucial for the individualization of therapeutic strategy, in particular for heart transplantation and ventricular assist device implantation. We tested the hypothesis that cardiac gene expression profiling can distinguish between HF patients with different disease severity. We obtained tissue samples from both left (LV) and right (RV) ventricle of explanted hearts of 44 patients undergoing cardiac transplantation or ventricular assist device placement. Gene expression profiles were obtained using an in‐house microarray containing 4217 muscular organ‐relevant genes. Based on their clinical status, patients were classified into three HF‐severity groups: deteriorating ( n = 12), intermediate ( n = 19) and stable ( n = 13). Two‐class statistical analysis of gene expression profiles of deteriorating and stable patients identified a 170‐gene and a 129‐gene predictor for LV and RV samples, respectively. The LV molecular predictor identified patients with stable and deteriorating status with a sensitivity of 88% and 92%, and a specificity of 100% and 96%, respectively. The RV molecular predictor identified patients with stable and deteriorating status with a sensitivity of 100% and 96%, and a specificity of 100% and 100%, respectively. The molecular prediction was reproducible across biological replicates in LV and RV samples. Gene expression profiling has the potential to reproducibly detect HF patients with highest HF severity with high sensitivity and specificity. In addition, not only LV but also RV samples could be used for molecular risk stratification with similar predictive power.