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Risk stratification by long non‐coding RNAs profiling in COVID‐19 patients

Authors :
Xiang Zhou
Xinlu Zhang
Weijun Feng
Yi Pan
Cheng Zhou
Jie Cheng
Yun Sun
Shu Zhang
Taixue An
Lei Wen
Zhaoming Zhou
Minyuan Luan
Min Jia
Source :
Journal of Cellular and Molecular Medicine
Publication Year :
2021
Publisher :
Wiley, 2021.

Abstract

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.

Details

ISSN :
15824934 and 15821838
Volume :
25
Database :
OpenAIRE
Journal :
Journal of Cellular and Molecular Medicine
Accession number :
edsair.doi.dedup.....f41a9f8c5a3afcd7fa1e35d07011f2e7
Full Text :
https://doi.org/10.1111/jcmm.16444