Kun Huang , Feng Wen , Jingyi Li , Wenhao Niu , Hui Chen , Shilei Wan , Fupeng Yang , Yihong Chen , Chun Liang
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Next, main roles of the pathological states in AMI were analyzed using GO and KEGG and PPI networks. Diagnostic biomarkers were selected and identified using three machine learning algorithms. Additionally, the expression and diagnostic efficiency of hub genes were verified in the validation cohort (<em>n</em> = 56).</div></div><div><h3>Results</h3><div>366 DEGs were identified (20 upregulated and 306 downregulated). A total of 276 intersecting genes were markedly associated with AMI in the pink and turquoise modules. Based on multiple machine learning algorithms and independent validation, six genes including LILRA1, CCL20, IL1R2, TYROBP, CXCL16 and NFKBIA were identified as hub genes and showed satisfactory diagnostic efficiency both in the discovery cohort and validation cohort.</div></div><div><h3>Conclusion</h3><div>Our data provides evidence supporting a list of six hub genes to be trapped in the pathophysiology of AMI and proposes them as candidate biomarkers for the early diagnosis of AMI.</div></div>","PeriodicalId":72158,"journal":{"name":"American heart journal plus : cardiology research and practice","volume":"56 ","pages":"Article 100554"},"PeriodicalIF":1.8000,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Early diagnosis of acute myocardial infarction via hub genes identified by integrated weighted gene co-expression network analysis\",\"authors\":\"Kun Huang , Feng Wen , Jingyi Li , Wenhao Niu , Hui Chen , Shilei Wan , Fupeng Yang , Yihong Chen , Chun Liang\",\"doi\":\"10.1016/j.ahjo.2025.100554\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background</h3><div>Acute myocardial infarction (AMI) is a leading cause of morbidity and mortality worldwide. 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Additionally, the expression and diagnostic efficiency of hub genes were verified in the validation cohort (<em>n</em> = 56).</div></div><div><h3>Results</h3><div>366 DEGs were identified (20 upregulated and 306 downregulated). A total of 276 intersecting genes were markedly associated with AMI in the pink and turquoise modules. 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引用次数: 0
摘要
背景:急性心肌梗死(AMI)是世界范围内发病率和死亡率的主要原因。循环内皮细胞(CECs)已被报道与AMI的早期阶段有关。我们研究的具体目的是利用生物信息学分析发现循环中CECs的早期诊断标志物。方法从Gene Expression Omnibus (GEO)数据库中获取GSE66360数据集的原始芯片数据。使用R软件从GSE66360发现队列(n = 43)中筛选差异表达基因(DEGs)。采用加权基因共表达网络分析(WGCNA)来探索与AMI相关的关键模块。接下来,利用GO、KEGG和PPI网络分析AMI病理状态的主要作用。使用三种机器学习算法选择和识别诊断性生物标志物。此外,在验证队列(n = 56)中验证了hub基因的表达和诊断效率。结果共鉴定出366个基因,其中上调20个,下调306个。在粉红色和绿松石色模块中,共有276个交叉基因与AMI显著相关。基于多种机器学习算法和独立验证,确定了LILRA1、CCL20、IL1R2、TYROBP、CXCL16和NFKBIA 6个基因为枢纽基因,在发现队列和验证队列中均表现出满意的诊断效率。结论我们的数据为AMI的病理生理学提供了六个中心基因的支持,并提出了它们作为AMI早期诊断的候选生物标志物。
Early diagnosis of acute myocardial infarction via hub genes identified by integrated weighted gene co-expression network analysis
Background
Acute myocardial infarction (AMI) is a leading cause of morbidity and mortality worldwide. Circulating endothelial cells (CECs) have been reported to be involved with the early stages of AMI. The specific objective of our study was to discover early diagnostic markers of CECs in circulation using bioinformatics analysis.
Methods
Raw microarray data of the GSE66360 dataset were acquired from the Gene Expression Omnibus (GEO) database. The R software was used to filtrate differentially expressed genes (DEGs) from the discovery cohort of GSE66360 (n = 43). A weighted gene co-expression network analysis (WGCNA) was performed to explore the key modules connected with AMI. Next, main roles of the pathological states in AMI were analyzed using GO and KEGG and PPI networks. Diagnostic biomarkers were selected and identified using three machine learning algorithms. Additionally, the expression and diagnostic efficiency of hub genes were verified in the validation cohort (n = 56).
Results
366 DEGs were identified (20 upregulated and 306 downregulated). A total of 276 intersecting genes were markedly associated with AMI in the pink and turquoise modules. Based on multiple machine learning algorithms and independent validation, six genes including LILRA1, CCL20, IL1R2, TYROBP, CXCL16 and NFKBIA were identified as hub genes and showed satisfactory diagnostic efficiency both in the discovery cohort and validation cohort.
Conclusion
Our data provides evidence supporting a list of six hub genes to be trapped in the pathophysiology of AMI and proposes them as candidate biomarkers for the early diagnosis of AMI.