用于评估新冠肺炎疾病严重程度的基因表达风险评分

Gene Expression Risk Scores for COVID-19 Illness Severity.

作者信息

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

机构信息

Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY, USA.

Division of Neonatology and Pediatric Molecular and Personalized Medicine Program, Department of Pediatrics, University of Rochester, Rochester, NY, USA.

出版信息

bioRxiv. 2021 Aug 24:2021.08.24.457521. doi: 10.1101/2021.08.24.457521.

DOI:10.1101/2021.08.24.457521

PMID:34462743

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8404885/

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.

摘要

背景

感染严重急性呼吸综合征冠状病毒2（SARS-CoV-2）后，新冠病毒疾病严重程度的相关因素尚未完全明确。

方法

我们评估了53名确诊感染SARS-CoV-2的成年人的外周血基因表达情况，这些患者经临床判定患有轻度、中度或重度疾病。采用监督主成分分析构建加权基因表达风险评分（WGERS），以区分重度和非重度新冠肺炎患者。

结果

轻度和中度疾病患者的基因表达模式相似，但与重度疾病患者显著不同。比较重度与非重度疾病时，我们鉴定出4000多个差异表达基因（FDR<0.05）。重度新冠肺炎中增加的生物学途径与血小板活化和凝血有关，而显著减少的途径与T细胞信号传导和分化有关。基于18个基因的WGERS在我们的训练队列中能够区分重度疾病（交叉验证的ROC-AUC=0.98），并在独立队列中预测重症监护需求（ROC-AUC=0.85）。将WGERS进行二分法分析，在我们的训练队列中对重度疾病分类的灵敏度为100%，特异度为85%，在验证队列中对确定重症监护需求的灵敏度为84%，特异度为74%。