ArticlePloS one2022
Integrated RNA gene expression analysis identified potential immune-related biomarkers and RNA regulatory pathways of acute myocardial infarction.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 15 citations in OpenAlex.
- Silent Myocardial Infarction Revisited: Immuno-metabolic Mechanisms, Multimodal Biomarkers, and Translational Diagnostics.Journal of cardiovascular translational research · 2026Review
- 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
- Deciphering the clinical implication of an obesity-related gene signature as the novel biomarker for acute myocardial infarction diagnosis.Scientific reports · 2025Article
- Comprehensive bioinformatics analytics andAging · 2024Article
- Integration of machine learning to identify diagnostic genes in leukocytes for acute myocardial infarction patients.Journal of translational medicine · 2023Article
- Article
- Identification of an 11 immune-related gene signature as the novel biomarker for acute myocardial infarction diagnosis.Genes and immunity · 2022Article
- Construction of the ceRNA network in the progression of acute myocardial infarction.American journal of cardiovascular disease · 2022Article
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1 author at 1 institution in 1 country.
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Abstract
backgroundAcute lesions are among the most important causes of death due to vascular lesions worldwide. However, there are no accurate genetic markers for Acute myocardial infarction (AMI). This project will use microarray integration analysis in bioinformatics analysis to find and validate relevant AMI gene markers.
methodsFive microarray gene expression datasets were downloaded through the GEO database. We identified 50 significant DEGs by comparing and analyzing gene expression between 92 AMI and 57 standard samples. The BioGPS database screened differentially expressed genes specific to the immune system. DEGs were mainly involved in immune-related biological processes based on Enrichment analysis. Eight hub genes and three-gene cluster modules were subsequently screened using Cytoscape and validated using Box plot's grouping comparison and ROC curves. Combined group comparison results and ROC curves analysis concluded that AQP9, IL1B, and IL1RN might be potential gene markers for the AMI process. We used the StarBase database to predict target miRNAs for eight essential genes. The expected results were used to screen and obtain target lncRNAs. Then Cytoscape was used to create CeRNA networks. By searching the literature in PubMed, we concluded that AQP9, IL1B, and IL1RN could be used as gene markers for AMI, while FSTL3-miR3303p-IL1B/IL1RN and ACSL4-miR5905p-IL1B could be used as RNA regulatory pathways affecting AMI disease progression.
conclusionsOur study identified three genes that may be potential genetic markers for AMI's early diagnosis and treatment. In addition, we suggest that FSTL3-miR-330-3p-IL1B/IL1RN and ACSL4-miR-590-5p-IL1B may be possible RNA regulatory pathways to control AMI disease progression.
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