Evidence map›Paper›PMID 39931851›Full record

ArticleCombinatorial chemistry & high throughput screening2026

Identification of NR4A2 as a Potential Predictive Biomarker for Atherosclerosis.

Lebin Yuan, Ruru Bai, Xinhao Han, Jiajia Xiang

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Article in Combinatorial chemistry & high throughput screening, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Lebin YuanDepartment of Nail and Breast Surgery, Affiliated Xiangyang Central Hospital of Hubei University of Arts and Science, Xiangyang Center Hospital, Xiangyang, Hubei, China.
Ruru BaiJiangxi Province Key Laboratory of Molecular Medicine, the 2nd affiliated hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Xinhao HanInstitute of Geriatrics, Jiangxi Provincial People's Hospital Affiliated to Nanchang University, Nanchang, Jiangxi, China.
Jiajia XiangJiangxi Province Key Laboratory of Molecular Medicine, the 2nd affiliated hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

INTRODUCTION/

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

methodsThe 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.

resultsOur 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.

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

Indexed as

AtherosclerosisNuclear Receptor Subfamily 4, Group A, Member 2BiomarkersDatabases, GeneticHumansMachine LearningBiomarkersNR4A2 protein, humanNuclear Receptor Subfamily 4, Group A, Member 2Atherosclerosisbiomarkercardio-cerebrovascular diseaseDEGsGEONR4A2

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.