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Gene Expression Risk Scores for COVID-19 Illness Severity
Derick R Peterson1, Andrea M Baran1, Soumyaroop Bhattacharya2
1Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA.
Biorxiv : the Preprint Server for Biology
|August 31, 2021
Summary
Gene expression patterns can predict COVID-19 severity. A weighted gene expression risk score (WGERS) accurately identified severe illness and intensive care needs, offering potential clinical utility.
Area of Science:
- Genomics and Molecular Biology
- Infectious Diseases
- Immunology
Background:
- Correlates of COVID-19 illness severity remain incompletely understood.
- Accurate prediction of severe COVID-19 is crucial for patient management.
Approach:
- Peripheral blood gene expression was analyzed in 53 adults with varying COVID-19 severity.
- Supervised principal components analysis developed a weighted gene expression risk score (WGERS).
Key Points:
- Over 4000 genes were differentially expressed between severe and non-severe COVID-19.
- Severe cases showed increased platelet activation/coagulation and decreased T cell signaling.
- An 18-gene WGERS achieved high accuracy (ROC-AUC=0.98) in predicting severe illness.
Conclusions:
- Gene expression classifiers demonstrate potential clinical utility.
- WGERS accurately predicted severe COVID-19 and intensive care unit admission.
- This approach may aid in early identification of high-risk patients.
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