AQP9 and SOCS3 gene expression in leukocytes demonstrated high diagnostic value for acute myocardial infarction, with an area under the curve greater than 0.9 in both training and testing sets.
Observational (n=260)
No
Do leukocyte transcriptional RNA markers AQP9 and SOCS3 accurately diagnose acute myocardial infarction compared to stable coronary artery disease?
Leukocyte transcriptional RNA markers AQP9 and SOCS3, identified via machine learning, show high diagnostic accuracy for acute myocardial infarction and correlate with disease severity.
Effect estimate: AUC > 0.9
BACKGROUND: Acute myocardial infarction (AMI) has two clinical characteristics: high missed diagnosis and dysfunction of leukocytes. Transcriptional RNA on leukocytes is closely related to the course evolution of AMI patients. We hypothesized that transcriptional RNA in leukocytes might provide potential diagnostic value for AMI. Integration machine learning (IML) was first used to explore AMI discrimination genes. The following clinical study was performed to validate the results. METHODS: A total of four AMI microarrays (derived from the Gene Expression Omnibus) were included in bioanalysis (220 sample size). Then, the clinical validation was finished with 20 AMI and 20 stable coronary artery disease patients (SCAD). At a ratio of 5:2, GSE59867 was included in the training set, while GSE60993, GSE62646, and GSE48060 were included in the testing set. IML was explicitly proposed in this research, which is composed of six machine learning algorithms, including support vector machine (SVM), neural network (NN), random forest (RF), gradient boosting machine (GBM), decision trees (DT), and least absolute shrinkage and selection operator (LASSO). IML had two functions in this research: filtered optimized variables and predicted the categorized value. Finally, The RNA of the recruited patients was analyzed to verify the results of IML. RESULTS: Thirty-nine differentially expressed genes (DEGs) were identified between controls and AMI individuals from the training sets. Among the thirty-nine DEGs, IML was used to process the predicted classification model and identify potential candidate genes with overall normalized weights > 1. Finally, two genes (AQP9 and SOCS3) show their diagnosis value with the area under the curve (AUC) > 0.9 in both the training and testing sets. The clinical study verified the significance of AQP9 and SOCS3. Notably, more stenotic coronary arteries or severe Killip classification indicated higher levels of these two genes, especially SOCS3. These two genes correlated with two immune cell types, monocytes and neutrophils. CONCLUSION: AQP9 and SOCS3 in leukocytes may be conducive to identifying AMI patients with SCAD patients. AQP9 and SOCS3 are closely associated with monocytes and neutrophils, which might contribute to advancing AMI diagnosis and shed light on novel genetic markers. Multiple clinical characteristics, multicenter, and large-sample relevant trials are still needed to confirm its clinical value.
Zhang et al. (Fri,) conducted a observational in Acute myocardial infarction (n=260). AQP9 and SOCS3 gene expression vs. Stable coronary artery disease or healthy controls was evaluated on Diagnostic value for acute myocardial infarction (AUC > 0.9). AQP9 and SOCS3 gene expression in leukocytes demonstrated high diagnostic value for acute myocardial infarction, with an area under the curve greater than 0.9 in both training and testing sets.