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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, New York, USA.
The Journal of Infectious Diseases
|December 1, 2021
Summary
Gene expression patterns in peripheral blood can predict the severity of coronavirus disease 2019 (COVID-19). A weighted gene expression risk score (WGERS) effectively identified severe illness and intensive care needs, suggesting clinical utility.
Area of Science:
- Immunology
- Genomics
- Infectious Diseases
Background:
- Correlates of COVID-19 illness severity are not fully understood.
- Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection leads to a range of illness severities.
Purpose of the Study:
- To assess peripheral blood gene expression in COVID-19 patients.
- To develop a gene expression-based risk score to predict COVID-19 severity.
Main Methods:
- Analyzed gene expression in 53 adults with mild, moderate, or severe COVID-19.
- Utilized supervised principal components analysis to create a weighted gene expression risk score (WGERS).
Main Results:
- Gene expression differed significantly between severe and non-severe COVID-19 cases.
- Severe COVID-19 showed increased platelet activation/coagulation pathways and decreased T-cell signaling.
- WGERS accurately predicted severe illness (ROC-AUC = 0.98) and intensive care needs (ROC-AUC = 0.85).
Conclusions:
- Gene expression classifiers show potential clinical utility for predicting COVID-19 severity.
- WGERS demonstrated high sensitivity and specificity in classifying disease severity.
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