Evidence map›Paper›PMID 40857258›Full record

ArticlePloS one2025

Immunophenotyping identifies key immune biomarkers for coronary artery disease through machine learning.

Lelin Jiang, Minghao Jiang, Yiying Liu, Wei Zhao, Xinlang Zhou, Ying Liu, Shue Huang, Lina Chen, Wenbing Jiang

Abstract read
In one paragraph

Article in PloS one, 2025. 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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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Lelin JiangDepartment of Clinical Medicine, The Second Clinical Medical College of Wenzhou Medical University, Wenzhou, Zhejiang, China.ORCID https://orcid.org/0009-0000-6772-9419
Minghao JiangDepartment of Surgery, The Second Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.
Yiying LiuWenzhou Central Hospital, Wenzhou, Zhejiang, China.
Wei ZhaoDepartment of Cardiology, The Third Clinical Institute Affiliated to Wenzhou Medical University, Wenzhou, Zhejiang, China.
Xinlang ZhouDepartment of Cardiology, Wenzhou Integrated Traditional Chinese and Western Medicine Hospital, Wenzhou, Zhejiang, China.
Ying LiuDepartment of Cardiology, Wenzhou Integrated Traditional Chinese and Western Medicine Hospital, Wenzhou, Zhejiang, China.
Shue HuangDepartment of Cardiology, Wenzhou Integrated Traditional Chinese and Western Medicine Hospital, Wenzhou, Zhejiang, China.
Lina ChenDepartment of Cardiology, The Central Affiliated Hospital, Shaoxing University, Shaoxing, Zhejiang, China.
Wenbing JiangDepartment of Cardiology, Wenzhou Integrated Traditional Chinese and Western Medicine Hospital, Wenzhou, Zhejiang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionThe differences among immune subtypes in coronary artery disease (CAD), their interrelationships, and the associated immune biomarkers remain incompletely understood.

methodsThe samples were collected from the GSE20686 and GSE42148 datasets for analysis. Principal component analysis (PCA) and Gene Set Variation Analysis (GSVA) were performed on the subtypes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to determine functional and pathways in CAD. Machine learning models were constructed for CAD prediction. Model validation was performed using GSE56885 and GSE71226 datasets. The expression and function of the identified genes were evaluated using immunohistochemistry, CCK-8 assays, wound healing assays, and Transwell invasion assays.

resultsMultiple immune cells showed correlations with CAD samples. Two immune cell subtypes were identified, with significant differences in programmed cell death-ligand (PD-L1) expression, immune scores, and stromal scores between subtypes (P < 0.05). Three CAD hub genes were identified by WGCNA. GO analysis revealed enrichment in Biological Process (BP) and Molecular Function (MF). Among the several machine learning models, the RF model was selected based on combining parameters. The model mainly included two CAD immune marker genes, AKT1 and PTK2B. Differential expression of AKT1 and PTK2B was observed in cardiac myocytes. Inhibition of PTK2B suppressed cell proliferation and invasion, and induced apoptosis in HUVEC cells.

conclusionImmunophenotyping revealed an association between CAD and PD-L1. AKT1 and PTK2B were identified as key disease signature genes, which may hold clinical significance for the diagnosis, prognostic assessment and treatment of CAD.

Indexed as

Coronary Artery DiseaseImmunophenotypingMachine LearningB7-H1 AntigenBiomarkersFocal Adhesion Kinase 1Gene OntologyHumansB7-H1 AntigenBiomarkersCD274 protein, humanFocal Adhesion Kinase 1PTK2 protein, human

Identifiers

PMID40857258
PMCPMC12380355

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