Risk Stratification By Long Non-Coding Rnas Profiling In Covid-19 Patients

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has become a global pandemic worldwide. Long non-coding RNAs (lncRNAs) are a subclass of endogenous, non-protein-coding RNA, which lacks an open reading frame and is more than 200 nucleotides in length. However, the functions for lncRNAs in COVID-19 have not been unravelled. The present study aimed at identifying the related lncRNAs based on RNA sequencing of peripheral blood mononuclear cells from patients with SARS-CoV-2 infection as well as health individuals. Overall, 17 severe, 12 non-severe patients and 10 healthy controls were enrolled in this study. Firstly, we reported some altered lncRNAs between severe, non-severe COVID-19 patients and healthy controls. Next, we developed a 7-lncRNA panel with a good differential ability between severe and non-severe COVID-19 patients using least absolute shrinkage and selection operator regression. Finally, we observed that COVID-19 is a heterogeneous disease among which severe COVID-19 patients have two subtypes with similar risk score and immune score based on lncRNA panel using iCluster algorithm. As the roles of lncRNAs in COVID-19 have not yet been fully identified and understood, our analysis should provide valuable resource and information for the future studies.