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预测心肌梗死未来风险的缺氧相关基因:一项基于GEO数据库的研究

Hypoxia-associated genes predicting future risk of myocardial infarction: a GEO database-based study.

作者信息

Li Shaohua, Zhang Junwen, Ni Jingwei, Cao Jiumei

机构信息

Department of Cardiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China.

Department of Cardiothoracic Surgery, Xinhua Hospital Affiliated to Shanghai Jiaotong University School of Medicine, Shanghai, China.

出版信息

Front Cardiovasc Med. 2023 Jul 3;10:1068782. doi: 10.3389/fcvm.2023.1068782. eCollection 2023.

Abstract

BACKGROUND

Patients with unstable angina (UA) are prone to myocardial infarction (MI) after an attack, yet the altered molecular expression profile therein remains unclear. The current work aims to identify the characteristic hypoxia-related genes associated with UA/MI and to develop a predictive model of hypoxia-related genes for the progression of UA to MI.

METHODS AND RESULTS

Gene expression profiles were obtained from the GEO database. Then, differential expression analysis and the WGCNA method were performed to select characteristic genes related to hypoxia. Subsequently, all 10 hypoxia-related genes were screened using the Lasso regression model and a classification model was established. The area under the ROC curve of 1 shows its excellent classification performance and is confirmed on the validation set. In parallel, we construct a nomogram based on these genes, showing the risk of MI in patients with UA. Patients with UA and MI had their immunological status determined using CIBERSORT. These 10 genes were primarily linked to B cells and some inflammatory cells, according to correlation analysis.

CONCLUSION

Overall, GWAS identified that the CSTF2F UA/MI risk gene promotes atherosclerosis, which provides the basis for the design of innovative cardiovascular drugs by targeting CSTF2F.

摘要

背景

不稳定型心绞痛(UA)患者发作后易发生心肌梗死(MI),但其分子表达谱的改变尚不清楚。目前的工作旨在识别与UA/MI相关的特征性缺氧相关基因,并建立一个用于预测UA进展为MI的缺氧相关基因模型。

方法与结果

从GEO数据库获取基因表达谱。然后,进行差异表达分析和加权基因共表达网络分析(WGCNA)方法以选择与缺氧相关的特征基因。随后,使用套索回归模型筛选出所有10个缺氧相关基因并建立分类模型。ROC曲线下面积为1显示其具有出色的分类性能,并在验证集上得到证实。同时,我们基于这些基因构建了一个列线图,显示UA患者发生MI的风险。使用CIBERSORT确定UA和MI患者的免疫状态。根据相关性分析,这10个基因主要与B细胞和一些炎症细胞相关。

结论

总体而言,全基因组关联研究(GWAS)确定CSTF2F是UA/MI风险基因,可促进动脉粥样硬化,这为通过靶向CSTF2F设计创新心血管药物提供了依据。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/eff6/10351911/2971c59886ba/fcvm-10-1068782-g001.jpg

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