Zhang Zhijin, Yang Huqin, Gao Leyi, Guan Lujia, Li Xuyan, Li Jieqiong, Tong Zhaohui
Department of Respiratory and Critical Care Medicine, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, China.
Medical Research Center, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing 100020, China.
J Proteome Res. 2025 Jul 4;24(7):3160-3173. doi: 10.1021/acs.jproteome.4c00725. Epub 2025 Jun 23.
The coronavirus disease 2019 (COVID-19) pandemic increased the demand for reliable tests to predict disease severity and mortality. In training cohort, we obtained traditional clinical data and plasma proteomics performed using the Olink proteomics platform from 52 fatal COVID-19 cases (COVID-19-F), 50 severe COVID-19 cases (COVID-19-S), 55 moderate/mild COVID-19 cases (COVID-19-M), and 54 healthy controls. Receiver operating characteristic (ROC) curves and logistic regression were applied to judge the accuracy of biomarkers to predict in-hospital mortality and build combined panel. An independent external cohort was used for validation. In total, 19 clinical parameters and 92 proteins were assessed. Traditional clinical indices did not show adequate predictive value of short-term mortality in severe COVID-19. In proteomics analysis, 75 proteins were differentially expressed among the four groups. Pathway analysis revealed an imbalance of inflammatory responses and excessive immunity in COVID-19-F. Subsequently, a novel plasma biomarker panel (including interleukin 8 and osteoprotegerin) was developed, with AUC values of 0.791 and 0.781 when comparing COVID-19-F to COVID-19-M or COVID-19-S, respectively. The predictive power of the panel was verified in an external cohort. Our standardized assays yielded a prediction panel of mortality during hospitalization in patients with COVID-19.
2019年冠状病毒病（COVID-19）大流行增加了对预测疾病严重程度和死亡率的可靠检测方法的需求。在训练队列中，我们从52例COVID-19死亡病例（COVID-19-F）、50例COVID-19重症病例（COVID-19-S）、55例COVID-19中/轻症病例（COVID-19-M）和54例健康对照中获取了传统临床数据以及使用Olink蛋白质组学平台进行的血浆蛋白质组学数据。应用受试者工作特征（ROC）曲线和逻辑回归来判断生物标志物预测院内死亡率的准确性并构建联合检测指标。使用一个独立的外部队列进行验证。总共评估了19项临床参数和92种蛋白质。传统临床指标在COVID-19重症患者中未显示出足够的短期死亡率预测价值。在蛋白质组学分析中，四组之间有75种蛋白质差异表达。通路分析显示COVID-19-F中炎症反应失衡和免疫过度。随后，开发了一种新型血浆生物标志物检测指标（包括白细胞介素8和骨保护素），在将COVID-19-F与COVID-19-M或COVID-19-S进行比较时，其AUC值分别为0.791和0.781。该检测指标的预测能力在外部队列中得到了验证。我们的标准化检测方法得出了一个用于预测COVID-19患者住院期间死亡率的检测指标。
