Bioinformatics analysis of the regulatory lncRNA‑miRNA‑mRNA network and drug prediction in patients with hypertrophic cardiomyopathy

Abstract

Hypertrophic cardiomyopathy (HCM) is a complex inherited cardiovascular disease. The present study investigated the long noncoding (lnc)RNA/microRNA (mi)RNA/mRNA expression pattern of patients with HCM and aimed to identify key molecules involved in the development of this condition. An integrated strategy was conducted to identify differentially expressed miRNAs (DEmiRs), differentially expressed lncRNAs (DElncs) and differentially expressed genes (DEGs) based on the GSE36961 (mRNA), GSE36946 (miRNA), GSE68316 (lncRNA/mRNA) and GSE32453 (mRNA) expression profiles downloaded from the Gene Expression Omnibus datasets. Bioinformatics tools were employed to perform function and pathway enrichment analysis, protein-protein interaction, lncRNA-miRNA-mRNA and hub gene networks. Subsequently, DEGs were used as targets to predict drugs. The results indicated that a total of 2,234 DElncs (1,120 upregulated and 1,114 downregulated), 5 DEmiRs (2 upregulated and 3 downregulated) and 42 DEGs (35 upregulated and 7 downregulated) were identified in 4 microarray profiles. Gene ontology analysis revealed that DEGs were mainly involved in actin filament and stress fiber formation and in calcium ion binding, whereas Kyoto Encyclopedia of Genes and Genomes pathway analysis identified the hypoxia inducible factor-1, transforming growth factor-β and tumor necrosis factor signaling pathways as the main pathways involved in these processes. The hub genes were screened using cytoHubba. A total of 1,086 lncRNA-miRNA-mRNA interactions including 67 lncRNAs, 5 miRNAs and 25 mRNAs were mined in the present study based on prediction websites. Drug prediction indicated that the targeted drugs mainly included angiotensin converting enzyme inhibitors or β-blockers. A comprehensive bioinformatics analysis of the molecular regulatory lncRNA-miRNA-mRNA network was performed and potential therapeutic applications of drugs were predicted in HCM patients. The data may unravel the future molecular mechanism of HCM.

Individualized interactomes for network-based precision medicine in hypertrophic cardiomyopathy with implications for other clinical pathophenotypes

“…The endophenotype enrichment findings from patient networks in this study are consistent with reports implicating hypoxia- and redox-dependent post-transcriptional pathobiological mechanisms in HCM 31 , 32 . However, our data expand the number of endophenotypes previously unrecognized in HCM, particularly neoplastic- and DNA repair signaling, whereas refocusing importance on the role of classical HCM features that contribute to phenotypic heterogeneity.…”

Systemic Bioinformatic Analyses of Nuclear-Encoded Mitochondrial Genes in Hypertrophic Cardiomyopathy

“…With the development of genetic studies, high-throughput omics technologies (such as DNA microarrays and next-generation sequencing) that investigate gene function and expression at the genome-wide level have been widely used in basic research, clinical diagnosis, drug research and other fields. As a powerful technique, gene expression microarray-based bioinformatics analyses have also been widely used to identify HCM-related genes or noncoding RNAs, possible molecular mechanisms, and biological pathways ( Lim et al, 2001 ; Yang et al, 2015 ; Hu et al, 2019 ; Li et al, 2019 ; Liu et al, 2019 ). For example, microarray analysis was performed to explore the expression pattern of lncRNAs (long noncoding RNAs) and mRNAs (messenger RNAs) in HCM, which identified hundreds of differentially expressed lncRNAs and genes ( Yang et al, 2015 ).…”

Can miRNAs Be Considered as Diagnostic and Therapeutic Molecules in Ischemic Stroke Pathogenesis?—Current Status

“…A significant challenge for miRNA profiling as diagnostic makers in ischemic stroke is data interpretation [ 162 , 173 , 174 ]. There is a wide range of bioinformatics and machine learning tools that can be used to create predictive models to study long non-coding RNA (lncRNA) to miRNA to mRNA networks during IS [ 173 , 175 , 176 , 177 , 178 ]. The translation of scientific studies into everyday clinical practice is not trivial, and the broad applicability of NGS under routine conditions for miRNA profiling to understand the pathogenesis of IS has yet to be achieved.…”