Evidence map›Paper›PMID 36211422›Full record

ArticleFrontiers in immunology2022

Identification of immune cell infiltration and diagnostic biomarkers in unstable atherosclerotic plaques by integrated bioinformatics analysis and machine learning.

Jing Wang, Zijian Kang, Yandong Liu, Zifu Li, Yang Liu, Jianmin Liu

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
70citing papers in PubMed, 1 pooled it
11.3field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

70 citing papers in PubMed, 1 synthesis or guideline pooled it, 107 citations in OpenAlex.

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  7. CD8Nature reviews. Cardiology · 2026
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10 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Jing WangNeurovascular Center, Changhai Hospital, Naval Medical University, Shanghai, China.
Zijian KangDepartment of Rheumatology and Immunology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.
Yandong LiuDepartment of Geriatrics, Navy 905th Hospital, Shanghai, China.
Zifu LiNeurovascular Center, Changhai Hospital, Naval Medical University, Shanghai, China.
Yang LiuDepartment of Critical Care Medicine, Naval Medical Center of People's Liberation Army of China (PLA), Shanghai, China.
Jianmin LiuNeurovascular Center, Changhai Hospital, Naval Medical University, Shanghai, China.
Second Military Medical University · CNShanghai Changzheng Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: The decreased stability of atherosclerotic plaques increases the risk of ischemic stroke. However, the specific characteristics of dysregulated immune cells and effective diagnostic biomarkers associated with stability in atherosclerotic plaques are poorly characterized. This research aims to investigate the role of immune cells and explore diagnostic biomarkers in the formation of unstable plaques for the sake of gaining new insights into the underlying molecular mechanisms and providing new perspectives for disease detection and therapy. Method: Using the CIBERSORT method, 22 types of immune cells between stable and unstable carotid atherosclerotic plaques from RNA-sequencing and microarray data in the public GEO database were quantitated. Differentially expressed genes (DEGs) were further calculated and were analyzed for enrichment of GO Biological Process and KEGG pathways. Important cell types and hub genes were screened using machine learning methods including least absolute shrinkage and selection operator (LASSO) regression and random forest. Single-cell RNA sequencing and clinical samples were further used to validate critical cell types and hub genes. Finally, the DGIdb database of gene-drug interaction data was utilized to find possible therapeutic medicines and show how pharmaceuticals, genes, and immune cells interacted. Results: A significant difference in immune cell infiltration was observed between unstable and stable plaques. The proportions of M0, M1, and M2 macrophages were significantly higher and that of CD8 Conclusion: M1 macrophages is an important cause of unstable plaque formation, and CD68, PAM, and IGFBP6 could be used as diagnostic markers to identify unstable plaques effectively.

Indexed as

InsulinsPlaque, AtheroscleroticApolipoproteins EBiomarkersCD8-Positive T-LymphocytesComputational BiologyFatty Acid-Binding ProteinsHLA-DR alpha-ChainsHumansIndomethacinMachine LearningMatrix Metalloproteinase 9NivolumabPharmaceutical PreparationsRNAApolipoproteins EBiomarkersFatty Acid-Binding ProteinsHLA-DR alpha-ChainsIndomethacinInsulinsMatrix Metalloproteinase 9NivolumabPharmaceutical PreparationsRNAbiomarkercarotid artery atherosclerosisimmune infiltrationmachine learningunstable atherosclerosis

Identifiers

PMID36211422
PMCPMC9537477
OpenAlexW4296875092

What OpenQuestion holds

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Registered trials

None linked

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.