ArticleBMC cardiovascular disorders2023
Uncovering potential diagnostic biomarkers of acute myocardial infarction based on machine learning and analyzing its relationship with immune cells.
Article in BMC cardiovascular disorders, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 9 citations in OpenAlex.
- An exploratory study of high-throughput transcriptomic analysis reveals novel mRNA biomarkers for acute myocardial infarction using integrated methods.Scientific reports · 2025Article
- Machine Learning Applications in Acute Coronary Syndrome: Diagnosis, Outcomes and Management.Advances in therapy · 2025Review
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundAcute myocardial infarction (AMI) is a common cardiovascular disease. This study aimed to mine biomarkers associated with AMI to aid in clinical diagnosis and management.
methodsAll mRNA and miRNA data were downloaded from public database. Differentially expressed mRNAs (DEmRNAs) and differentially expressed miRNAs (DEmiRNAs) were identified using the metaMA and limma packages, respectively. Functional analysis of the DEmRNAs was performed. In order to explore the relationship between miRNA and mRNA, we construct miRNA-mRNA negative regulatory network. Potential biomarkers were identified based on machine learning. Subsequently, ROC and immune correlation analysis were performed on the identified key DEmRNA biomarkers.
resultsAccording to the false discovery rate < 0.05, 92 DEmRNAs and 272 DEmiRNAs were identified. GSEA analysis found that kegg_peroxisome was up-regulated in AMI and kegg_steroid_hormone_biosynthesis was down-regulated in AMI compared to normal controls. 5 key DEmRNA biomarkers were identified based on machine learning, and classification diagnostic models were constructed. The random forests (RF) model has the highest accuracy. This indicates that RF model has high diagnostic value and may contribute to the early diagnosis of AMI. ROC analysis found that the area under curve of 5 key DEmRNA biomarkers were all greater than 0.7. Pearson correlation analysis showed that 5 key DEmRNA biomarkers were correlated with most of the differential infiltrating immune cells.
conclusionThe identification of new molecular biomarkers provides potential research directions for exploring the molecular mechanism of AMI. Furthermore, it is important to explore new diagnostic genetic biomarkers for the diagnosis and treatment of AMI.
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Registered trials
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