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.

Investigation of shared genes and regulatory mechanisms associated with coronavirus disease 2019 and ischemic stroke

“…Peripheral blood levels of miR-200c-3p have been suggested as potential biomarkers for IS ( 54 ). The reduced miR-34a-5p expression has been observed in lung tissues and airway samples of patients with COVID-19 ( 55 , 56 ). Increased miR-34a-5p expression induces brain cell apoptosis in patients with acute IS ( 57 ).…”

Bioinformatics and system biology analysis revealed the crosstalk between COVID‐19 and osteoarthritis

“…Moreover, miR‐320a had higher expression in OA chondrocytes and promote matrix degeneration 55 while a study conversely found that miR‐320a upregulated the expression of RUNX2 and inhibited OA cartilage degeneration 56 . Machine Learning predicted that miR‐24‐3p, targeting NRP‐1, significantly decreased in COVID‐19 patients 57 and could also inhibit S protein expression and SARS‐CoV‐2 replication 58 . As for OA, the chondrocyte destruction induced by IL‐1β was attenuated by miR‐24‐3p, 59 further indicating that miR‐24‐3p may alleviate the severity of both COVID‐19 and OA.…”

Binary modified Cat and Mouse based Optimizer for medical feature selection: A COVID-19 case study

“…Therefore, it is essential and inevitable to provide efficient models for identifying and separating infected or suspected patients from healthy individuals. Within this short time since the first cases of the COVID-19 were reported, researchers have used MAs for its diagnosis and prevention with promising results [97][98][99]. These applications can be broken down into three categories: screening, prediction, and diagnosis [7,16].…”