Proteómica completa de orina mediante DIA y PRM para la caracterización de proteínas de baja abundancia en la...

Wilson disease (WD) is an inherited disorder of copper metabolism with early diagnostic challenges. Urinary proteomics shows promise for identifying WD-related biomarkers, but current strategies neglect the detection of low-abundance proteins critical for preclinical diagnosis. Urine samples from 53 newly diagnosed WD patients and 47 matched controls were analyzed using an optimized proteomics strategy integrating data-independent acquisition (DIA) with parallel reaction monitoring (PRM). Functional enrichment analysis, hierarchical clustering and other multidimensional analyses were employed to delve into low-abundance protein biomarkers. Recursive feature elimination (RFE) and support vector machine (SVM) were applied to identify the candidate biomarkers, and the performance of the diagnostic model was measured by the receiver operating characteristic (ROC) curve. Furthermore, the potential biomarkers were validated by ELISA in an independent validation cohort. The optimized DIA-based untargeted proteomics identified 2263 urine proteins, including 447 differentially expressed proteins (68 upregulated and 379 downregulated). After LC-PRM-MS verification, 46 new candidate biomarkers for WD were identified and 11 were included in the final model. The SVM model performed the best in classifying WD and healthy control, and the areas under the ROC curve in the training set and the test set were 0.95 and 0.94 respectively. Four proteins were validated by ELISA in an independent cohort, with expression levels consistent with the proteomics data. The proposed DIA-PRM proteomics analysis detect more low-abundance urinary proteins, and a urinary protein biomarker panel with high accuracy for non-invasive diagnosis in WD patients was identified.