Identification of cardiomyopathy-related core genes through human metabolic networks and expression data

Abstract

Background Cardiomyopathy is a complex type of myocardial disease, and its incidence has increased significantly in recent years. Dilated cardiomyopathy (DCM) and ischemic cardiomyopathy (ICM) are two common and indistinguishable types of cardiomyopathy. Results Here, a systematic multi-omics integration approach was proposed to identify cardiomyopathy-related core genes that could distinguish normal, DCM and ICM samples using cardiomyopathy expression profile data based on a human metabolic network. First, according to the differentially expressed genes between different states (DCM/ICM and normal, or DCM and ICM) of samples, three sets of initial modules were obtained from the human metabolic network. Two permutation tests were used to evaluate the significance of the Pearson correlation coefficient difference score of the initial modules, and three candidate modules were screened out. Then, a cardiomyopathy risk module that was significantly related to DCM and ICM was determined according to the significance of the module score based on Markov random field. Finally, based on the shortest path between cardiomyopathy known genes, 13 core genes related to cardiomyopathy were identified. These core genes were enriched in pathways and functions significantly related to cardiomyopathy and could distinguish between samples of different states. Conclusion The identified core genes might serve as potential biomarkers of cardiomyopathy. This research will contribute to identifying potential biomarkers of cardiomyopathy and to distinguishing different types of cardiomyopathy.

A circular network of purine metabolism as coregulators of dilated cardiomyopathy

“…Identification of novel biomarkers can predict disease progression and guide individualized treatment [18]. Various physiological reactions catalyzed by gene encoding enzymes and their interaction systems can be reflected through metabolic networks [19]. In the present study, an integrated analysis strategy based on non-targeted metabolomics and transcriptomics was used to analyze the metabolic profile of patients with heart failure, and patients without heart failure were included as a control group.…”