Identifying MicroRNA Markers That Predict COVID-19 Severity Using Machine Learning Methods.

Abstract

Individuals with the SARS-CoV-2 infection may experience a wide range of symptoms, from being asymptomatic to having a mild fever and cough to a severe respiratory impairment that results in death. MicroRNA (miRNA), which plays a role in the antiviral effects of SARS-CoV-2 infection, has the potential to be used as a novel marker to distinguish between patients who have various COVID-19 clinical severities. In the current study, the existing blood expression profiles reported in two previous studies were combined for deep analyses. The final profiles contained 1444 miRNAs in 375 patients from six categories, which were as follows: 30 patients with mild COVID-19 symptoms, 81 patients with moderate COVID-19 symptoms, 30 non-COVID-19 patients with mild symptoms, 137 patients with severe COVID-19 symptoms, 31 non-COVID-19 patients with severe symptoms, and 66 healthy controls. An efficient computational framework containing four feature selection methods (LASSO, LightGBM, MCFS, and mRMR) and four classification algorithms (DT, KNN, RF, and SVM) was designed to screen clinical miRNA markers, and a high-precision RF model with a 0.780 weighted F1 was constructed. Some miRNAs, including miR-24-3p, whose differential expression was discovered in patients with acute lung injury complications brought on by severe COVID-19, and miR-148a-3p, differentially expressed against SARS-CoV-2 structural proteins, were identified, thereby suggesting the effectiveness and accuracy of our framework. Meanwhile, we extracted classification rules based on the DT model for the quantitative representation of the role of miRNA expression in differentiating COVID-19 patients with different severities. The search for novel biomarkers that could predict the severity of the disease could aid in the clinical diagnosis of COVID-19 and in exploring the specific mechanisms of the complications caused by SARS-CoV-2 infection. Moreover, new therapeutic targets for the disease may be found.