通过生物信息学和机器学习分析来解读急性缺血性卒中中坏死相关的分子亚型

IF 2.8 4区 医学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
Zongkai Wu, Hongzhen Fan, Lu Qin, Xiaoli Niu, Bao Chu, Kaihua Zhang, Yaran Gao, Hebo Wang
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引用次数: 0

摘要

急性缺血性脑卒中(Acute ischemic stroke, AIS)是一种具有复杂病理生理过程的严重疾病,可导致残疾和死亡。本研究旨在确定急性缺血性卒中(AIS)中坏死相关基因,并探讨其作为AIS诊断和治疗靶点的潜力。表达谱数据来自Gene Expression Omnibus数据库,坏死相关基因来自GeneCards。将差异表达基因(DEGs)与坏死相关基因相交,得到AIS中坏死相关DEGs (NRDEGs)。在AIS中,共鉴定出76个与坏死性下垂相关的基因(称为NRDEGs)。这些基因的富集分析显示,它们主要富集在已知的诱导坏死性坏死的途径中。利用加权基因共表达网络分析(WGCNA),鉴定出5个由nrdeg组成的共表达模块,以及2个与AIS表现出强相关性的模块。蛋白质-蛋白质相互作用(PPI)分析鉴定出20个枢纽基因。最小绝对收缩和选择算子(LASSO)回归模型显示了诊断预测的良好潜力。受试者工作特征(ROC)曲线验证了诊断模型,并选择了9个具有统计学差异的特征基因(p < 0.05)。通过采用共识聚类,使用这9个特征基因确定了不同的坏死性下垂模式。结果通过AIS患者静脉血、健康对照、HT22细胞以及外部数据集的定量PCR (qPCR)验证。此外,分析的ceRNA网络包括9个lncrna, 6个mirna和3个mrna。总的来说,这项研究为AIS中NRDEGs的分子机制提供了新的见解。这些发现提供了有价值的证据,有助于我们了解这种疾病。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deciphering Necroptosis-Associated Molecular Subtypes in Acute Ischemic Stroke Through Bioinformatics and Machine Learning Analysis

Acute ischemic stroke (AIS) is a severe disorder characterized by complex pathophysiological processes, which can lead to disability and death. This study aimed to determine necroptosis-associated genes in acute ischemic stroke (AIS) and to investigate their potential as diagnostic and therapeutic targets for AIS. Expression profiling data were acquired from the Gene Expression Omnibus database, and necroptosis-associated genes were retrieved from GeneCards. The differentially expressed genes (DEGs) and necroptosis-related genes were intersected to obtain the necroptosis-related DEGs (NRDEGs) in AIS. In AIS, a total of 76 genes associated with necroptosis (referred to as NRDEGs) were identified. Enrichment analysis of these genes revealed that they were primarily enriched in pathways known to induce necroptosis. Using weighted gene co-expression network analysis (WGCNA), five co-expression modules consisting of NRDEGs were identified, along with two modules that exhibited a strong correlation with AIS. Protein–protein interaction (PPI) analysis resulted in the identification of 20 hub genes. The Least absolute shrinkage and selection operator (LASSO) regression model demonstrated promising potential for diagnostic prediction. The receiver operating characteristic (ROC) curve validated the diagnostic model and selected nine characteristic genes that exhibited statistically significant differences (p < 0.05). By employing consensus clustering, distinct patterns of necroptosis were identified using these nine signature genes. The results were validated by quantitative PCR (qPCR) in venous blood from patients with AIS and healthy controls and HT22 cells, as well as external datasets. Furthermore, the analyzed ceRNA network included nine lncRNAs, six miRNAs, and three mRNAs. Overall, this study offers novel insights into the molecular mechanisms underlying NRDEGs in AIS. The findings provide valuable evidence and contribute to our understanding of the disease.

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来源期刊
Journal of Molecular Neuroscience
Journal of Molecular Neuroscience 医学-神经科学
CiteScore
6.60
自引率
3.20%
发文量
142
审稿时长
1 months
期刊介绍: The Journal of Molecular Neuroscience is committed to the rapid publication of original findings that increase our understanding of the molecular structure, function, and development of the nervous system. The criteria for acceptance of manuscripts will be scientific excellence, originality, and relevance to the field of molecular neuroscience. Manuscripts with clinical relevance are especially encouraged since the journal seeks to provide a means for accelerating the progression of basic research findings toward clinical utilization. All experiments described in the Journal of Molecular Neuroscience that involve the use of animal or human subjects must have been approved by the appropriate institutional review committee and conform to accepted ethical standards.
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