Identification of Biomarkers Associated with Oxidative Stress in Aortic Dissection Based on Bulk Transcriptome Analyses.

IF 2.1 4区 医学 Q2 MEDICINE, GENERAL & INTERNAL
International Journal of General Medicine Pub Date : 2024-11-28 eCollection Date: 2024-01-01 DOI:10.2147/IJGM.S478146
Zhenghao Li, Changying Li, Yue Shao, Haoyu Ran, Haoming Shi, Ruiqin Zhou, Xuanyu Liu, Qingchen Wu, Cheng Zhang
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引用次数: 0

Abstract

Purpose: The aim of this study is to investigate the underlying molecular mechanism of oxidative stress (OS) involved in aortic dissection (AD).

Methods: Datasets of AD and OS-related genes were obtained from the Gene Expression Omnibus (GEO) and the GeneCards database, respectively. Differential expression analysis and weighted gene correlation network analysis (WGCNA) were employed to screen genes. After enrichment analysis, a protein-protein interaction (PPI) network was constructed, and machine learning algorithms were used to determine signature genes. Comprehensive bioinformatics analyses on the signature genes were executed, and a clinical prediction model was established and evaluated. External datasets, in vitro experiment, and Mendelian randomization (MR) analysis were applied to validation.

Results: We identified CCL2, ITGB4, MYC, SOCS3, SPP1 and TEK as OS-related signature genes in AD. The area under the ROC curve of all the signature genes was greater than 0.75. The clinical prediction model based on the signature genes showed satisfactory diagnostic efficacy in both training and validation cohorts. In validation cohort and in vitro experiment, CCL2, MYC, SPP1 and TEK were further validated. However, the MR results showed no causal association between the expression of the signature genes and AD.

Conclusion: This study demonstrated that OS participates in and affects the progression of AD. Six biomarkers associated with OS could be perceived as crucial targets for the diagnosis and treatment of AD.

基于大量转录组分析的主动脉夹层氧化应激相关生物标志物鉴定
目的:探讨氧化应激(OS)参与主动脉夹层(AD)的潜在分子机制。方法:分别从Gene Expression Omnibus (GEO)和GeneCards数据库中获取AD和os相关基因数据集。采用差异表达分析和加权基因相关网络分析(WGCNA)筛选基因。在富集分析后,构建蛋白质-蛋白质相互作用(PPI)网络,并使用机器学习算法确定特征基因。对特征基因进行全面的生物信息学分析,建立临床预测模型并进行评估。采用外部数据集、体外实验和孟德尔随机化(MR)分析进行验证。结果:我们鉴定出CCL2、ITGB4、MYC、SOCS3、SPP1和TEK是AD中os相关的特征基因。所有特征基因的ROC曲线下面积均大于0.75。基于特征基因的临床预测模型在训练组和验证组均显示出满意的诊断效果。在验证队列和体外实验中,进一步验证CCL2、MYC、SPP1和TEK。然而,MR结果显示特征基因的表达与AD之间没有因果关系。结论:本研究表明OS参与并影响AD的进展。与OS相关的六种生物标志物可被视为AD诊断和治疗的关键靶点。
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来源期刊
International Journal of General Medicine
International Journal of General Medicine Medicine-General Medicine
自引率
0.00%
发文量
1113
审稿时长
16 weeks
期刊介绍: The International Journal of General Medicine is an international, peer-reviewed, open access journal that focuses on general and internal medicine, pathogenesis, epidemiology, diagnosis, monitoring and treatment protocols. The journal is characterized by the rapid reporting of reviews, original research and clinical studies across all disease areas. A key focus of the journal is the elucidation of disease processes and management protocols resulting in improved outcomes for the patient. Patient perspectives such as satisfaction, quality of life, health literacy and communication and their role in developing new healthcare programs and optimizing clinical outcomes are major areas of interest for the journal. As of 1st April 2019, the International Journal of General Medicine will no longer consider meta-analyses for publication.
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