Potential Predictive Value of miR-125b-5p, miR-155-5p and Their Target Genes in the Course of COVID-19

Xuewen Li,1 Yiting Wang,1 Qi Zhou,2 Junqi Pan,3 Jiancheng Xu1 1Department of Laboratory Medicine, First Hospital of Jilin University, Changchun, People’s Republic of China; 2Department of Pediatrics, First Hospital of Jilin University, Changchun, People’s Republic of China; 3Faculty of Medicine, Dentistry and Health Science, University of Melbourne, Melbourne, Victoria, AustraliaCorrespondence: Jiancheng Xu, Tel +86-431-8878-2595, Fax +86-431-8878-6169, Email xjc@jlu.edu.cnPurpose: This study aimed to provide new biomarkers for predicting the disease course of COVID-19 by analyzing the dynamic changes of microRNA (miRNA) and its target gene expression in the serum of COVID-19 patients at different stages.Methods: Serum samples were collected from all COVID-19 patients at three time points: the acute stage, the turn-negative stage, and the recovery stage. The expression level of miRNA and the target mRNA was measured by Quantitative Real-Time Polymerase Chain Reaction (RT-qPCR). The classification tree model was established to predict the disease course, and the prediction efficiency of independent variables in the model was analyzed using the receiver operating characteristic (ROC) curve.Results: The expression of miR-125b-5p and miR-155-5p was significantly up-regulated in the acute stage and gradually decreased in the turn-negative and recovery stages. The expression of the target genes CDH5, STAT3, and TRIM32 gradually down-regulated in the acute, turn-negative, and recovery stages. MiR-125b-5p, miR-155-5p, STAT3, and TRIM32 constituted a classification tree model with 100% accuracy of prediction and AUC > 0.7 for identification and prediction in all stages.Conclusion: MiR-125b-5p, miR-155-5p, STAT3, and TRIM32 could be useful biomarkers to predict the time nodes of the acute, turn-negative, and recovery stages of COVID-19.Keywords: miRNA, mRNA, COVID-19, classification tree model, RT-qPCR