Gene Expression Risk Scores for COVID-19 Illness Severity.
Gene Expression Risk Scores for COVID-19 Illness Severity.
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DOI：
10.1093/infdis/jiab568
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发表时间：
2023-02-01
期刊：
影响因子：
--
通讯作者：
Mariani TJ
中科院分区：
文献类型：
--
作者：
Peterson DR;Baran AM;Bhattacharya S;Branche AR;Croft DP;Corbett AM;Walsh EE;Falsey AR;Mariani TJ
严重急性呼吸综合征冠状病毒2（SARS-CoV-2）感染后2019冠状病毒病（COVID-19）疾病严重程度的相关性尚未完全了解。我们评估了53名确诊为SARS-CoV-2感染的成人的外周血基因表达，临床上判定为轻度、中度或重度疾病。使用监督主成分分析建立加权基因表达风险评分（WGERS），以区分严重和非严重COVID-19。轻度和中度疾病参与者的基因表达模式相似，但与重度疾病有显著差异。当比较严重与非严重疾病时，我们确定了>4000个差异表达的基因（错误发现率< 0.05）。在严重的COVID-19中，生物途径增加与血小板活化和凝血有关，而这些生物途径随着T细胞信号传导和分化而显著降低。基于18个基因的WGERS在我们的训练队列中区分了严重疾病（交叉验证的受试者工作特征-曲线下面积[ROC-AUC] = 0.98），并且在独立队列中需要重症监护（ROC-AUC = 0.85）。在我们的训练队列中，对WGERS进行二分法，对严重疾病进行分类的敏感性为100%，特异性为85%，在验证队列中，对重症监护需求的敏感性为84%，特异性为74%。这些数据表明，基因表达分类器可以提供临床实用性，作为COVID-19疾病严重程度的预测因子。对COVID-19患者外周血的转录组学分析确定了区分严重疾病受试者和非严重疾病受试者的差异表达基因。这些标志物以高准确度正确识别了无关COVID-19受试者的住院状态。
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. 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.
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DOI：
10.1093/bioinformatics/btu638
发表时间：
2015-01-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Anders S;Pyl PT;Huber W
通讯作者：
Huber W
影响因子：
168.9
作者：
Huang, Chaolin;Wang, Yeming;Cao, Bin
通讯作者：
Cao, Bin
影响因子：
4.6
作者：
Bhattacharya S;Rosenberg AF;Peterson DR;Grzesik K;Baran AM;Ashton JM;Gill SR;Corbett AM;Holden-Wiltse J;Topham DJ;Walsh EE;Mariani TJ;Falsey AR
通讯作者：
Falsey AR
影响因子：
64.5
作者：
Moderbacher, Carolyn Rydyznski;Ramirez, Sydney, I;Crotty, Shane
通讯作者：
Crotty, Shane
影响因子：
4.6
作者：
Li CX;Chen J;Lv SK;Li JH;Li LL;Hu X
通讯作者：
Hu X