Does Decreased SNX10 Serve as a Novel Risk Factor in Atrial Fibrillation of the Valvular Heart Disease? – A Case-Control Study

Yao, Jianping; Hou, Jian; Lv, Linhua; Song, Chen; Zhang, Mingxia; Wu, Zhongkai

doi:10.21470/1678-9741-2019-0413

Cited by 5 publications

(3 citation statements)

References 25 publications

(8 reference statements)

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“…Studies have revealed that KCNJ2 [115], TLR4 [116], JAK2 [117], TLR2 [118], EGR1 [119], GAB1 [120], ZBTB11 [121], BIN1 [122], TCF4 [123], PPP1R13L [124], TRPM4 [125], LGALS3 [126] and SNTA1 [127] plays a key role in cardiac arrhythmia. 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.…”

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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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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“…By performing DEGs analysis, 479 up regulated and 479 down regulated DEGs were successfully identified (|logFC| > 0.512 for up regulated genes, |logFC| < -0.831 for down regulated genes and adjust P-value < .05), respectively. Involvement of SLCO1A2 molecule) [315], DACH1 [316], PNPLA3 [317], FGF9 [192], SLC7A11 [193], SGPP1 [318], VIP (vasoactive intestinal peptide) [319], KCNJ2 [320], KL (klotho) [321], SMAD6 [135], BMPR2 [322], APOA1 [323], CALCRL (calcitonin receptor like receptor) [324], INSIG1 [325], RASGRF1 [198], LRRK2 [326], TLR3 [327], ADRB1 [328], SLC22A3 [329], CA2 [330], SNX10 [331], LIFR (LIF receptor subunit alpha) [332], TLR8 [333], CMPK2 [334], GATA3 [335], RSPO2 [336], CCR2 [205], NEK7 [337], TLR7 [338], BEX1 [339], EFNB2 [340], CAV1 [341], ARRB1 [342], TRPC3 [343], CR1 [344], PEG10 [345], DLL4 [346], MEFV (MEFV innate immuity regulator, pyrin) [347], TFPI (tissue factor pathway inhibitor) [348], EPAS1 [349], FADS1 [215], DKK2 [350], CACNA2D2 [351], DPP6 [352]…”

Section: Discussionmentioning

confidence: 99%

Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

Vastrad,

Vastrad

2023

Preprint

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Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analysis of DEGs were performed. Then, a protein protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst database was used to construct the targeted microRNAs (miRNAs) and transcription factors (TFs) of the hub genes. Finally receiver operating characteristic (ROC) curve analysis was used to validates the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8 and EFHC2 were selected. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.

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“…MT1A [159], LYVE1 [160], S100A12 [161], GCKR (glucokinase regulator) [162], TLR8 [163], MRC1 [164], AGTR1 [165], P2RY12 [166], MSR1 [167], NQO1 [168], FKBP5 [169], CMTM5 [170], ADH1C [171], APLNR (apelin receptor) [172], SFRP4 [173], CCL3 [174], COL11A2 [175], EGR3 [176] and IL2RB [177] expression have a role in coronary artery disease. EDN2 [178], SNX10 [179] and KCNN1 [180] were played a predominant role in progression of atrial fibrillation. EDNRB (endothelin receptor type B) [181], FGF10 [182], WNK3 [183], KNG1 [184], FCN3 [185], AQP3 [186], HPR (haptoglobin-related protein) [187], CTH (cystathionine gamma-lyase) [188], SPARCL1 [189], MAOA (monoamine oxidase A) [190], BMPR1B (bone morphogenetic protein receptor type 1B) [191], FGF7 [192], CALCRL (calcitonin receptor like receptor) [193], MARK3 [194], ADH1B [195], AMH (anti-Mullerian hormone) [196], RET (ret proto-oncogene) [197], IGF2 [198], SLC6A9 [199], NPPA (natriuretic peptide A) [200], SCT (secretin) [201], DCX (doublecortin) [202], ASIC1 [203], LMX1B [204], DBP (D-box binding PAR bZIP transcription factor) [205] and SLC6A9 [206] levels are correlated with disease severity in patients with hypertension.…”

Section: Discussionmentioning

confidence: 99%

Identification and Interaction Analysis of Molecular Markers in Myocardial Infarction by Integrated Bioinformatics Analysis

Vastrad¹,

Vastrad²

2021

Preprint

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Myocardial infarction (MI) is the leading cardiovascular diseases in worldwide, yet relatively little is known about the genes and signaling pathways involved in MI progression. The present investigation aimed to elucidate potential crucial candidate genes and pathways in MI. expression profiling by high throughput sequencing dataset (GSE132143) was downloaded from the Gene Expression Omnibus (GEO) database, which included data from 20 MI samples and 12 normal control samples. Differentially expressed genes (DEGs) were identified using t-tests in the DESeq2 R package. These DEGs were subsequently investigated by Gene Ontology (GO) and pathway enrichment analysis, a protein-protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed and analyzed. Hub genes were validated by receiver operating characteristic curve (ROC) analysis. In total, 958 DEGs were identified, of which 480 were up regulated and 478 were down regulated. GO and pathway enrichment analysis results revealed that the DEGs were mainly enriched in, immune system, neuronal system, response to stimulus, and multicellular organismal process. A PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network was constructed by using Cytoscape software, and CFTR, CDK1, RPS13, RPS15A, RPS27, NOTCH1, MRPL12, NOS2, CCDC85B and ATN1 were identified as the hub genes. Our results highlight the important roles of the genes including CFTR, CDK1, RPS13, RPS15A, RPS27, NOTCH1, MRPL12, NOS2, CCDC85B and ATN1 in MI pathogenesis or therapeutic management.

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Does Decreased SNX10 Serve as a Novel Risk Factor in Atrial Fibrillation of the Valvular Heart Disease? – A Case-Control Study

Cited by 5 publications

References 25 publications

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

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

Study on potential differentially expressed genes in idiopathic pulmonary fibrosis by bioinformatics and next generation sequencing data analysis

Identification and Interaction Analysis of Molecular Markers in Myocardial Infarction by Integrated Bioinformatics Analysis

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