ArticleJournal of translational medicine2020
Collapsing the list of myocardial infarction-related differentially expressed genes into a diagnostic signature.
Article in Journal of translational medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 20 citations in OpenAlex.
- Remimazolam attenuates myocardial ischemia/reperfusion injury by inhibiting ferroptosis involving the AKT/SLC7A11/GPX4 axis.International journal of molecular medicine · 2026Article
- KLRB1 gene in tumor immune regulation and disease prognosis: a multidimensional role review.Discover oncology · 2026Review
- Development of immune-derived molecular markers for coronary heart disease via multimachine learning.iScience · 2026Article
- Application of acute myocardial infarction-related key genes in noninvasive diagnosis: a comprehensive analysis based on transcriptome and single-cell transcriptome.Frontiers in genetics · 2026Article
- Identification and validation of feature genes of acute myocardial infarction based on ferroptosis-related genes.European journal of medical research · 2025Article
- RPS27A as a potential clock-related diagnostic biomarker for myocardial infarction: Comprehensive bioinformatics analysis and experimental validation.Clinics (Sao Paulo, Brazil) · 2025Article
- Gene Expression Signatures of Smoking and Acute Myocardial Infarction: A Blood Transcriptome Analysis.Mediators of inflammation · 2025Article
- Article
- Identification of key genes for atherosclerosis in different arterial beds.Scientific reports · 2024Article
- Atherosclerotic plaque vulnerability quantification system for clinical and biological interpretability.iScience · 2023Article
- Identification through machine learning of potential immune- related gene biomarkers associated with immune cell infiltration in myocardial infarction.BMC cardiovascular disorders · 2023Article
- Article
- Identification and validation of potential hypoxia-related genes associated with coronary artery disease.Frontiers in physiology · 2023Article
- Integrated whole-genome gene expression analysis reveals an atlas of dynamic immune landscapes after myocardial infarction.Frontiers in cardiovascular medicine · 2023Article
- Hypoxia-associated genes predicting future risk of myocardial infarction: a GEO database-based study.Frontiers in cardiovascular medicine · 2023Article
- Bioinformatics analysis identifies ferroptosis‑related genes in the regulatory mechanism of myocardial infarction.Experimental and therapeutic medicine · 2022Article
- Article
- Impact of medication on blood transcriptome reveals off-target regulations of beta-blockers.PloS one · 2022Observational
- Identification of Biomarkers Related to Immune Cell Infiltration with Gene Coexpression Network in Myocardial Infarction.Disease markers · 2021Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMyocardial infarction (MI) is one of the most severe manifestations of coronary artery disease (CAD) and the leading cause of death from non-infectious diseases worldwide. It is known that the central component of CAD pathogenesis is a chronic vascular inflammation. However, the mechanisms underlying the changes that occur in T, B and NK lymphocytes, monocytes and other immune cells during CAD and MI are still poorly understood. One of those pathogenic mechanisms might be the dysregulation of intracellular signaling pathways in the immune cells.
methodsIn the present study we performed a transcriptome profiling in peripheral blood mononuclear cells of MI patients and controls. The machine learning algorithm was then used to search for MI-associated signatures, that could reflect the dysregulation of intracellular signaling pathways.
resultsThe genes ADAP2, KLRC1, MIR21, PDGFD and CD14 were identified as the most important signatures for the classification model with L1-norm penalty function. The classifier output quality was equal to 0.911 by Receiver Operating Characteristic metric on test data. These results were validated on two independent open GEO datasets. Identified MI-associated signatures can be further assisted in MI diagnosis and/or prognosis.
conclusionsThus, our study presents a pipeline for collapsing the list of differential expressed genes, identified by high-throughput techniques, in order to define disease-associated diagnostic signatures.
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