Comprehensive analysis of miRNA–mRNA regulatory network and potential drugs in chronic chagasic cardiomyopathy across human and mouse

Abstract

Background Chronic chagasic cardiomyopathy (CCC) is the leading cause of heart failure in Latin America and often causes severe inflammation and fibrosis in the heart. Studies on myocardial function and its molecular mechanisms in patients with Chronic chagasic cardiomyopathy are very limited. In order to understand the development and progression of Chronic chagasic cardiomyopathy and find targets for its diagnosis and treatment, the field needs to better understand the exact molecular mechanisms involved in these processes. Methods The mRNA microarray datasets GSE84796 (human) and GSE24088 (mouse) were obtained from the Gene Expression Omnibus (GEO) database. Homologous genes between the two species were identified using the online database mining tool Biomart, followed by differential expression analysis, gene enrichment analysis and protein–protein interaction (PPI) network construction. Cytohubba plug-in of Cytoscape software was used to identify Hub gene, and miRNet was used to construct the corresponding miRNA–mRNA regulatory network. miRNA-related databases: miRDB, Targetscan and miRWalk were used to further evaluate miRNAs in the miRNA–mRNA network. Furthermore, Comparative Toxicogenomics Database (CTD) and L1000 Platform were used to identify hub gene-related drugs. Results A total of 86 homologous genes were significantly differentially expressed in the two datasets, including 73 genes with high expression and 13 genes with low expression. These differentially expressed genes were mainly enriched in the terms of innate immune response, signal transduction, protein binding, Natural killer cell mediated cytotoxicity, Tuberculosis, Chemokine signaling pathway, Chagas disease and PI3K−Akt signaling pathway. The top 10 hub genes LAPTM5, LCP1, HCLS1, CORO1A, CD48, TYROBP, RAC2, ARHGDIB, FERMT3 and NCF4 were identified from the PPI network. A total of 122 miRNAs were identified to target these hub genes and 30 of them regulated two or more hub genes at the same time. miRDB, Targetscan and miRWalk were further analyzed and screened out hsa-miR-34c-5p, hsa-miR-34a-5p and hsa-miR-16-5p as miRNAs regulating these hub genes. Finally, Progesterone, Flutamide, Nimesulide, Methotrexate and Temozolomide were identified to target these hub genes and might be targeted therapies for Chronic chagasic cardiomyopathy. Conclusions In this study, the potential genes associated with Chronic chagasic cardiomyopathy are identified and a miRNA–mRNA regulatory network is constructed. This study explores the molecular mechanisms of Chronic chagasic cardiomyopathy and provides important clues for finding new therapeutic targets.

Expression pattern and diagnostic value of ferroptosis-related genes in acute myocardial infarction

“…This obtained PPI network was further visualized using Cytoscape software (version 3.7.1). The Cytohubba plug-in of Cytoscape software was used to screen out the top 10 key genes with high connectivity in this PPI network using the Maximal Clique Centrality (MCC) algorithm ( 18 , 19 ). Multiple algorithms (Degree, EPC, Betweenness) continued to be used for prediction, and genes predicted by more than three methods at the same time are considered to be Hub Genes for AMI.…”

The Dual Function of RhoGDI2 in Immunity and Cancer

“…Using mRNA microarray analysis, RhoGDI2 was found to be differentially expressed in chronic chagasic cardiomyopathy (CCC), a myocardial disease characterized by severe cardiac inflammation leading to heart failure. RhoGDI2 was identified as one of the top genes, along with Rac2, from a module that is enriched in the pathway of natural killer (NK) cell-mediated cytotoxicity [ 67 ]. This suggests that RhoGDI2 may be involved in CCC pathogenesis via NK cell-mediated cytotoxicity, possibly via Rac2 regulation [ 68 ].…”

Comprehensive Analysis of circRNA-miRNA-mRNA Regulatory Network and Novel Potential Biomarkers in Acute Myocardial Infarction

“…Based on DEcircRNAs and CoDEmiRNAs, the circRNA-miRNA interactions were predicted using website tools of circBank 7 and circInteractome 8 (23,24). Based on CoDEmiRNAs and differentially expressed genes (DEGs), the miRNA-mRNA interactions were predicted using website tools of miRDB, 9 Targetscan (version7.2 10 ), and miRWalk 11 (25)(26)(27)(28). According to these regulatory relationships, the up-regulated circRNAs and down-regulated circRNAs were used to construct a circRNA-miRNA-mRNA regulatory network respectively.…”