Identification of dilated cardiomyopathy signature genes through gene expression and network data integration.
Dilated cardiomyopathy (DCM) is a leading cause of heart failure (HF) and cardiac transplantations in Western countries. Single-source gene expression analysis studies have identified potential disease biomarkers and drug targets. However, because of the diversity of experimental settings and relative lack of data, concerns have been raised about the robustness and reproducibility of the predictions. This study presents the identification of robust and reproducible DCM signature genes based on the integration of several independent data sets and functional network information. Gene expression profiles from three public data sets containing DCM and non-DCM samples were integrated and analyzed, which allowed the implementation of clinical diagnostic models. Differentially expressed genes were evaluated in the context of a global protein-protein interaction network, constructed as part of this study. Potential associations with HF were identified by searching the scientific literature. From these analyses, classification models were built and their effectiveness in differentiating between DCM and non-DCM samples was estimated. The main outcome was a set of integrated, potentially novel DCM signature genes, which may be used as reliable disease biomarkers. An empirical demonstration of the power of the integrative classification models against single-source models is also given.
Camargo
Anyela
A
School of Computing and Mathematics, University of Ulster at Jordanstown, Shore Road, Newtownabbey, County Antrim BT37 0QB, Northern Ireland, UK.
Azuaje
Francisco
F
D002311:Cardiomyopathy, Dilated / Q000235:genetics*; D030541:Databases, Genetic / Q000706:statistics & numerical data; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D053263:Gene Regulatory Networks; D006801:Humans; D008957:Models, Genetic; D013223:Statistics as Topic