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Risk stratification by long non-coding RNAs profiling in COVID-19 patients
Jie Cheng1,2, Xiang Zhou3, Weijun Feng4
1Center for Reproductive Medicine, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
Journal of Cellular and Molecular Medicine
|March 24, 2021
Summary
This study identifies long non-coding RNAs (lncRNAs) altered in COVID-19 patients. A 7-lncRNA panel distinguishes severe from non-severe cases, revealing subtypes within severe COVID-19.
Area of Science:
- Genomics
- Molecular Biology
- Infectious Diseases
Background:
- Coronavirus disease 2019 (COVID-19), caused by SARS-CoV-2, is a global pandemic.
- The role of long non-coding RNAs (lncRNAs) in COVID-19 pathogenesis remains largely unknown.
Purpose of the Study:
- To identify lncRNAs associated with COVID-19 severity.
- To develop a diagnostic panel for differentiating COVID-19 patient groups.
- To explore COVID-19 heterogeneity using lncRNA expression profiles.
Main Methods:
- RNA sequencing of peripheral blood mononuclear cells from COVID-19 patients and healthy controls.
- Least Absolute Shrinkage and Selection Operator (LASSO) regression for lncRNA panel development.
- iCluster algorithm for subtyping severe COVID-19 patients.
Main Results:
- Identified differentially expressed lncRNAs between severe, non-severe COVID-19 patients, and healthy individuals.
- Developed a 7-lncRNA panel demonstrating significant differential ability between severe and non-severe COVID-19.
- Discovered two distinct subtypes within severe COVID-19 patients based on lncRNA profiles.
Conclusions:
- lncRNAs play a role in COVID-19 and can serve as biomarkers for disease severity.
- The developed lncRNA panel offers potential for clinical application in COVID-19 patient stratification.
- COVID-19 exhibits heterogeneity, with severe cases presenting distinct molecular subtypes.
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