Penrice-Randal, Rebekah 
ORCID: 0000-0002-0653-2097, Dong, Xiaofeng, Shapanis, Andrew George, Gardner, Aaron, Harding, Nicholas, Legebeke, Jelmer, Lord, Jenny, Vallejo, Andres F, Poole, Stephen, Brendish, Nathan J et al (show 10 more authors)
(2022)
Blood gene expression predicts intensive care unit admission in hospitalised patients with COVID-19.
Frontiers in immunology, 13.
p. 988685.
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Blood gene expression predicts intensive care unit admission in hospitalised patients with COVID-19.pdf - Author Accepted Manuscript Download (1MB) | Preview |
Abstract
<h4>Background</h4>The COVID-19 pandemic has created pressure on healthcare systems worldwide. Tools that can stratify individuals according to prognosis could allow for more efficient allocation of healthcare resources and thus improved patient outcomes. It is currently unclear if blood gene expression signatures derived from patients at the point of admission to hospital could provide useful prognostic information.<h4>Methods</h4>Gene expression of whole blood obtained at the point of admission from a cohort of 78 patients hospitalised with COVID-19 during the first wave was measured by high resolution RNA sequencing. Gene signatures predictive of admission to Intensive Care Unit were identified and tested using machine learning and topological data analysis, TopMD.<h4>Results</h4>The best gene expression signature predictive of ICU admission was defined using topological data analysis with an accuracy: 0.72 and ROC AUC: 0.76. The gene signature was primarily based on differentially activated pathways controlling epidermal growth factor receptor (EGFR) presentation, Peroxisome proliferator-activated receptor alpha (PPAR-α) signalling and Transforming growth factor beta (TGF-β) signalling.<h4>Conclusions</h4>Gene expression signatures from blood taken at the point of admission to hospital predicted ICU admission of treatment naïve patients with COVID-19.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | COVID-19, Critical Care, biomarkers, prognosis, topology, transcriptome, RNA-seq, RNA sequencing |
| Divisions: | Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Infection, Veterinary and Ecological Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 17 Oct 2022 08:22 |
| Last Modified: | 18 Jan 2023 20:36 |
| DOI: | 10.3389/fimmu.2022.988685 |
| Related URLs: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3165488 |
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