Key result
Diagnostic model combining S100A9 and SOCS3 detects AMI with up to ~0.97 AUC.
Why the study?
Diagnostic biomarkers frequently used for AMI in current clinical practice have limitations in sensitivity and specificity, potentially leading to delayed diagnosis.
Do S100A9 and SOCS3 serve as accurate diagnostic biomarkers for acute myocardial infarction?
Observational (n=362)
Do S100A9 and SOCS3 serve as accurate diagnostic biomarkers for acute myocardial infarction?
Effect estimate: AUC 0.973
S100A9 and SOCS3 are identified as potential diagnostic biomarkers for acute myocardial infarction with high accuracy in public datasets, and are associated with immune infiltration.
Warrants prospective validation before clinical use; extends transcriptomic biomarker research for acute myocardial infarction.
Acute myocardial infarction (AMI) is one of the leading causes of death globally, with a mortality rate of over 20%. However, the diagnostic biomarkers frequently used in current clinical practice have limitations in both sensitivity and specificity, likely resulting in delayed diagnosis. This study aimed to identify potential diagnostic biomarkers for AMI and explored the possible mechanisms involved. Datasets were retrieved from the Gene Expression Omnibus. First, we identified differentially expressed genes (DEGs) and preserved modules, from which we identified candidate genes by LASSO (least absolute shrinkage and selection operator) regression and the SVM-RFE (support vector machine-recursive feature elimination) algorithm. Subsequently, we used ROC (receiver operating characteristic) analysis to evaluate the diagnostic accuracy of the candidate genes. Thereafter, functional enrichment analysis and an analysis of immune infiltration were implemented. Finally, we assessed the association between biomarkers and biological processes, infiltrated cells, clinical traits, tissues and time points. We identified nine preserved modules containing 1,016 DEGs and managed to construct a diagnostic model with high accuracy (GSE48060: AUC = 0.923; GSE66360: AUC = 0.973) incorporating two genes named S100A9 and SOCS3. Functional analysis revealed the pivotal role of inflammation; immune infiltration analysis indicated that eight cell types (monocytes, epithelial cells, neutrophils, CD8+ T cells, Th2 cells, NK cells, NKT cells and platelets) were likely involved in AMI. Furthermore, we observed that S100A9 and SOCS3 were correlated with inflammation, variably infiltrated cells, clinical traits of patients, sampling tissues and sampling time points. In conclusion, we suggested S100A9 and SOCS3 as diagnostic biomarkers of AMI and discovered their association with inflammation, infiltrated immune cells and other factors.
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Lin et al. (2022) conducted an observational in Acute myocardial infarction (n=362). S100A9 and SOCS3 diagnostic model vs. Normal controls was evaluated on Diagnostic accuracy (AUC) of the combined S100A9 and SOCS3 model in the GSE66360 training dataset (AUC 0.973). A diagnostic model incorporating S100A9 and SOCS3 demonstrated high accuracy for detecting acute myocardial infarction, achieving AUCs of 0.923 and 0.973 in the training datasets.
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