Machine Learning Revealed Ferroptosis Features and a Novel Ferroptosis-Based Classification for Diagnosis in Acute Myocardial Infarction

Abstract

Acute myocardial infarction (AMI) is a leading cause of death and disability worldwide. Early diagnosis of AMI and interventional treatment can significantly reduce myocardial damage. However, owing to limitations in sensitivity and specificity, existing myocardial markers are not efficient for early identification of AMI. Transcriptome-wide association studies (TWASs) have shown excellent performance in identifying significant gene–trait associations and several cardiovascular diseases (CVDs). Furthermore, ferroptosis is a major driver of ischaemic injury in the heart. However, its specific regulatory mechanisms remain unclear. In this study, we screened three Gene Expression Omnibus (GEO) datasets of peripheral blood samples to assess the efficiency of ferroptosis-related genes (FRGs) for early diagnosis of AMI. To the best of our knowledge, for the first time, TWAS and mRNA expression data were integrated in this study to identify 11 FRGs specifically expressed in the peripheral blood of patients with AMI. Subsequently, using multiple machine learning algorithms, an optimal prediction model for AMI was constructed, which demonstrated satisfactory diagnostic efficiency in the training cohort (area under the curve (AUC) = 0.794) and two external validation cohorts (AUC = 0.745 and 0.711). Our study suggests that FRGs are involved in the progression of AMI, thus providing a new direction for early diagnosis, and offers potential molecular targets for optimal treatment of AMI.

Inflammation and Oxidative Stress Role of S100A12 as a Potential Diagnostic and Therapeutic Biomarker in Acute Myocardial Infarction

“…As a serious heart disease, acute myocardial infarction (AMI) has the characteristics of high mortality and a poor prognosis; and it is easy to induce severe cardiovascular events, such as ventricular remodeling and heart failure, which seriously threaten people's lives and cause great pain and financial burden in patients [1][2][3]. The early diagnosis and treatment of AMI are essential for reducing myocardial injury and malignant consequences, reducing mortality and improving patient prognosis to some extents [4][5][6]. Currently, the levels of myocardial enzyme (CKMB) and cardiac troponin I (cTnI) are still the gold standards for the clinical diagnosis of AMI, but they are not specific for AMI.…”

Revealing the novel ferroptosis-related therapeutic targets for diabetic foot ulcer based on the machine learning

“…The R packages “glmnet,” “caret,” “Boruta” and “XGBoost” were used to build a machine learning model ( Huang et al, 2022 ). The least absolute shrinkage and selection operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), Boruta, and extreme gradient boosting (XGBoost) analyses were performed on the entire dataset to screen for key ferroptosis-related genes.…”

Roles of Ferroptosis in Cardiovascular Diseases

“…Inhibition of ferroptosis has been provide novel tactics for the precise treatment of myocardial infarction. Meanwhile, Through machine learning, Huang et al ( 48 ) filtered out ferroptosis-related genes (FRGs) specifically expressed in the peripheral blood of AMI patients. In this study, they also proposed a diagnostic model composed of mitogen-activated protein kinase 3 (MAPK3), WD repeat domain phosphoinositide-interacting protein 2 (WIPI2) and voltage-dependent anion channel three (VDAC3) and provided a new direction for early diagnosis of AMI.…”