Evidence map›Paper›PMID 37928399›Full record

ArticleHeliyon2023

Evaluation the role of cuproptosis-related genes in the pathogenesis, diagnosis and molecular subtypes identification of atherosclerosis.

Mengxi Wang, Liying Cheng, Qian Xiang, Ziwei Gao, Yuhan Ding, Haitao Xie, Xiaohu Chen, Peng Yu, Le Shen

Open access · goldAbstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
2.9field-weighted citation impact, top 9% 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

6 citing papers in PubMed, 12 citations in OpenAlex.

  1. Distinct types of regulated cell death in atherosclerosis.Journal of pharmaceutical analysis · 2026
    Review
  2. Article
  3. Review
  4. Review
  5. Cuproptosis and copper deficiency in ischemic vascular injury and repair.Apoptosis : an international journal on programmed cell death · 2024
    Review
  6. Article
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

9 authors at 2 institutions in 1 country.

Mengxi WangDepartment of Cardiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China.
Liying ChengState Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
Qian XiangDepartment of Cardiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China.
Ziwei GaoDepartment of Cardiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China.
Yuhan DingDepartment of Cardiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China.
Haitao XieDepartment of Cardiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China.
Xiaohu ChenDepartment of Cardiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China.
Peng YuDepartment of Cardiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China.
Le ShenDepartment of Cardiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, China.
Nanjing University of Chinese Medicine · CNTianjin University of Traditional Chinese Medicine · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: At present, the pathogenesis of atherosclerosis has not been fully elucidated, and the diagnosis and treatment face great challenges. Cuproptosis is a novel cell death pattern that might be involved in the development of atherosclerosis. However, no research has reported the correlation between cuproptosis and atherosclerosis. Methods: The differential cuproptosis-related genes (CRGs) between atherosclerosis group and control group (A-CRGs) were discovered via differential expression analysis. The correlation analysis, PPI network analysis, GO, KEGG and GSEA analysis were performed to investigate the function of A-CRGs. The differences of biological function between atherosclerosis group and control group were investigated via immune infiltration analysis and GSVA. The LASSO regression, nomogram and machine learning models were constructed to predict atherosclerosis risk. The atherosclerosis molecular subtypes clusters were discovered via unsupervised cluster analysis. Subsequently, we used the above research methods to analyze the differential CRGs between clusters (M-CRGs) and evaluate the molecular subtypes identification performance of M-CRGs. Finally, we verified the diagnostic value for atherosclerosis and role in cuproptosis of these CRGs through the validation set and in vitro experiments. Results: Five A-CRGs were identified and they were mainly related to the biological function of copper ion metabolism and immune inflammatory response. The diagnostic models and nomogram of atherosclerosis based on 5 A-CRGs indicated that these genes had well diagnostic value. A total of two molecular subtypes clusters were obtained in the atherosclerosis group. There were many differences in biological functions between these two molecular subtypes clusters, such as mitochondrial outer membrane permeabilization and primary immunodeficiency. In addition, 3 M-CRGs were identified in the 2 clusters. Machine learning models and nomogram constructed based on M-CRGs showed that these genes had well molecular subtypes identification efficacy. In the end, the results of in vitro experiment and validation set confirmed the diagnostic value for atherosclerosis and role in cuproptosis of these genes. Conclusion: The cuproptosis may be a potential pathogenesis of atherosclerosis and CRGs may be promising markers for the diagnosis and molecular subtypes identification of atherosclerosis.

Indexed as

AtherosclerosisCuproptosisDiagnosisMolecular subtypesPathogenesis

Identifiers

PMID37928399
PMCPMC10622704
OpenAlexW4387747231

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.