Identification And Functional Analysis Of Biomarkers In Patients With Hypertrophic Cardiomyopathy Based On Multiple Molecular Levels Data

Hypertrophic cardiomyopathy (HCM) is a heritable heart disease characterized by asymmetric cardiac hypertrophy and without ventricular enlargement (usually left ventricle) . Severe HCM patients may be complicated by ventricular arrhythmia, atrial fibrillation, heart failure and sudden cardiac death. So far, the pathogenic mechanism of HCM has not been clear. Therefore, revealing the pathogenic mechanism of HCM and identifing its critical biomarkers have important scientific and clinical significance.With the development of the theory and technology of molecular biology, the molecular mechanism of HCM has made great progress and achieved certain results at home and abroad. However, the existing studies are relatively simple, do not systematically study its pathogenic mechanism from a molecular interaction network level and the identified biomarkers are also relatively limited. In this paper, we use graph theory, complex network theory and bioinformatics methods and means to construct a three element hybrid network with miRNA-mRNA-lncRNA associated with HCM. By analyzing the topological features and parameters of the network, we identified the potential critical biomarkers of HCM and analyzed their biological functions. The main research contents of this paper are as follows.(1) Construction of a miRNA-mRNA-lncRNA hybrid network associated with HCM.First of all, we clear up lncRNA, mRNA and miRNA expression spectrum data,including the removal of null values and repeating values, etc. Then, we screen out the differentially expressed lncRNAs, mRNAs and miRNAs by using the T-test and fold change. Besides, we designed the method to construct miRNA-mRNA-lncRNA hybrid network using differentially expressed lncRNA, mRNA and miRNA lncRNA.Finally, we analyzed the biological properties of the hybrid network.(2) Based on the characteristics of miRNA-mRNA-lncRNA hybrid network, we screened the important biomarkers related to HCM. The hubs of the network are selected according to the centrality measure, degree, betweenness centrality and closeness centrality.(3) We use GO, KEGG, GeneCards and other bioinformatics tools to analyze the function of critical lncRNAs, mRNAs and miRNAs and to explain the pathogenesis of HCM.