Fudan University, 220 Handan Road, Shanghai 200433, China.
Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province 310024, China.
J Proteome Res. 2023 Sep 1;22(9):2847-2859. doi: 10.1021/acs.jproteome.3c00092. Epub 2023 Aug 9.
The ongoing pandemic of the coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 still has limited treatment options. Our understanding of the molecular dysregulations that occur in response to infection remains incomplete. We developed a web application COVIDpro (https://www.guomics.com/covidPro/) that includes proteomics data obtained from 41 original studies conducted in 32 hospitals worldwide, involving 3077 patients and covering 19 types of clinical specimens, predominantly plasma and serum. The data set encompasses 53 protein expression matrices, comprising a total of 5434 samples and 14,403 unique proteins. We identified a panel of proteins that exhibit significant dysregulation, enabling the classification of COVID-19 patients into severe and non-severe disease categories. The proteomic signatures achieved promising results in distinguishing severe cases, with a mean area under the curve of 0.87 and accuracy of 0.80 across five independent test sets. COVIDpro serves as a valuable resource for testing hypotheses and exploring potential targets for novel treatments in COVID-19 patients.
由严重急性呼吸综合征冠状病毒 2 引起的 2019 年冠状病毒病(COVID-19)大流行仍有有限的治疗选择。我们对感染后发生的分子失调的理解仍不完整。我们开发了一个名为 COVIDpro(https://www.guomics.com/covidPro/)的网络应用程序,其中包括从全球 32 家医院进行的 41 项原始研究中获得的蛋白质组学数据,涉及 3077 名患者,涵盖 19 种临床标本,主要是血浆和血清。该数据集包含 53 个蛋白质表达矩阵,共包含 5434 个样本和 14403 个独特蛋白质。我们确定了一组表现出明显失调的蛋白质,使 COVID-19 患者能够分为严重和非严重疾病类别。蛋白质组学特征在区分严重病例方面取得了有希望的结果,在五个独立测试集中的平均曲线下面积为 0.87,准确性为 0.80。COVIDpro 是一个宝贵的资源,可用于测试假设并探索 COVID-19 患者新型治疗方法的潜在靶点。