Integration of machine learning to identify diagnostic genes in leukocytes for acute myocardial infarction patients

Abstract

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.

A Comprehensive Review on Synergy of Multi-Modal Data and AI Technologies in Medical Diagnosis

“…Support vector machine (SVM). The SVM [ 9 ] is another commonly employed algorithm in disease diagnosis [ 42 , 43 , 44 ]. The SVM, introduced by Vapnik in 1990, operates on labeled data.…”

“…They used data from the GEO dataset GSE113079, achieving an AUC of 97.1% in the training set and 98.9% in the testing set. Zhang et al [ 44 ] introduced the Integration Machine Learning (IML) algorithm, incorporating a SVM, neural network (NN), RF, gradient boosting machine (GBM), decision trees (DT), and LASSO. This algorithm was applied to classify patients with Acute Myocardial Infarction (AMI) and stable coronary artery disease (SCAD), using GEO datasets GSE60993, GSE62646, GSE48060, and GSE59867, achieving an AUC over 90%.…”

Integrating Taguchi Method and Support Vector Machine for Enhanced Surface Roughness Modeling and Optimization

“…SVM has demonstrated its capability when it delivered a better performance than ANN [14]. SVM has been widely applied in various fields such as health [15]- [16], agriculture [17]- [18], geology [19], and etc. In machining field, SVM has been extensively applied in various studies for the prediction of machining performances in conventional and non-conventional machining.…”

Circulating MicroRNA-122-5p Is Associated With a Lack of Improvement in Left Ventricular Function After Transcatheter Aortic Valve Replacement and Regulates Viability of Cardiomyocytes Through Extracellular Vesicles

“…studies indicated that changes in the levels of miRNAs in the valve itself do not necessarily correlate with an alteration of their levels in the plasma. 13 Therefore, circulating miRNAs might affect mainly myocardial remodeling or other systemic dysfunction but not local pathological valvular changes. Some studies have compared the levels of circulating miRNAs in patients with AVS with those in healthy control subjects to identify potential biomarkers and therapeutic targets.…”

“…Among these circulating miRNAs, some show similar expression patterns at the myocardial level, supporting the idea of a role in the myocardium. [13][14][15] Circulating miRNAs are often encapsulated in extracellular vesicles (EVs), which are small membrane-bound vesicles secreted by cells into the extracellular space. EVs can be classified into 3 primary subtypes: exosomes, large EVs (also known as microvesicles), and apoptotic bodies.…”