Development and Validation of a Random Forest Diagnostic Model of Acute Myocardial Infarction Based on Ferroptosis-Related Genes in Circulating Endothelial Cells

Abstract

The high incidence and mortality of acute myocardial infarction (MI) drastically threaten human life and health. In the past few decades, the rise of reperfusion therapy has significantly reduced the mortality rate, but the MI diagnosis is still by means of the identification of myocardial injury markers without highly specific biomarkers of microcirculation disorders. Ferroptosis is a novel reported type of programmed cell death, which plays an important role in cancer development. Maintaining iron homeostasis in cells is essential for heart function, and its role in the pathological process of ischemic organ damages remains unclear. Being quickly detected through blood tests, circulating endothelial cells (CECs) have the potential for early judgment of early microcirculation disorders. In order to explore the role of ferroptosis-related genes in the early diagnosis of acute MI, we relied on two data sets from the GEO database to first detect eight ferroptosis-related genes differentially expressed in CECs between the MI and healthy groups in this study. After comparing different supervised learning algorithms, we constructed a random forest diagnosis model for acute MI based on these ferroptosis-related genes with a compelling diagnostic performance in both the validation (AUC = 0.8550) and test set (AUC = 0.7308), respectively. These results suggest that the ferroptosis-related genes might play an important role in the early stage of MI and have the potential as specific diagnostic biomarkers for MI.

Identification and external validation of the hub genes associated with cardiorenal syndrome through time-series and network analyses

“…Compared with POSTN, FN1 exhibited higher mean decreases in accuracy and Gini ( Figure 6E ), which was in agreement with the classification tree analysis. The random forest model could distinguish the CRS sample with the highest effectiveness ( Figure 6F , Supplementary Figure 1A and 1B ), which is in alignment with the results of several previous studies that have provided proof of its strong performance [ 35 – 37 ]. The accuracy, precision, recall, and F-score of each model are listed in Table 4 .…”

Construction of Novel Gene Signature-Based Predictive Model for the Diagnosis of Acute Myocardial Infarction by Combining Random Forest With Artificial Neural Network

“…It has been reported that the combined machine-learning methods of RF and ANN were efficient in many data-generating processes ( 43 ). As shown in our study, our model has a superior predictive capacity (AUC = 0.980) when compared to another model built by Chen et al (AUC = 0.8550) ( 44 ). Moreover, the AUC of the predictive model achieved 0.900 in the validation set GSE61144, 0.882 in set GSE34198, and 1.00 in set GSE97320, which suggested that our model is of great applicability.…”

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

“…Furthermore, the prediction model exhibited good efficiency in two external validation cohorts (AUC = 0.745 and 0.711), providing new insights into early and rapid diagnosis of AMI. Chen et al also developed a RF diagnostic model of AMI, the AUC value is 0.855 (train set) and 0.731 (test set) ( Yifan et al, 2021 ). Fang et al developed a SVM diagnostic model of AMI, the AUC value is 0.860 (train set) and 0.921 (test set) ( Fang et al, 2020 ).…”