Identification of key genes for hypertrophic cardiomyopathy using integrated network analysis of differential lncRNA and gene expression.
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ABSTRACT:
SUBMITTER: Cao J
PROVIDER: S-EPMC9386162 | biostudies-literature | 2022
REPOSITORIES: biostudies-literature
Publications
Identification of key genes for hypertrophic cardiomyopathy using integrated network analysis of differential lncRNA and gene expression.
Frontiers in cardiovascular medicine 20220804
<h4>Objective</h4>Hypertrophic cardiomyopathy (HCM) is a complex heterogeneous heart disease. Recent reports found that long non-coding RNAs (lncRNAs) play an important role in the progression of cardiovascular diseases. The present study aimed to identify the novel lncRNAs and messenger RNAs (mRNAs) and determine the key pathways involved in HCM.<h4>Methods</h4>The lncRNA and mRNA sequencing datasets of GSE68316 and GSE130036 were downloaded from the Gene Expression Omnibus (GEO) database. An i ...[more]
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Project description:BackgroundHypertrophic cardiomyopathy (HCM), an autosomal dominant genetic disease, is the main cause of sudden death in adolescents and athletes globally. Hypoxia and immune factors have been revealed to be related to the pathology of HCM. There is growing evidence of a role for hypoxia and inflammation as triggers and enhancers in the pathology in HCM. However, the role of hypoxia- and immune-related genes in HCM have not been reported.MethodsFirstly, we obtained four HCM-related datasets from the Gene Expression Omnibus (GEO) database for differential expression analysis. Immune cells significantly expressed in normal samples and HCM were then screened by a microenvironmental cell population counter (MCP-counter) algorithm. Next, hypoxia- and immune-related genes were screened by the LASSO + support vector machine recursive feature elimination (SVM-RFE) and weighted gene co-expression network analysis (WGCNA). Single-gene enrichment analysis and expression validation of key genes were then performed. Finally, we constructed a competing endogenous RNA (ceRNA) network of key genes.ResultsIn this study, 35 differentially expressed hypoxia genes were found. By using LASSO + SVM-RFE analysis, 10 more targets with differentially expressed hypoxia genes were identified. The MCP-count algorithm yielded five differentially expressed immune cells, and after assessing them for WGCNA characteristics, 612 immune genes were discovered. When hypoxia and immune genes were combined for cross-tabulation analysis, three hypoxia- and immune-related genes (ATP2A2, DDAH1, and OMA1) were identified.ConclusionBased on hypoxia characteristic genes, three key genes were identified. These were also significantly related to immune activation, which proves a theoretical basis and reference value for studying the relationship between HCM and hypoxia and immunity.
| S-EPMC10410988 | biostudies-literature
Project description:BackgroundHypertrophic cardiomyopathy (HCM) is a group of heterogeneous diseases that affects the myocardium. It is also a common familial disease. The symptoms are not common and easy to find.ObjectiveIn this paper, we aim to explore and analyze the dysfunctional gene network related to hypertrophic cardiomyopathy, and the key target genes with diagnostic and therapeutic significance for HCM were screened.MethodsThe gene expression profiles of 37 samples (GSE130036) were downloaded from the GEO database. Differential analysis was used to identify the related dysregulated genes in patients with HCM. Enrichment analysis identified the biological function and signaling pathway of these differentially expressed genes. Then, PPI network was built and verified in the GSE36961 dataset. Finally, the gene of single-nucleotide variants (SNVs) in HCM samples was screened by means of maftools.ResultsIn this study, 920 differentially expressed genes were obtained, and these genes were mainly related to metabolism-related signaling pathways. 187 interacting genes were identified by PPI network analysis, and the expression trends of C1QB, F13A1, CD163, FCN3, PLA2G2A, and CHRDL2 were verified by another dataset and quantitative real-time polymerase chain reaction. ROC curve analysis showed that they had certain clinical diagnostic ability, and they were the potential key dysfunctional genes of HCM. In addition, we found that PRMT5 mutation was the most frequent in HCM samples, which may affect the pathogenesis of HCM.ConclusionTherefore, the key genes and enrichment results identified by our analysis may provide a reference for the occurrence and development mechanism of HCM. In addition, mutations in PRMT5 may be a useful therapeutic and diagnostic target for HCM. Our results also provide an independent quantitative assessment of functional limitations in patients with unknown history.
| S-EPMC8816546 | biostudies-literature
Project description:BackgroundConsidering it is one of the major causes of sudden cardiac arrest, the proper management of hypertrophic cardiomyopathy (HCM) is essential. However, efficient treatment options for this disease are still lacking. The discovery of HCM-associated hub genes may help in diagnosis and offer a reliable tool for developing effective therapeutic strategies.MethodsWe examined HCM-based gene expression datasets (GSE36961) from the Gene Expression Omnibus (GEO) database for the identification of differentially expressed genes (DEGs), PPI network development, module screening, and shortlisting of hub genes via GEOR2, STRING, and Cytoscape. Moreover, we also used another HCM-based gene expression dataset (GSE32453) for the expression validation of hub genes. Following this, we constructed the lncRNA-cricRNA-miRNA-mRNA regulatory network after retrieving information from the miRTarBase, miRDB, and MiRcode databases. Finally, we used DAVID to perform functional and pathway analysis of the hub genes.ResultsFrom GSE36961, a total of the 262 most significant DEGs, including 162 down-regulated and 76 up-regulated, were identified between HCM patients and normal individuals. Among these DEGs, a total of 10 significantly down-regulated DEGs, including cluster of differentiation 14 (CD14), beta2 Integrin Gene (ITGB2), C1q subcomponent subunit B (C1QB), Cluster of Differentiation 163 (CD163), Hematopoietic Cell-Specific Lyn Substrate 1 (HCLS1), Arachidonate 5-Lipoxygenase Activating Protein (ALOX5AP), Pleckstrin (PLEK), Complement C1q C Chain (C1QC), Fc fragment Of IgE receptor Ig (FCER1G), and tyrosine kinase binding protein (TYROBP), were shortlisted as the hub genes. Pathway enrichment analysis showed that the identified hub genes were involved in the dysregulation of some diverse pathways in HCM patients. Such as, Pertussis, Complement and coagulation cascade, Legnionellosis, Asthma, Staphylococcus aureus infection, etc. Lastly, we also explored hub genes' regulatory 2 MicroRNAs (miRNAs, has-mir-7-5p and has-mir-27a-3p), one Long non-coding RNAs (lncRNA, OIP5-AS1-201), and one Circular RNA (cricRNA, CDR1as) via lncRNA-cricRNA-miRNA-mRNA regulatory network.ConclusionOur study revealed that ten hub genes (CD14, ITGB2, C1QB, CD163, HCLS1, ALOX5AP, PLEK, C1QC, FCER1G, and TYROBP) are involved in the development and progression of HCM. These genes can potentially be used as biomarkers and therapeutic targets for HCM patients.
| S-EPMC9827312 | biostudies-literature
Project description:ObjectiveTo reveal the molecular mechanism underlying the pathogenesis of HCM and find new effective therapeutic strategies using a systematic biological approach.MethodsThe WGCNA algorithm was applied to building the co-expression network of HCM samples. A sample cluster analysis was performed using the hclust tool and a co-expression module was constructed. The WGCNA algorithm was used to study the interactive connection between co-expression modules and draw a heat map to show the strength of interactions between modules. The genetic information of the respective modules was mapped to the associated GO terms and KEGG pathways, and the Hub Genes with the highest connectivity in each module were identified. The Wilcoxon test was used to verify the expression level of hub genes between HCM and normal samples, and the "pROC" R package was used to verify the possibility of hub genes as biomarkers. Finally, the potential functions of hub genes were analyzed by GSEA software.ResultsSeven co-expression modules were constructed using sample clustering analysis. GO and KEGG enrichment analysis judged that the turquoise module is an important module. The hub genes of each module are RPL35A for module Black, FH for module Blue, PREI3 for module Brown, CREB1 for module Green, LOC641848 for module Pink, MYH7 for module Turquoise and MYL6 for module Yellow. The results of the differential expression analysis indicate that MYH7 and FH are considered true hub genes. In addition, the ROC curves revealed their high diagnostic value as biomarkers for HCM. Finally, in the results of the GSEA analysis, MYH7 and FH highly expressed genes were enriched with the "proteasome" and a "PPAR signaling pathway," respectively.ConclusionsThe MYH7 and FH genes may be the true hub genes of HCM. Their respective enriched pathways, namely the "proteasome" and the "PPAR signaling pathway," may play an important role in the development of HCM.
| S-EPMC7585681 | biostudies-literature
Project description:BackgroundEsophageal squamous cell carcinoma (ESCC) is a typical Gastro-Intestinal (GI) tract neoplasm. This study was conducted to know the Differential Expressed Genes (DEGs) profile of ESCC along with hub gene screening, lncRNA identification, and drug-genes interactions.MethodsGSE161533, GSE20347, GSE45670 microarray datasets were retrieved from the NCBI Gene Expression Omnibus (GEO) database. GEO2R was used for the DEGs identification, whereas GO (Gene Ontology) and KEGG enrichment analysis were performed in DAVID. PPI network constructed using STRING and visualized with Cytoscape app with the help of MCODE. The top ten connectivity genes were selected as hub genes-further survival analysis was performed in the Kaplan-Meier plotter. Moreover, Boxplot, pathological stage plots were constructed using GEPIA (Gene Expression Profiling Interactive Analysis). The methylation heatmap assembled in the DiseaseMeth version 2.0. lncRNA (Long non-coding RNA) was identified comparing the list of genes in HUGO, and Gene-drug interactions were accumulated from the DgiDB platform.ResultsThis experiment showed 16 upregulated, and 59 downregulated DEGs shared among the three datasets. Biological process analysis showed significant terms such as extracellular matrix disassembly and collagen catabolism. The extracellular region was detected as the most crucial cellular compartment. Notably, metalloen dopeptidease and serine-type endopeptidase activity showed significant molecular functions term. In contrast, transcriptional misregulation was a highly substantial KEGG pathway. Kaplan-Meier plotter showed higher expression of CXCL8, SPP1, MMP13, CXCL1, and TOP2A have a significant impact on the overall survival of the patients. Nine out of ten hub genes have significantly different expression levels than normal and cancer tissues. HYMAI was the only lncRNA commonly expressed upregulated among the three datasets. Drug-gene interaction showed multiple genes have no drug options exist till now.
| S-EPMC9035652 | biostudies-literature
Project description:BackgroundHypertrophic cardiomyopathy (HCM) represents one of the most common inherited heart diseases. To identify key molecules involved in the development of HCM, gene expression patterns of the heart tissue samples in HCM patients from multiple microarray and RNA-seq platforms were investigated.MethodsThe significant genes were obtained through the intersection of two gene sets, corresponding to the identified differentially expressed genes (DEGs) within the microarray data and within the RNA-Seq data. Those genes were further ranked using minimum-Redundancy Maximum-Relevance feature selection algorithm. Moreover, the genes were assessed by three different machine learning methods for classification, including support vector machines, random forest and k-Nearest Neighbor.ResultsOutstanding results were achieved by taking exclusively the top eight genes of the ranking into consideration. Since the eight genes were identified as candidate HCM hallmark genes, the interactions between them and known HCM disease genes were explored through the protein-protein interaction (PPI) network. Most candidate HCM hallmark genes were found to have direct or indirect interactions with known HCM diseases genes in the PPI network, particularly the hub genes JAK2 and GADD45A.ConclusionsThis study highlights the transcriptomic data integration, in combination with machine learning methods, in providing insight into the key hallmark genes in the genetic etiology of HCM.
| S-EPMC8259117 | biostudies-literature
Project description:Hypertrophic cardiomyopathy (HCM) is an autosomal dominant disease that affects 1 in every 200 people in the general population, leading to cardiac ischemia, heart failure and increased risk of sudden death. Recently, accumulating evidence has suggested that long noncoding RNAs (lncRNAs) may serve specific roles in various biological processes and participate in the pathology of various diseases, including HCM. Although a large number of lncRNAs have been detected, the functions of lncRNAs in HCM are still unknown. In the present study, a global triple network based on competitive endogenous RNA (ceRNA) theory was constructed using data from the National Center for Biotechnology Information Gene Expression Omnibus. Furthermore, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses of mRNAs in the lncRNA-microRNA (miRNA)-mRNA network were performed using the Cytoscape plugins, BiNGO and Database. The lncRNA-miRNA-mRNA network was composed of 30 lncRNA nodes, 94 mRNA nodes and 8 miRNA nodes. Subsequently, hub nodes and the number of relationship pairs were analyzed and showed that 5 lncRNAs (ENST00000597346.1, ENST00000458178.1, ENST00000544461.1, ENST00000567093.1 and ENST00000571219.1) were closely related to HCM. Cluster module analysis and Random Walk with Restart of the ceRNA network further confirmed the potential role of two lncRNAs (ENST00000458178.1 and ENST00000567093.1) in HCM. The present study provides a new strategy for identifying potential pathways associated with HCM or other diseases. Furthermore, lncRNA-miRNA pairs may be regarded as candidate diagnostic biomarkers or potential therapeutic targets for HCM.
| S-EPMC7388262 | biostudies-literature
Project description:Hypertrophic cardiomyopathy (HCM) is an autosomal dominant cardiac disorder characterized by ventricular hypertrophy resulting from the disordered arrangement of myocardial cells, which leads to impaired cardiac function or death. Autophagy (AT) is a biochemical process through which lysosomes degrade and recycle damaged or discarded intracellular components to protect cells against external environmental conditions, such as hypoxia and oxidative stress. AT is closely related to HCM, and thus, serves an important role in myocardial hypertrophy. However, the precise mechanism underlying the regulation of AT in cardiac hypertrophy remains elusive. The present study aimed to examine the role and mechanisms of AT-related genes (ARGs) in HCM through bioinformatics analysis and experimental validation and to identify potential targeted drugs for HCM. In this study, cardiac samples were obtained from healthy individuals and patients with HCM from the GEO database, and screened for differentially expressed ARGs to further investigate their potential interactions and functional pathways. These genes were subjected to functional enrichment analysis to identify potential crosstalk and involved pathways. Based on a protein-protein interaction network, EIF4EBP1, MCL1, PIK3R1, CCND1 and PPARG were identified as potential biomarkers for the diagnosis and treatment of HCM. Furthermore, 10 components with therapeutic potential for HCM were predicted based on the aforementioned hub genes. The results of bioinformatics analysis were validated using H9c2 cells stimulated with angiotensin II, which represented an in vitro model of cardiac hypertrophy. Overall, the present study demonstrated that the expression levels of ARGs were substantially altered in HCM. Therefore, these genes may be used as diagnostic biomarkers and therapeutic targets for HCM.
| S-EPMC11443588 | biostudies-literature
Project description:Background: Hypertrophic cardiomyopathy (HCM) is a myocardial disease with unidentified pathogenesis. Increasing evidence indicated the potential role of microRNA (miRNA)-mRNA regulatory network in disease development. This study aimed to explore the miRNA-mRNA axis in HCM. Methods: The miRNA and mRNA expression profiles obtained from the Gene Expression Omnibus (GEO) database were used to identify differentially expressed miRNAs (DEMs) and genes (DEGs) between HCM and normal samples. Target genes of DEMs were determined by miRTarBase. Gene ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to identify biological functions of the DEGs and DEMs. miRNA-mRNA regulatory network was constructed to identify the hub genes and miRNAs. Logistic regression model for HCM prediction was established basing on the network. Results: A total of 224 upregulated and 366 downregulated DEGs and 10 upregulated and 14 downregulated DEMs were determined. We identified 384 DEM-targeted genes, and 20 of them were overlapped with the DEGs. The enriched functions include extracellular structure organization, organ growth, and phagosome and melanoma pathways. The four miRNAs and three mRNAs, including hsa-miR-373, hsa-miR-371-3p, hsa-miR-34b, hsa-miR-452, ARHGDIA, SEC61A1, and MYC, were identified through miRNA-mRNA regulatory network to construct the logistic regression model. The area under curve (AUC) values over 0.9 suggested the good performance of the model. Conclusion: The potential miRNA-mRNA regulatory network and established logistic regression model in our study may provide promising diagnostic methods for HCM.
| S-EPMC8200816 | biostudies-literature
Project description:Glioblastoma multiforme (GBM) is the most fatal malignancy, and despite extensive treatment, tumors inevitably recur. This study aimed to identify recurrence-associated molecules in GBM. The gene expression profile GSE139533, containing 70 primary and 47 recurrent GBM tissues and their corresponding clinical traits, was downloaded from the Gene Expression Omnibus (GEO) database and used for weighted gene co-expression network analysis (WGCNA) and differentially expressed gene (DEG) analysis. After identifying the hub genes which differentially expressed in recurrent GBM tissues and in the gene modules correlated with recurrence, data from the Chinese Glioma Genome Atlas (CCGA) and The Cancer Genome Atlas (TCGA) databases were analyzed with GSE43378 to determine the relationship between hub genes and patient prognosis. The diagnostic value of the identified hub genes was verified using 52 GBM tissues. Three gene modules were correlated with recurrence and 2623 genes were clustered in these clinically significant modules. Among these, 13 genes - EHF, TRPM1, FXYD4, CDH15, LHX5, TP73, FBN3, TLX1, C1QL4, COL2A, SEC61G, NEUROD4 and GPR139 - were differentially expressed in recurrent GBM samples; low LHX5 and TLX1 expression predicted poor outcomes. LHX5 and TLX1 expression showed weak positive relationships with Karnofsky performance scale scores. Additionally, LHX5 and TLX1 expression was found to be decreased in our recurrent GBM samples compared with that in primary samples; these genes exhibited high diagnostic value in distinguishing recurrent samples from primary samples. Our findings indicate that LHX5 and TLX1 might be involved in GBM recurrence and act as potential biomarkers for this condition.
| S-EPMC8806787 | biostudies-literature