Common Variants in the <i>ARG1</i> Gene Contribute to the Risk of Dilated Cardiomyopathy in the Han Chinese Population

Li, Chaomin; Wang, Liping; Li, Yuanbo; Zhang, Feng; Wang, Qiang; Luo, Wei

doi:10.1089/gtmb.2020.0080

Cited by 5 publications

(4 citation statements)

References 43 publications

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“…In functional enrichment analysis, some genes involved with regulation of cardiovascular system processes were enriched in HF. Liu et al [ 47 ], Kosugi et al [ 48 ], McMacken et al [ 49 ], Pan and Zhang [ 50 ], Li et al [ 51 ] and Jiang et al [ 52 ] presented that expression of HLA-DQA1, KDM5D, UCHL1, SAA1, ARG1 and LYVE1 were associated with progression of cardiomyopathy. Hou et al [ 53 ] and Olesen et al [ 54 ] demonstrated that DACT2 and KCND3 were found to be substantially related to atrial fibrillation.…”

Section: Discussionmentioning

confidence: 99%

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Kolur¹,

Vastrad²,

Vastrad

et al. 2021

BMC Cardiovasc Disord

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Introduction Heart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules. Methods The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein–protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene—miRNA regulatory network and target gene—TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed. Results A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene—miRNA regulatory network and target gene—TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed. Conclusion The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

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Section: Discussionmentioning

confidence: 99%

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Kolur¹,

Vastrad²,

Vastrad

et al. 2021

BMC Cardiovasc Disord

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show abstract

“…Recently, increasing evidence demonstrated that KCNJ2 [128], TLR4 [129], CYP2D6 [130], TLR2 [129], SNX10 [131], SIRT1 [132], PF4 [133], PCYT2 [134] and LGALS3 [135] were altered expression in atrial fibrillation. Studies had shown that KCNJ2 [136], ARG1 [137], TLR4 [138], IFIH1 [139], FBN2 [140], PDK4 [141], TLR8 [142], PDGFC (platelet derived growth factor C) [143], FNIP1 [144], TLR2 [145], PTPN11 [146], LATS2 [147], GCH1 [148], ARFGEF2 [149], CA2 [150], PTPRC (protein tyrosine phosphatase receptor type C) [151], CCR2 [152], GAB1 [153], VEGFA (vascular endothelial growth factor A) [154], UBR1 [155], PHLPP1 [156], MTM1 [157], FMR1 [158], SIRT1 [159], NOD2 [160], MYOF (myoferlin) [161], OSBPL11 [162], ZBTB11 [121], UTRN (utrophin) [163], ZNF593 [164], CCR7 [165], PRDX2 [166], BIN1 [167], NFIC (nuclear factor I C) [168], TCF4 [169], PPP1R13L [124], NDUFB11 [170], TAX1BP3 [171], TRPM4 [172], NMRAL1 [173], LGALS3 [126] and NAA10 [174] were associated with cardiomyopathy. CD274 [175], CEACAM1 [176], STAT1 [177], ARG1 [178], TLR4 [179], LRRK2 [180], ABCA1 [181], IFIH1 [182], TLR5 [183], PTGS2 [184], CYP2D6 [185], RNF213 [186], C9ORF72 [187], JAK2 [188], TLR8 [189], NOTCH2 [190], PDGFC (platelet derived growth factor C) [191], TLR2 [192], PRKAB2 [193], HDAC9 [194], NCOA4 [195], LATS2 [196], DICER1 […”

Section: Discussionmentioning

confidence: 99%

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Vastrad¹,

Vastrad²

2023

Preprint

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Coronary artery disease (CAD) is the most common cardiovascular disorder and the leading cause of heart related deaths in world. Increasing molecular targets have been discovered for CAD and CAD - related complications prognosis and therapy. However, there is still an urgent need to identify novel biomarkers. Therefore, we evaluated biomarkers that might help the diagnosis and treatment of CAD and CAD related complications. We searched next generation sequencing (NGS) dataset (GSE202625) and identified differentially expressed genes (DEGs) by comparing CAD and normal control samples using DESeq2. Gene ontology (GO) and pathway enrichment analyses of the DEGs were performed using the g:Profiler online database. The protein protein interaction (PPI) network was plotted with IMEx interactome and visualized using Cytoscape. Module analysis of the PPI network was done using PEWCC1. MiRNA hub gene regulatory network and TF hub gene regulatory network analysis was performed to identify the hub genes, miRNAs and TFs. Receiver operating characteristic (ROC) curve analysis was used to predict the diagnostic effectiveness of the hub genes. A total of 118 DEGs (479 up regulated genes and 479 down regulated genes) were detected. The GO enrichment analysis indicated that the DEGs most significantly enriched in cellular response to stimulus and biosynthetic process. The REACTOME pathway enrichment analysis revealed that the DEGs were most significantly enriched in immune system and eukaryotic translation elongation. PPI network, modules, miRNA hub gene regulatory network and TF hub gene regulatory network analysis demonstrated that EGR1, SIRT1, STAT1, LRRK2, HIF1A, CSNK2B, RPS3, RPS2, RPS4X and HDAC11 were the hub genes. On the whole, the findings of this study enhance our understanding of the potential molecular mechanisms of CAD and CAD-related complications, and provide potential targets for further research.

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“…In functional enrichment analysis, some genes involved with regulation of cardiovascular system processes were enriched in HF. Liu et al [38], Kosugi et al [39], McMacken et al [40], Pan and Zhang [41], Li et al [42] and Jiang et al [43] presented that expression of HLA-DQA1, KDM5D, UCHL1, SAA1, ARG1 and LYVE1 were associated with progression of cardiomyopathy. Hou et al [44] and Olesen et al [45] demonstrated that DACT2 and KCND3 were found to be substantially related to atrial fibrillation.…”

Section: Discussionmentioning

confidence: 99%

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Vastrad¹,

Tengli

Vastrad³

2021

Preprint

View full text Add to dashboard Cite

Heart failure (HF) is a heterogeneous clinical syndrome and affects millions of people all over the world. HF occurs when the cardiac overload and injury, which is a worldwide complaint. The aim of this study was to screen and verify hub genes involved in developmental HF as well as to explore active drug molecules. The expression profiling by high throughput sequencing of GSE141910 dataset was downloaded from the Gene Expression Omnibus (GEO) database, which contained 366 samples, including 200 heart failure samples and 166 non heart failure samples. The raw data was integrated to find differentially expressed genes (DEGs) and were further analyzed with bioinformatics analysis. Gene ontology (GO) and REACTOME enrichment analyses were performed via ToppGene; protein-protein interaction (PPI) networks of the DEGs was constructed based on data from the HiPPIE interactome database; modules analysis was performed; target gene - miRNA regulatory network and target gene - TF regulatory network were constructed and analyzed; hub genes were validated; molecular docking studies was performed. A total of 881 DEGs, including 442 up regulated genes and 439 down regulated genes were observed. Most of the DEGs were significantly enriched in biological adhesion, extracellular matrix, signaling receptor binding, secretion, intrinsic component of plasma membrane, signaling receptor activity, extracellular matrix organization and neutrophil degranulation. The top hub genes ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 were identified from the PPI network. Module analysis revealed that HF was associated with adaptive immune system and neutrophil degranulation. The target genes, miRNAs and TFs were identified from the target gene - miRNA regulatory network and target gene - TF regulatory network. Furthermore, receiver operating characteristic (ROC) curve analysis and RT-PCR analysis revealed that ESR1, PYHIN1, PPP2R2B, LCK, TP63, PCLAF, CFTR, TK1, ECT2 and FKBP5 might serve as prognostic, diagnostic biomarkers and therapeutic target for HF. The predicted targets of these active molecules were then confirmed. The current investigation identified a series of key genes and pathways that might be involved in the progression of HF, providing a new understanding of the underlying molecular mechanisms of HF.

show abstract

Common Variants in the ARG1 Gene Contribute to the Risk of Dilated Cardiomyopathy in the Han Chinese Population

Cited by 5 publications

References 43 publications

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

Identification of novel prognostic targets in coronary artery disease and related complications using bioinformatics and next generation sequencing data analysis

Identification of candidate biomarkers and therapeutic agents for heart failure by bioinformatics analysis

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