Joint efficacy of the three biomarkers SNCA, GYPB and HBG1 for atrial fibrillation and stroke: Analysis via the support vector machine neural network

Wang, Xiang; Meng, Xuyang; Meng, Ling‐Bing; Guo, Ying; Li, Yang; Yang, Chenguang; Pei, Zuowei; Li, Jiahan; Wang, Fang

doi:10.1111/jcmm.17224

Cited by 5 publications

(4 citation statements)

References 56 publications

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“…In agreement with previous studies, the GO annotations related to HBD are oxygen transport, iron ion binding, blood coagulation, and combination with oxygen (Chen et al, 2020). In previous studies, we found that HBG1/HBD may induce the occurrence and development of AF through inflammation and hemoglobin levels (Wang et al, 2022). We need further studies to evaluate the clinical implications of our results and whether patients with atrial fibrillation will benefit from the antiinflammatory effects of edoxaban.…”

Section: Introductionsupporting

confidence: 91%

“…Through the immunofluorescence assay, compared with the red blood cells of the control individuals, the expression of HBG1 was higher in the red blood cells of the patients with atrial fibrillation (Wang et al, 2022) (p < 0.05, Figure 1A). In addition, the expression of HBD was also higher in the red blood cells of the patients with atrial fibrillation than in those of the control individuals (p < 0.05; Figure 1B).…”

Section: Expressions Of Hbg1 and Hbd In The Red Blood Cells Of The Pa...mentioning

confidence: 99%

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Beneficial Effect of Edoxaban on Preventing Atrial Fibrillation and Coagulation by Reducing Inflammation via HBG1/HBD Biomarkers

et al. 2022

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Background: Atrial fibrillation (AF) is the most common cardiac arrhythmia. The effectiveness and mechanism of edoxaban in preventing stroke after atrial fibrillation remain unclear.Methods: The expressions of HBG1 and HBD in red blood cells were tested in AF. Sixty C57B/6J mice were randomly divided into the following groups: the control (CON) group, atrial fibrillation (AF) group, AF + edoxaban group, and AF + rivaroxaban group. H&E staining assay and reticular fiber staining were performed. Myocardial fibrosis was evaluated by the Masson staining assay, Sirius red staining assay, and immunohistochemical assay for the expressions of α-SMA and COL1A1. ELISA and RT-PCR assay were performed for the detection of inflammatory parameters (TNF-α, IL-1β, IL-6, and IL-10). Blood lipids were detected by using the Beckman automatic biochemical analyzer. Furthermore, four items of coagulation were detected, and molecular docking among HBG1, HBD, and MASP1 (Xa) was performed by PyMOL 2.1 software. The BP neural network model, cubic spline interpolation, and support vector machine model were constructed to predict prothrombin time based on HBG1 and HBD expressions. COIP assay was performed to construct the interaction between HBG1 and HBD. The functional enrichment analysis was performed by DAVID and Metascape tools.Results: The expressions of HBG1 and HBD in red blood cells of the patients with atrial fibrillation were decreased. The results showed a lower level of hemoglobin in red blood cells with HBG1-siRNA and HBG1-siRNA. Compared with the AF group, the collagen fiber percentage in the AF + edoxaban group was decreased (p < 0.05). After using edoxaban, the expressions of TNF-α, IL-1β, IL-6, and IL-10 were significantly decreased (p < 0.05). The LDL-C, TC, and TG levels were downregulated in the AF + edoxaban group. The PT and APTT levels in the AF + edoxaban group were more increasing than in the AF mice (p < 0.05). Compared with the AF group, the expressions of HBG1 and HBD were downregulated in the AF + edoxaban group (p < 0.05). HBG1 protein matched well with HBD and MASP1(Xa) protein surfaces. There exists a significant interaction between HBG1, HBD, and PT via the BP neural network and support vector machine. Enrichment analysis showed that HBG1 and HBD were mainly enriched in blood coagulation.Conclusion: Edoxaban could prevent atrial fibrillation and coagulation by reducing inflammation, lipids, and fibrosis via HBG1/HBD biomarkers effectively, and the effect was superior to that of rivaroxaban.

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Section: Introductionsupporting

confidence: 91%

Section: Expressions Of Hbg1 and Hbd In The Red Blood Cells Of The Pa...mentioning

confidence: 99%

Beneficial Effect of Edoxaban on Preventing Atrial Fibrillation and Coagulation by Reducing Inflammation via HBG1/HBD Biomarkers

et al. 2022

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“…IFIT1 [51] might be involved in the pathogenesis of atherosclerosis. HBG1 [52] is important in the development of atrial fibrillation. RAP1GAP [53] was associated with cardiac hypertrophy.…”

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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“…As a result, 957 DEGs including 477 up regulated and 480 down regulated genes were identified. Wang et al [44] and Joshi et al [45] showed altered expression of HBG1 and S100A8 in cardiovascular diseases. Lu et al [46] reported that S100A8 promoted cognitive dysfunction.…”

Section: Discussionmentioning

confidence: 99%

Identification of Key Genes and Biological Pathways in Bipolar Disorder by Bioinformatics and Next Generation Sequencing Data Analysis

Vastrad¹,

Vastrad²

2022

Preprint

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Bipolar disorder (BD), also known as psychiatric disorder, affects millions of people all over the world. The aim of this investigation was to screen and verify hub genes involved in BD as well as to explore potential molecular mechanisms. The next generation sequencing (NGS) dataset GSE124326 was downloaded from the Gene Expression Omnibus (GEO) database, which contained 480 samples, including 240 BD and 240 normal controls. Differentially expressed genes (DEGs) were filtered and subjected to gene ontology (GO) and pathway enrichment analyses. A protein–protein interaction (PPI) network and module analysis were used to identify hub genes. The miRNA-hub gene regulatory network and TF-hub gene regulatory network were also constructed. Receiver operating characteristic curve (ROC) analysis was used to validate hub genes. In this work, 957 DEGs, including 477 up regulated and 480 down regulated, were obtained from NGS data. The GO and pathway enrichment analyses of the DEGs showed that the up regulated genes were enriched in the neutrophil degranulation, immune system, transport, cytoplasm and enzyme regulator activity, and the down regulated genes were enriched in extracellular matrix organization, diseases of metabolism, multicellular organismal process, cell periphery and metal ion binding. Finally, through analyzing the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, we screened hub genes UBB, UBE2D1, TUBA1A, RPL11, RPS24, NOTCH3, CAV1, CNBD2, CCNA1 and MYH11 by the Cytoscape software. The current investigation illustrates a characteristic NGS data in BD, which might contribute to the interpretation of the progression of BD and provide novel biomarkers and therapeutic targets for BD.

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Joint efficacy of the three biomarkers SNCA, GYPB and HBG1 for atrial fibrillation and stroke: Analysis via the support vector machine neural network

Cited by 5 publications

References 56 publications

Beneficial Effect of Edoxaban on Preventing Atrial Fibrillation and Coagulation by Reducing Inflammation via HBG1/HBD Biomarkers

Beneficial Effect of Edoxaban on Preventing Atrial Fibrillation and Coagulation by Reducing Inflammation via HBG1/HBD Biomarkers

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

Identification of Key Genes and Biological Pathways in Bipolar Disorder by Bioinformatics and Next Generation Sequencing Data Analysis

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