Circulating miRNA Profiles in COVID-19 Patients and Meta-Analysis: Implications for Disease Progression and Prognosis
We compared circulating miRNA profiles of hospitalized COVID-positive patients (n = 104, 27 with ARDS) and age and gender matched healthy controls (n = 18) to identify miRNA signatures associated with COVID and COVID-induced ARDS. Meta-analysis incorporating data from published studies and our data was performed to identify a set of differentially expressed miRNAs in 1) COVID-positive patients versus healthy controls as well as 2) severe (ARDS+) COVID vs moderate COVID. Gene ontology enrichment analysis of the genes these miRNAs interact with identified terms associated with immune response, such as interferon and interleukin signaling, as well as viral genome activities associated with COVID disease and severity. Additionally, we observed downregulation of a cluster of miRNAs located on chromosome 14 (14q32) among all COVID patients. To predict COVID disease and severity, we developed machine learning models that achieved AUC scores between 0.88–0.93 for predicting disease, and between 0.62–0.81 for predicting severity, even across diverse studies with different sample types (plasma versus serum), collection methods, and library preparations. Our findings provide network and top miRNA feature insights into COVID disease progression and contribute to the development of tools for disease prognosis and management. Overall design: Plasma samples were used to obtain miRNA profiles for patients and normal controls