Dataset Information

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

ABSTRACT: Atrial fibrillation (AF) is the most common type of persistent arrhythmia. Although its incidence has been increasing, the pathogenesis of AF in stroke remains unclear. In this study, a total of 30 participants were recruited, including 10 controls, 10 patients with AF and 10 patients with AF and stroke (AF + STROKE). Differentially expressed genes (DEGs) were identified, and functional annotation of DEGs, comparative toxicogenomic database analysis associated with cardiovascular diseases, and predictions of miRNAs of hub genes were performed. Using RT-qPCR, biological process and support vector machine neural networks, numerous DEGs were found to be related to AF. HBG1, SNCA and GYPB were found to be upregulated in the AF group. Higher expression of hub genes in AF and AF + STROKE groups was detected via RT-PCR. Upon training the biological process neural network of SNCA and GYPB for HBG1, only small differences were detected. Based on the support vector machine, the predicted value of SNCA and GYPB for HBG1 was 0.9893. Expression of the hub genes of HBG1, SNCA and GYPB might therefore be significantly correlated to AF. These genes are involved in the incidence of AF complicated by stroke, and may serve as targets for early diagnosis and treatment.

SUBMITTER: Wang X

PROVIDER: S-EPMC8980947 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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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.

Wang Xiang X Meng Xuyang X Meng Lingbing L Guo Ying Y Li Yi Y Yang Chenguang C Pei Zuowei Z Li Jiahan J Wang Fang F

Journal of cellular and molecular medicine 20220209 7

Atrial fibrillation (AF) is the most common type of persistent arrhythmia. Although its incidence has been increasing, the pathogenesis of AF in stroke remains unclear. In this study, a total of 30 participants were recruited, including 10 controls, 10 patients with AF and 10 patients with AF and stroke (AF + STROKE). Differentially expressed genes (DEGs) were identified, and functional annotation of DEGs, comparative toxicogenomic database analysis associated with cardiovascular diseases, and p ...[more]

PMID: 35138035

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Project description: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.

Project description:Background To explore the pathophysiological features of ischemic stroke in patients with atrial fibrillation (AF), we evaluated the association between 268 plasma proteins and subsequent ischemic stroke in 2 large AF cohorts receiving oral anticoagulation. Methods and Results A case-cohort sample of patients with AF from the ARISTOTLE (Apixaban for Reduction in Stroke and Other Thromboembolic Events in Atrial Fibrillation) trial, including 282 cases with ischemic stroke or systemic embolism and a random sample of 4124 without these events, during 1.9 years of follow-up was used for identification. Validation was provided by a similar case-cohort sample of patients with AF from the RE-LY (Randomized Evaluation of Long-Term Anticoagulation Therapy) trial, including 149 cases with ischemic stroke/systemic embolism and a random sample of 1062 without these events. In plasma obtained before randomization, 268 unique biomarkers were measured with OLINK proximity extension assay panels (CVD II, CVD III, and Inflammation) and conventional immunoassays. The association between biomarkers and outcomes was evaluated by random survival forest and adjusted Cox regression. According to random survival forest or Cox regression analyses, the biomarkers most strongly and consistently associated with ischemic stroke/systemic embolism were matrix metalloproteinase-9, NT-proBNP (N-terminal pro-B-type natriuretic peptide), osteopontin, sortilin, soluble suppression of tumorigenesis 2, and trefoil factor-3. The corresponding hazard ratios (95% CIs) for an interquartile difference were as follows: 1.18 (1.00-1.38), 1.55 (1.28-1.88), 1.28 (1.07-1.53), 1.19 (1.02-1.39), 1.23 (1.05-1.45), and 1.19 (0.97-1.45), respectively. Conclusions In patients with AF, of 268 unique biomarkers, the 6 biomarkers most strongly associated with subsequent ischemic stroke/systemic embolism represent fibrosis/remodeling (matrix metalloproteinase-9 and soluble suppression of tumorigenesis 2), cardiac dysfunction (NT-proBNP), vascular calcification (osteopontin), metabolism (sortilin), and mucosal integrity/ischemia (trefoil factor-3). Registration URL: https://www.clinicaltrials.gov. Unique Identifiers: NCT00412984 and NCT00262600.

Dataset Information

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

Publications

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

Similar Datasets

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