| Literature DB >> 30894400 |
Chen Shao1,2, Mindi Zhao3,4, Xizhao Chen5, Haidan Sun6, Yehong Yang6, Xiaoping Xiao6,7, Zhengguang Guo6, Xiaoyan Liu6, Yang Lv5, Xiangmei Chen5, Wei Sun8, Di Wu9, Youhe Gao10.
Abstract
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.Entities:
Keywords: Biofluids*; Label-free quantification; Mass Spectrometry; Protein Identification*; Urine analysis
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Year: 2019 PMID: 30894400 PMCID: PMC6553935 DOI: 10.1074/mcp.RA119.001343
Source DB: PubMed Journal: Mol Cell Proteomics ISSN: 1535-9476 Impact factor: 5.911