Identification of NR4A2 as a Potential Predictive Biomarker for Atherosclerosis.

IF 1.6 4区医学 Q4 BIOCHEMICAL RESEARCH METHODS

Combinatorial chemistry & high throughput screening Pub Date : 2025-02-10 DOI:10.2174/0113862073357411250127080814

Lebin Yuan, Ruru Bai, Xinhao Han, Jiajia Xiang

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引用次数: 0

Abstract

Background: Atherosclerosis, a leading cause of death globally, is characterized by the buildup of immune cells and lipids in medium to large-sized arteries. However, its precise mechanism remains unclear.

Objective: The purpose of this study is to explore innovative and reliable biomarkers as a viable approach for the identification and management of atherosclerosis.

Methods: The atherosclerosis-related datasets GSE100927 and GSE66360 were retrieved from the Gene Expression Omnibus (GEO) database. The Limma package in the R programming language was utilized, applying the criteria of |logFC| > 1 and P < 0.05. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed on the 127 identified DEGs using R. Machine learning techniques were then applied to these data to explore and pinpoint potential biomarkers. The diagnostic potential of these markers was assessed via Receiver Operating Characteristic (ROC) curve analysis. Finally, Western Blot, real-time quantitative PCR (qRT-PCR), and immunohistochemistry (IHC) were employed to confirm the key biomarkers.

Results: Our research indicated that a total of 127 DEGs linked to atherosclerosis were successfully identified. Through the application of machine learning methods, eight critical genes were highlighted. Among these, Nuclear Receptor Subfamily 4 Group A Member-2 (NR4A2) emerged as the most promising marker for further investigation. CIBERSORT analysis revealed that NR4A2 expression levels were significantly correlated with multiple immune cell types, including B cells, plasma cells, and macrophages. Additional validation experiments confirmed that NR4A2 expression was indeed elevated in atherosclerotic plaques, supporting its potential as a biomarker for atherosclerosis.

Conclusion: Our study identified NR4A2 as a potential immune-related biomarker for the diagnosis and treatment of atherosclerosis.

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鉴定NR4A2作为动脉粥样硬化潜在的预测性生物标志物。

背景：动脉粥样硬化是全球死亡的主要原因之一，其特征是免疫细胞和脂质在中大型动脉中积聚。然而，其确切机制尚不清楚。目的：本研究的目的是探索创新和可靠的生物标志物作为鉴别和管理动脉粥样硬化的可行方法。方法：从Gene Expression Omnibus （GEO）数据库中检索动脉粥样硬化相关数据集GSE100927和GSE66360。采用R编程语言中的Limma包，标准为|logFC| > 1, P < 0.05。随后，使用r对127个已鉴定的deg进行基因本体（GO）和京都基因与基因组百科全书（KEGG）途径富集分析，然后将机器学习技术应用于这些数据以探索和确定潜在的生物标志物。通过受试者工作特征（ROC）曲线分析评估这些指标的诊断潜力。最后，采用Western Blot、real-time quantitative PCR （qRT-PCR）和免疫组化（immunohistochemistry， IHC）确定关键生物标志物。结果：我们的研究表明，总共127个与动脉粥样硬化相关的deg被成功识别。通过应用机器学习方法，突出了8个关键基因。其中，核受体亚家族4组A成员-2 （NR4A2）是最有希望进一步研究的标记物。CIBERSORT分析显示NR4A2表达水平与多种免疫细胞类型显著相关，包括B细胞、浆细胞和巨噬细胞。另外的验证实验证实，NR4A2在动脉粥样硬化斑块中的表达确实升高，支持其作为动脉粥样硬化生物标志物的潜力。结论：我们的研究确定NR4A2是一种潜在的免疫相关生物标志物，可用于动脉粥样硬化的诊断和治疗。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Combinatorial chemistry & high throughput screening 化学-生化研究方法

CiteScore

3.10

自引率

5.60%

发文量

327

审稿时长

7.5 months

期刊介绍： Combinatorial Chemistry & High Throughput Screening (CCHTS) publishes full length original research articles and reviews/mini-reviews dealing with various topics related to chemical biology (High Throughput Screening, Combinatorial Chemistry, Chemoinformatics, Laboratory Automation and Compound management) in advancing drug discovery research. Original research articles and reviews in the following areas are of special interest to the readers of this journal: Target identification and validation Assay design, development, miniaturization and comparison High throughput/high content/in silico screening and associated technologies Label-free detection technologies and applications Stem cell technologies Biomarkers ADMET/PK/PD methodologies and screening Probe discovery and development, hit to lead optimization Combinatorial chemistry (e.g. small molecules, peptide, nucleic acid or phage display libraries) Chemical library design and chemical diversity Chemo/bio-informatics, data mining Compound management Pharmacognosy Natural Products Research (Chemistry, Biology and Pharmacology of Natural Products) Natural Product Analytical Studies Bipharmaceutical studies of Natural products Drug repurposing Data management and statistical analysis Laboratory automation, robotics, microfluidics, signal detection technologies Current & Future Institutional Research Profile Technology transfer, legal and licensing issues Patents.