TY - JOUR
T1 - Transcriptional networks characterize ventricular dysfunction after myocardial infarction
T2 - A proof-of-concept investigation
AU - Azuaje, Francisco
AU - Devaux, Yvan
AU - Vausort, Melanie
AU - Yvorra, Céline
AU - Wagner, Daniel R.
N1 - Funding Information:
We thank Céline Jeanty, Bernadette Leners, Malou Gloesener and Loredana Jacobs for expert technical assistance. Funding: This work was supported by grants from the Société pour la Recherche sur les Maladies Cardiovasculaires ; Ministère de la Culture, de l’Enseignement Supérieur et de la Recherche , and Fonds National de la Recherche of Luxembourg.
PY - 2010/10
Y1 - 2010/10
N2 - There is currently no method powerful enough to identify patients at risk of developing ventricular dysfunction after myocardial infarction (MI). We aimed to identify major mechanisms related to ventricular dysfunction to predict outcome after MI. Based on the combination of domain knowledge, protein-protein interaction networks and gene expression data, a set of potential biomarkers of ventricular dysfunction after MI was identified. Here we propose a new strategy for the prediction of ventricular dysfunction after MI based on " network activity indices" (NAI), which encode gene network-based signatures and distinguishes between prognostic classes. These models outperformed prognostic models based on standard differential expression analysis. NAI-based models reported high classification accuracy, with a maximum area under the receiver operating characteristic curve (AUC) of 0.75. Furthermore, the classification capacity of these models was validated by performing evaluations on an independent patient cohort (maximum AUC=0.75). These results suggest that transcriptional network-based biosignatures can offer both powerful and biologically-meaningful prediction models of ventricular dysfunction after MI. This research reports a new integrative strategy for identifying transcriptional responses that characterize cardiac repair and for predicting clinical outcome after MI. It can be adapted to other clinical domains, such as those constrained by small molecular datasets and limited translational knowledge. Furthermore, it may reflect clinically-meaningful synergistic effects that cannot be identified by standard analyses.
AB - There is currently no method powerful enough to identify patients at risk of developing ventricular dysfunction after myocardial infarction (MI). We aimed to identify major mechanisms related to ventricular dysfunction to predict outcome after MI. Based on the combination of domain knowledge, protein-protein interaction networks and gene expression data, a set of potential biomarkers of ventricular dysfunction after MI was identified. Here we propose a new strategy for the prediction of ventricular dysfunction after MI based on " network activity indices" (NAI), which encode gene network-based signatures and distinguishes between prognostic classes. These models outperformed prognostic models based on standard differential expression analysis. NAI-based models reported high classification accuracy, with a maximum area under the receiver operating characteristic curve (AUC) of 0.75. Furthermore, the classification capacity of these models was validated by performing evaluations on an independent patient cohort (maximum AUC=0.75). These results suggest that transcriptional network-based biosignatures can offer both powerful and biologically-meaningful prediction models of ventricular dysfunction after MI. This research reports a new integrative strategy for identifying transcriptional responses that characterize cardiac repair and for predicting clinical outcome after MI. It can be adapted to other clinical domains, such as those constrained by small molecular datasets and limited translational knowledge. Furthermore, it may reflect clinically-meaningful synergistic effects that cannot be identified by standard analyses.
KW - Angiogenesis
KW - Cardiovascular diseases
KW - Medical decision-support systems
KW - Myocardial infarction
KW - Systems medicine
KW - Translational bioinformatics
KW - Ventricular dysfunction
UR - http://www.scopus.com/inward/record.url?scp=77956262458&partnerID=8YFLogxK
U2 - 10.1016/j.jbi.2010.05.012
DO - 10.1016/j.jbi.2010.05.012
M3 - Article
AN - SCOPUS:77956262458
SN - 1532-0464
VL - 43
SP - 812
EP - 819
JO - Journal of Biomedical Informatics
JF - Journal of Biomedical Informatics
IS - 5
ER -