tetano
Editor, Senior Moderator
J Infect Dis
. 2021 Nov 30;jiab568.
doi: 10.1093/infdis/jiab568. Online ahead of print.
Gene Expression Risk Scores for COVID-19 Illness Severity
Derick R Peterson[SUP] 1 [/SUP], Andrea M Baran[SUP] 1 [/SUP], Soumyaroop Bhattacharya[SUP] 2 [/SUP], Angela R Branche[SUP] 3 [/SUP], Daniel P Croft[SUP] 4 [/SUP], Anthony M Corbett[SUP] 1 [/SUP], Edward E Walsh[SUP] 3 5 [/SUP], Ann R Falsey[SUP] 3 5 [/SUP], Thomas J Mariani[SUP] 2 [/SUP]
Affiliations
Abstract
Background: The correlates of COVID-19 illness severity following infection with SARS-Coronavirus 2 (SARS-CoV-2) are incompletely understood.
Methods: We assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2-infection clinically adjudicated as having mild, moderate or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and non-severe COVID.
Results: Gene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus non-severe illness, we identified >4000 genes differentially expressed (FDR<0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated ROC-AUC=0.98), and need for intensive care in an independent cohort (ROC-AUC=0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort.
Conclusion: These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.
Keywords: COVID-19; ICU; Molecular Markers; Prediction; RNA sequencing; Severity.
. 2021 Nov 30;jiab568.
doi: 10.1093/infdis/jiab568. Online ahead of print.
Gene Expression Risk Scores for COVID-19 Illness Severity
Derick R Peterson[SUP] 1 [/SUP], Andrea M Baran[SUP] 1 [/SUP], Soumyaroop Bhattacharya[SUP] 2 [/SUP], Angela R Branche[SUP] 3 [/SUP], Daniel P Croft[SUP] 4 [/SUP], Anthony M Corbett[SUP] 1 [/SUP], Edward E Walsh[SUP] 3 5 [/SUP], Ann R Falsey[SUP] 3 5 [/SUP], Thomas J Mariani[SUP] 2 [/SUP]
Affiliations
- PMID: 34850892
- DOI: 10.1093/infdis/jiab568
Abstract
Background: The correlates of COVID-19 illness severity following infection with SARS-Coronavirus 2 (SARS-CoV-2) are incompletely understood.
Methods: We assessed peripheral blood gene expression in 53 adults with confirmed SARS-CoV-2-infection clinically adjudicated as having mild, moderate or severe disease. Supervised principal components analysis was used to build a weighted gene expression risk score (WGERS) to discriminate between severe and non-severe COVID.
Results: Gene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus non-severe illness, we identified >4000 genes differentially expressed (FDR<0.05). Biological pathways increased in severe COVID-19 were associated with platelet activation and coagulation, and those significantly decreased with T cell signaling and differentiation. A WGERS based on 18 genes distinguished severe illness in our training cohort (cross-validated ROC-AUC=0.98), and need for intensive care in an independent cohort (ROC-AUC=0.85). Dichotomizing the WGERS yielded 100% sensitivity and 85% specificity for classifying severe illness in our training cohort, and 84% sensitivity and 74% specificity for defining the need for intensive care in the validation cohort.
Conclusion: These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.
Keywords: COVID-19; ICU; Molecular Markers; Prediction; RNA sequencing; Severity.