GSE158877
Early Predicting COVID-19 Severity Value with Extracellular Vesicles and Extracellular RNAs
The SARS-CoV-2 outbreak started on December 2019 in China and rapidly spread worldwide. Clinical manifestations of Coronavirus-disease 2019 (COVID-19) vary broadly, ranging from asymptomatic infection to acute respiratory failure and death, yet the underlying mechanisms and predictive biomarkers for this high variability are still unknown. Emerging evidence has shown that circulating extracellular vesicles (EVs) and extracellular RNAs (exRNAs) are functionally involved in a number of physiologic and pathologic processes. To test the hypothesis that these extracellular components are a key determinant of severity in COVID-19, we collected 31 serum samples from mild COVID-19 patients at admission in single center. After standard therapy without corticosteroids, 9 of 31 patients became severe COVID-19. We analyzed exRNA profiles from the 31 serums and 10 healthy controls for predicting COVID-19 severity value. Overall design: Total RNA was extracted from aliquots (200 µL) of the serum samples using QIAzol and the miRNeasy Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer's protocol.  The QIAseq miRNA-seq  analysis was performed using  the extracted miRNA.