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In iScience

MicroRNAs (miRNAs) have been shown to play important roles in viral infections, but their associations with SARS-CoV-2 infection remain poorly understood. Here we detected 85 differentially expressed miRNAs (DE-miRNAs) from 2,336 known and 361 novel miRNAs that were identified in 233 plasma samples from 61 healthy controls and 116 COVID-19 patients using the high throughput sequencing and computational analysis. These DE-miRNAs were associated with SASR-CoV-2 infection, disease severity, and viral persistence in the COVID-19 patients, respectively. Gene ontology and KEGG pathway analyses of the DE-miRNAs revealed their connections to viral infections, immune responses, and lung diseases. Finally, we established a machine learning model using the DE-miRNAs between various groups for classification of COVID-19 cases with different clinical presentations. Our findings may help understand the contribution of miRNAs to the pathogenesis of COVID-19 and identify potential biomarkers and molecular targets for diagnosis and treatment of SARS-CoV-2 infection.

Zeng Qiqi, Qi Xin, Ma Junpeng, Hu Fang, Wang Xiaorui, Qin Hongyu, Li Mengyang, Huang Shaoxin, Yang Yong, Li Yixin, Bai Han, Jiang Meng, Ren Doudou, Kang Ye, Zhao Yang, Chen Xiaobei, Ding Xi, Ye Di, Wang Yankui, Jiang Jianguo, Li Dong, Chen Xi, Hu Ke, Zhang Binghong, Shi Bingyin, Zhang Chengsheng


Asymptomatic infection, COVID-19, SARS-CoV-2, machine learning, miRNA