From the ‡Center for Bioinformatics, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China.
§State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences(Beijing), Beijing Institute of Lifeomics, Beijing 102206, China.
Mol Cell Proteomics. 2019 Jun;18(6):1110-1122. doi: 10.1074/mcp.RA119.001343. Epub 2019 Mar 20.
Disease biomarkers are the measurable changes associated with a pathophysiological process. Without homeostatic control, urine accumulates systematic changes in the body. Thus, urine is an attractive biological material for the discovery of disease biomarkers. One of the major bottlenecks in urinary biomarker discovery is that the concentration and composition of urinary proteins are influenced by many physiological factors. To elucidate the individual variation and related factors influencing the urinary proteome, we comprehensively analyzed the urine samples from healthy adult donors (aged 20-69 years). Co-expression network analysis revealed protein clusters representing the metabolic status, gender-related differences and age-related differences in urinary proteins. In particular, we demonstrated that gender is a crucial factor contributing to individual variation. Proteins that were increased in the male urine samples include prostate-secreted proteins and TIMP1, a protein whose abundance alters under various cancers and renal diseases; however, the proteins that were increased in the female urine samples have known functions in the immune system. Nine gender-related proteins were validated on 85 independent samples by multiple reaction monitoring. Five of these proteins were further used to build a model that could accurately distinguish male and female urine samples with an area under curve value of 0.94. Based on the above results, we strongly suggest that future biomarker investigations should consider gender as a crucial factor in experimental design and data analysis. Finally, reference intervals of each urinary protein were estimated, providing a baseline for the discovery of abnormalities.
疾病生物标志物是与病理生理过程相关的可测量变化。如果没有体内平衡控制，尿液会在体内积累系统性变化。因此，尿液是发现疾病生物标志物的有吸引力的生物材料。尿液生物标志物发现的主要瓶颈之一是尿液蛋白的浓度和组成受许多生理因素的影响。为了阐明影响尿蛋白质组的个体变异和相关因素，我们全面分析了来自健康成年供体（年龄 20-69 岁）的尿液样本。共表达网络分析揭示了代表代谢状态、性别相关差异和年龄相关差异的蛋白质簇。特别是，我们证明了性别是导致个体变异的关键因素。在男性尿液样本中增加的蛋白质包括前列腺分泌的蛋白质和 TIMP1，TIMP1 是一种在各种癌症和肾脏疾病下丰度改变的蛋白质；然而，在女性尿液样本中增加的蛋白质在免疫系统中有已知的功能。通过多重反应监测在 85 个独立样本上验证了 9 种性别相关蛋白。其中 5 种蛋白质进一步用于构建一个模型，该模型可以准确地区分男性和女性尿液样本，曲线下面积值为 0.94。基于上述结果，我们强烈建议未来的生物标志物研究应将性别视为实验设计和数据分析中的关键因素。最后，估计了每种尿液蛋白的参考区间，为发现异常提供了基线。
