National Key Laboratory of Medical Molecular Biology, Department of Physiology and Pathophysiology, Peking Union Medical College, 5 Dong Dan San Tiao, Beijing, China.
Mol Cell Proteomics. 2011 Nov;10(11):M111.010975. doi: 10.1074/mcp.M111.010975. Epub 2011 Aug 29.
Urine is an important source of biomarkers. A single proteomics assay can identify hundreds of differentially expressed proteins between disease and control samples; however, the ability to select biomarker candidates with the most promise for further validation study remains difficult. A bioinformatics tool that allows accurate and convenient comparison of all of the existing related studies can markedly aid the development of this area. In this study, we constructed the Urinary Protein Biomarker (UPB) database to collect existing studies of urinary protein biomarkers from published literature. To ensure the quality of data collection, all literature was manually curated. The website (http://122.70.220.102/biomarker) allows users to browse the database by disease categories and search by protein IDs in bulk. Researchers can easily determine whether a biomarker candidate has already been identified by another group for the same disease or for other diseases, which allows for the confidence and disease specificity of their biomarker candidate to be evaluated. Additionally, the pathophysiological processes of the diseases can be studied using our database with the hypothesis that diseases that share biomarkers may have the same pathophysiological processes. Because of the natural relationship between urinary proteins and the urinary system, this database may be especially suitable for studying the pathogenesis of urological diseases. Currently, the database contains 553 and 275 records compiled from 174 and 31 publications of human and animal studies, respectively. We found that biomarkers identified by different proteomic methods had a poor overlap with each other. The differences between sample preparation and separation methods, mass spectrometers, and data analysis algorithms may be influencing factors. Biomarkers identified from animal models also overlapped poorly with those from human samples, but the overlap rate was not lower than that of human proteomics studies. Therefore, it is not clear how well the animal models mimic human diseases.
尿液是生物标志物的重要来源。单个蛋白质组学检测可鉴定出疾病与对照样本之间数百种差异表达的蛋白质；然而，选择具有进一步验证研究最有前途的生物标志物候选物的能力仍然很困难。一种能够准确、方便地比较所有现有相关研究的生物信息学工具，将极大地促进该领域的发展。在这项研究中，我们构建了尿蛋白生物标志物（UPB）数据库，用于收集已发表文献中尿蛋白生物标志物的现有研究。为了确保数据收集的质量，所有文献都经过了人工编辑。该网站（http://122.70.220.102/biomarker）允许用户按疾病类别浏览数据库，并批量按蛋白质 ID 搜索。研究人员可以轻松地确定一个生物标志物候选物是否已经被另一个研究小组针对同一疾病或其他疾病进行了鉴定，从而可以评估其生物标志物候选物的可信度和疾病特异性。此外，还可以使用我们的数据库研究疾病的病理生理过程，假设具有相同生物标志物的疾病可能具有相同的病理生理过程。由于尿蛋白与泌尿系统之间的天然关系，该数据库可能特别适合研究泌尿系统疾病的发病机制。目前，该数据库包含分别从 174 篇和 31 篇人类和动物研究文献中编译的 553 条和 275 条记录。我们发现，不同蛋白质组学方法鉴定的生物标志物彼此之间重叠较差。样品制备和分离方法、质谱仪和数据分析算法的差异可能是影响因素。动物模型中鉴定的生物标志物与人类样本中的生物标志物也重叠较差，但重叠率并不低于人类蛋白质组学研究。因此，尚不清楚动物模型在多大程度上模拟人类疾病。
