Department of Biostatistics and Computational Biology, University of Rochester, Rochester, New York, USA.
Division of Neonatology and Pediatric Molecular and Personalized Medicine Program, Department of Pediatrics, University of Rochester, Rochester, New York, USA.
J Infect Dis. 2023 Feb 1;227(3):322-331. doi: 10.1093/infdis/jiab568.
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.
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.
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.
These data suggest that gene expression classifiers may provide clinical utility as predictors of COVID-19 illness severity.
感染严重急性呼吸综合征冠状病毒 2（SARS-CoV-2）后，与 2019 年冠状病毒病（COVID-19）疾病严重程度相关的因素尚未完全清楚。
我们评估了 53 名成人的外周血基因表达，这些成人经临床判定患有 SARS-CoV-2 感染引起的轻症、中症或重症疾病。采用监督主成分分析构建加权基因表达风险评分（WGERS），以区分严重和非严重 COVID-19。
轻症和中症患者的基因表达模式相似，但与重症疾病有明显差异。当比较重症与非重症疾病时，我们发现有 >4000 个基因差异表达（错误发现率 <0.05）。严重 COVID-19 中上调的生物学途径与血小板激活和凝血有关，而下调的途径与 T 细胞信号转导和分化有关。基于 18 个基因的 WGERS 可在我们的训练队列中区分重症疾病（交叉验证接收者操作特征曲线下面积[ROC-AUC] = 0.98），并可在独立队列中预测重症监护的需要（ROC-AUC = 0.85）。将 WGERS 二分类，在我们的训练队列中可实现对重症疾病的分类，其敏感性为 100%，特异性为 85%，在验证队列中，敏感性为 84%，特异性为 74%。
这些数据表明，基因表达分类器可能作为 COVID-19 疾病严重程度的预测指标具有临床应用价值。
