Abstract
Host biomarkers are increasingly being considered as tools for improved COVID-19 detection and prognosis. We recently profiled circulating host-encoded microRNA (miRNAs) during SARS-CoV-2 infection, revealing a signature that classified COVID-19 cases with 99.9% accuracy. Here we sought to develop a signature suited for clinical application by analyzing specimens collected using minimally invasive procedures. Eight miRNAs displayed altered expression in anterior nasal tissues from COVID-19 patients, with miR-142-3p, a negative regulator of interleukin-6 (IL-6) production, the most strongly upregulated. Supervised machine learning analysis revealed that a three-miRNA signature (miR-30c-2-3p, miR-628-3p and miR-93-5p) independently classifies COVID-19 cases with 100% accuracy. This study further defines the host miRNA response to SARS-CoV-2 infection and identifies candidate biomarkers for improved COVID-19 detection.
【초록키워드】 COVID-19, Biomarker, Prognosis, IL-6, SARS-COV-2 infection, miRNA, interleukin-6, interleukin, miRNAs, Accuracy, expression, COVID-19 patients, COVID-19 cases, COVID-19 case, specimen, circulating, host-encoded microRNA, miRNA signature, Supervised machine learning analysis, signature, nasal tissue, invasive, mir-93, host miRNA response, identify, collected, develop, upregulated, increasingly, machine learning analysis, 【제목키워드】 microRNA, detection, upper respiratory tract,