#AI reads Urine# Comprehensive urinary proteomics using DIA and PRM for low-abundance protein profiling of Wilson disease

Wilson disease is an inherited disorder of copper metabolism that poses challenges for early diagnosis. Urinary proteomics holds promise for identifying relevant biomarkers, but current strategies often overlook low-abundance proteins crucial for preclinical detection. In this study, urine samples from 53 newly diagnosed Wilson disease patients and 47 matched healthy controls were analyzed using an optimized proteomic approach integrating data-independent acquisition and parallel reaction monitoring. Multidimensional analytical methods including functional enrichment analysis and hierarchical clustering were employed to explore low-abundance protein biomarkers. Recursive feature elimination and support vector machine were used to screen candidate biomarkers, with the receiver operating characteristic curve evaluating the diagnostic model's performance. Potential biomarkers were further validated by enzyme-linked immunosorbent assay in an independent cohort. The optimized data-independent acquisition-based untargeted proteomics identified 2263 urinary proteins, among which 447 showed differential expression (68 upregulated and 379 downregulated). After liquid chromatography-parallel reaction monitoring-mass spectrometry verification, 46 novel candidate biomarkers for Wilson disease were identified, and 11 were incorporated into the final diagnostic model. The support vector machine model exhibited excellent performance in distinguishing Wilson disease patients from healthy controls, with areas under the receiver operating characteristic curve of 0.95 in the training set and 0.94 in the test set. Four proteins were validated by enzyme-linked immunosorbent assay in an independent cohort, and their expression levels were consistent with the proteomic data. This proposed data-independent acquisition-parallel reaction monitoring proteomic analysis enables more effective detection of low-abundance urinary proteins and identifies a high-accuracy urinary protein biomarker panel for the non-invasive diagnosis of Wilson disease, providing important support for the early detection of this disease.

Journal of Chromatography B DOI: 10.1016/j.jchromb.2025.124906

Youhe Gao

Statement: During the preparation of this work the author(s) used Doubao / AI reading for summarizing the content. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

For earlier AI Reads Urine articles：

https://www.keaipublishing.com/en/journals/advances-in-biomarker-sciences-and-technology/ai-reads-urine/