Evidence map›Paper›PMID 39552895›Full record

ArticleJournal of thoracic disease2024

Integrated bioinformatics and machine learning algorithms reveal the unfolded protein response pathways and immune infiltration in acute myocardial infarction.

Yang Bai, Zequn Niu, Zhenyu Yang, Yi Sun, Weidong Yan, Anshi Wu, Changwei Wei

Abstract read
In one paragraph

Article in Journal of thoracic disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Yang BaiDepartment of Anesthesiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Zequn NiuComputer Science and Technology, The Open University of China, Beijing, China.
Zhenyu YangDepartment of Endocrinology, South China Hospital of Shenzhen University, Shenzhen, China.
Yi SunDepartment of Anesthesiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Weidong YanDepartment of Anesthesiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Anshi WuDepartment of Anesthesiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.
Changwei WeiDepartment of Anesthesiology, Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The unfolded protein response (UPR) is a critical biological process related to a variety of physiological functions and cardiac disease. However, the role of UPR-related genes in acute myocardial infarction (AMI) has not been well characterized. Therefore, this study aims to elucidate the mechanism and role of the UPR in the context of AMI. Methods: Gene expression profiles related to AMI and UPR pathway were downloaded from the Gene Expression Omnibus database and PathCards database, respectively. Differentially expressed genes (DEGs) were identified and then functionally annotated. The random forest (RF) and least absolute shrinkage and selection operator (LASSO) regression analysis were conducted to identify potential diagnostic UPR-AMI biomarkers. Furthermore, the results were validated by using external data sets, and discriminability was measured by the area under the curve (AUC). A nomogram based on the feature genes was developed to predict the AMI-risk rate. Then we utilized two algorithms, CIBERSORT and MCPcounter, to investigate the relationship between the key genes and immune microenvironment. Additionally, we performed uniform clustering of AMI samples based on the expression of UPR pathway-related genes. The weighted gene co-expression network analysis was conducted to identify the key modules in various clusters, enrichment analysis was performed for the genes existing in different modules. Results: A total of 14 DEGs related to the UPR pathway were identified. Among the 14 DEGs, Conclusions: Our study highlights the importance of the UPR pathway in the pathogenesis of myocardial infarction, and identifies four genes

Indexed as

acute myocardial infarction (AMI)bioinformaticsimmune cell infiltrationUnfolded protein response (UPR)weighted gene coexpression network analysis (WGCNA)

Identifiers

PMID39552895
PMCPMC11565340

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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.