In Frontiers in immunology ; h5-index 100.0
Background : 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.
Methods : 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.
Results : 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.
Conclusions : Gene expression signatures from blood taken at the point of admission to hospital predicted ICU admission of treatment naïve patients with COVID-19.
Penrice-Randal Rebekah, Dong Xiaofeng, Shapanis Andrew George, Gardner Aaron, Harding Nicholas, Legebeke Jelmer, Lord Jenny, Vallejo Andres F, Poole Stephen, Brendish Nathan J, Hartley Catherine, Williams Anthony P, Wheway Gabrielle, Polak Marta E, Strazzeri Fabio, Schofield James P R, Skipp Paul J, Hiscox Julian A, Clark Tristan W, Baralle Diana
COVID-19, Critical Care, RNA-seq - RNA sequencing, biomarkers, prognosis, topology, transcriptome