ArticleJournal of inflammation research2022
Identification of Immune-Related Genes in Patients with Acute Myocardial Infarction Using Machine Learning Methods.
Article in Journal of inflammation research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 25 citations in OpenAlex.
- Integrative transcriptomic analysis and experimental validation identify GPR97 and PROK2 as novel genes upregulated in acute myocardial infarction.Functional & integrative genomics · 2026Article
- Machine learning-derived identification of an obesity and lipid metabolism-related genes signature for the diagnosis and molecular typing of acute myocardial infarction.Frontiers in cardiovascular medicine · 2026Article
- Identification of neutrophil extracellular traps-related genes for the diagnosis of acute myocardial infarction based on bioinformatics and experimental verification.Journal of inflammation (London, England) · 2025Article
- Identification and validation of ANXA3 and SOCS3 as biomarkers for acute myocardial infarction related to sphingolipid metabolism.Hereditas · 2025Article
- Temporal dynamics of the multi-omic response reveals the modulation of macrophage subsets post-myocardial infarction.Journal of translational medicine · 2025Article
- Comprehensive analysis of diagnostic biomarkers related to histone acetylation in acute myocardial infarction.BMC medical genomics · 2025Article
- An exploratory study of high-throughput transcriptomic analysis reveals novel mRNA biomarkers for acute myocardial infarction using integrated methods.Scientific reports · 2025Article
- Identification of therapeutic targets for Alzheimer's Disease Treatment using bioinformatics and machine learning.Scientific reports · 2025Article
- Article
- Identification of key genes associated with acute myocardial infarction using WGCNA and two-sample mendelian randomization study.PloS one · 2024Article
- Characterization of the mFrontiers in immunology · 2024Article
- Integration of machine learning to identify diagnostic genes in leukocytes for acute myocardial infarction patients.Journal of translational medicine · 2023Article
- Article
- Identifying potential biomarkers for non-obstructive azoospermia using WGCNA and machine learning algorithms.Frontiers in endocrinology · 2023Article
- Leveraging Machine Learning Techniques to Forecast Chronic Total Occlusion before Coronary Angiography.Journal of clinical medicine · 2022Article
- Inflammation and Oxidative Stress Role of S100A12 as a Potential Diagnostic and Therapeutic Biomarker in Acute Myocardial Infarction.Oxidative medicine and cellular longevity · 2022Article
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Authors and funding
11 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: This study aimed to analyze immune-related genes and immune cell components in the peripheral blood of patients with acute myocardial infarction (AMI). Methods: Six datasets were obtained from the GEO repository comprising 88 healthy samples and 215 AMI samples. We performed the weighted gene co-expression analysis (WGCNA) and five machine learning (ML) methods to identify immune-related genes and construct diagnostic models. CIBERSORT algorithm was adopted for the assessment of the degree of immune infiltration. Finally, RT-PCR, immunofluorescence double and immunohistochemistry were conducted to analyze the expression level of the identification of featured immune-related genes and localization relationship in heart tissue of AMI mouse model. Results: A total of 496 immune-related DEGs were obtained between AMI and normal samples. WGCNA finally determined the co-expression modules that showed the most significantly positively associated with AMI (r=0.41; P<0.001). Among the five ML models, XGBoost had the highest AUC (0.849) and accuracy (0.812) to discriminate patients with AMI from normal in the validation sets. Furthermore, we found that the proportion of chemokine receptor (CCR), macrophages, neutrophils, and Treg cells in the AMI groups was significantly higher than that in the normal groups. In vitro RT-PCR verification revealed that Conclusion: Immune-related genes and immune cells are intimately related to AMI. Constructing different ML models based on these biomarkers could be a valuable approach to diagnosing AMI in clinical practice.
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