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基于集成机器学习的肥厚型心肌病诊断基因生物标志物预测。

Prediction of diagnostic gene biomarkers for hypertrophic cardiomyopathy by integrated machine learning.

机构信息

Department of Cardiovascular Medicine, Shaanxi Provincial People's Hospital, Xi'an, Shaanxi, China.

出版信息

J Int Med Res. 2023 Nov;51(11):3000605231213781. doi: 10.1177/03000605231213781.

Abstract

OBJECTIVES

Hypertrophic cardiomyopathy (HCM), a leading cause of heart failure and sudden death, requires early diagnosis and treatment. This study investigated the underlying pathogenesis and explored potential diagnostic gene biomarkers for HCM.

METHODS

Transcriptional profiles of myocardial tissues from patients with HCM (dataset GSE36961) were downloaded from the Gene Expression Omnibus database and subjected to bioinformatics analyses, including differentially expressed gene (DEG) identification, enrichment analyses, and protein-protein interaction (PPI) network analysis. Least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination were performed to identify candidate diagnostic gene biomarkers. mRNA expression levels of candidate biomarkers were tested in an external dataset (GSE141910); area under the receiver operating characteristic curve (AUC) values were obtained to validate diagnostic efficacy.

RESULTS

Overall, 156 DEGs (109 downregulated, 47 upregulated) were identified. Enrichment and PPI network analyses indicated that the DEGs were involved in biological functions and molecular pathways including inflammatory response, platelet activity, complement and coagulation cascades, extracellular matrix organization, phagosome, apoptosis, and VEGFA-VEGFR2 signaling. RASD1, CDC42EP4, MYH6, and FCN3 were identified as diagnostic biomarkers for HCM.

CONCLUSIONS

RASD1, CDC42EP4, MYH6, and FCN3 might be diagnostic gene biomarkers for HCM and can provide insights concerning HCM pathogenesis.

摘要

目的

肥厚型心肌病(HCM)是心力衰竭和猝死的主要原因,需要早期诊断和治疗。本研究探讨了 HCM 的潜在发病机制,并探索了潜在的诊断基因生物标志物。

方法

从基因表达综合数据库(GEO)中下载 HCM 患者心肌组织的转录谱数据集(GSE36961),并进行生物信息学分析,包括差异表达基因(DEG)鉴定、富集分析和蛋白质-蛋白质相互作用(PPI)网络分析。采用最小绝对收缩和选择算子(LASSO)回归和支持向量机递归特征消除来鉴定候选诊断基因生物标志物。在外部数据集(GSE141910)中测试候选生物标志物的 mRNA 表达水平;获得受试者工作特征曲线(ROC)下面积(AUC)值以验证诊断效能。

结果

共鉴定出 156 个 DEG(109 个下调,47 个上调)。富集和 PPI 网络分析表明,这些 DEG 参与了包括炎症反应、血小板活性、补体和凝血级联、细胞外基质组织、吞噬体、细胞凋亡和 VEGFA-VEGFR2 信号通路在内的生物学功能和分子途径。RASD1、CDC42EP4、MYH6 和 FCN3 被鉴定为 HCM 的诊断生物标志物。

结论

RASD1、CDC42EP4、MYH6 和 FCN3 可能是 HCM 的诊断基因生物标志物,可为 HCM 的发病机制提供新的见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/39a2/10683566/5dc496771be5/10.1177_03000605231213781-fig1.jpg

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