Gene Expression Risk Scores for COVID-19 Illness Severity. (30th November 2021)

• Record Type: Journal Article
• Authors: Peterson, Derick R; Baran, Andrea M; Bhattacharya, Soumyaroop; Branche, Angela R; Croft, Daniel P; Corbett, Anthony M; Walsh, Edward E; Falsey, Ann R; Mariani, Thomas J
• Is Part Of: Journal of infectious diseases. Volume 227:Number 3(2023)
• Page Start: 322
• Page End: 331
• Publication Date: 2021-11-30
• Subjects: COVID-19 -- severity -- RNA sequencing -- ICU -- prediction -- molecular markers; Communicable diseases -- Periodicals; Diseases -- Causes and theories of causation -- Periodicals; Medicine -- Periodicals; Communicable Diseases -- Periodicals; Electronic journals; 616.9
• DOI: 10.1093/infdis/jiab568
• Languages: English
• ISSNs: 0022-1899

Abstract

Abstract: Background: The correlates of coronavirus disease 2019 (COVID-19) illness severity following infection with severe acute respiratory syndrome 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 nonsevere COVID-19. Results: Gene expression patterns in participants with mild and moderate illness were similar, but significantly different from severe illness. When comparing severe versus nonsevere illness, we identified >4000 genes differentially expressed (false discovery rate < 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 receiver operating characteristic-area under the curve [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. Conclusions: These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity. Abstract : Transcriptomic analysis of peripheral blood from COVID-19 patients identified differentially expressed genes distinguishing subjects with severe disease from those with nonsevere disease. These markers correctly identified the hospitalization status of unrelated COVID-19 subjects with a high level of accuracy.