miR-125b-5p、miR-155-5p及其靶基因在新型冠状病毒肺炎病程中的潜在预测价值

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

作者信息

Li Xuewen, Wang Yiting, Zhou Qi, Pan Junqi, Xu Jiancheng

机构信息

Department of Laboratory Medicine, First Hospital of Jilin University, Changchun, People's Republic of China.

Department of Pediatrics, First Hospital of Jilin University, Changchun, People's Republic of China.

出版信息

Infect Drug Resist. 2022 Jul 29;15:4079-4091. doi: 10.2147/IDR.S372420. eCollection 2022.

PURPOSE

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.

目的

本研究旨在通过分析新型冠状病毒肺炎（COVID-19）患者不同阶段血清中微小RNA（miRNA）及其靶基因表达的动态变化，为预测COVID-19疾病进程提供新的生物标志物。

方法

收集所有COVID-19患者在急性期、转阴期和恢复期三个时间点的血清样本。采用定量实时聚合酶链反应（RT-qPCR）检测miRNA和靶mRNA的表达水平。建立分类树模型预测疾病进程，并使用受试者工作特征（ROC）曲线分析模型中自变量的预测效率。

结果

miR-125b-5p和miR-155-5p在急性期表达显著上调，在转阴期和恢复期逐渐下降。靶基因CDH5、STAT3和TRIM32的表达在急性期、转阴期和恢复期逐渐下调。MiR-125b-5p、miR-155-5p、STAT3和TRIM32构成了一个分类树模型，在所有阶段的识别和预测准确率均为100%，AUC>0.7。