Abstract
BackgroundThis study aims to expand knowledge of the complex process of myocardial infarction (MI) through the application of a systems-based approach.MethodsWe generated a gene co-expression network from microarray data originating from a mouse model of MI. We characterized it on the basis of connectivity patterns and independent biological information. The potential clinical novelty and relevance of top predictions were assessed in the context of disease classification models. Models were validated using independent gene expression data from mouse and human samples.ResultsThe gene co-expression network consisted of 178 genes and 7298 associations. The network was dissected into statistically and biologically meaningful communities of highly interconnected and co-expressed genes. Among the most significant communities, one was distinctly associated with molecular events underlying heart repair after MI (P < 0.05). Col5a2, a gene previously not specifically linked to MI response but responsible for the classic type of Ehlers-Danlos syndrome, was found to have many and strong co-expression associations within this community (11 connections with ρ > 0.85). To validate the potential clinical application of this discovery, we tested its disease discriminatory capacity on independently generated MI datasets from mice and humans. High classification accuracy and concordance was achieved across these evaluations with areas under the receiving operating characteristic curve above 0.8.ConclusionNetwork-based approaches can enable the discovery of clinically-interesting predictive insights that are accurate and robust. Col5a2 shows predictive potential in MI, and in principle may represent a novel candidate marker for the identification and treatment of ischemic cardiovascular disease.
Highlights
This study aims to expand knowledge of the complex process of myocardial infarction (MI) through the application of a systems-based approach
Datasets The co-expression network in MI was derived from a microarray dataset consisting of 36 MI and 23 control cardiac tissue samples published in Tarnavski et al [10] (GEO accession code: GDS488)
As further illustrated by basic network topology parameters, genes are in relatively close proximity to each other and are tightly grouped
Summary
This study aims to expand knowledge of the complex process of myocardial infarction (MI) through the application of a systems-based approach. MI is underpinned by complex, intertwined biological processes [1]. These processes operate in the context of large, intricate biological interaction networks. Despite over 60,000 reports on MI [2,3], there is still a pressing need to better define the disease biology of this condition based on integrative, systematic approaches. Systematic network-based approaches can bridge the gap between our knowledge of the functional roles of molecular entities, disease phenotypes and new clinical applications [4,5]. We and others have shown that such an approach may generate new targets and markers for MI, which may become clinically useful [6,7,8,9]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
