Urinary Proteomics Analysis of Active Vitiligo Patients: Biomarkers for Steroid Treatment Efficacy Prediction and Monitoring

Abstract

Vitiligo is a common acquired skin disorder caused by immune-mediated destruction of epidermal melanocytes. Systemic glucocorticoids (GCs) have been used to prevent the progression of active vitiligo, with 8.2–56.2% of patients insensitive to this therapy. Currently, there is a lack of biomarkers that can accurately predict and evaluate treatment responses. The goal of this study was to identify candidate urinary protein biomarkers to predict the efficacy of GCs treatment in active vitiligo patients and monitor the disease. Fifty-eight non-segmental vitiligo patients were enrolled, and 116 urine samples were collected before and after GCs treatment. Patients were classified into a treatment-effective group (n= 42) and a treatment-resistant group (n= 16). Each group was divided equally into age- and sex-matched experimental and validation groups, and proteomic analyses were performed. Differentially expressed proteins were identified, and Ingenuity Pathway Analysis was conducted for the functional annotation of these proteins. Receiver operating characteristic curves were used to evaluate the diagnostic value. A total of 245 and 341 differentially expressed proteins between the treatment-resistant and treatment-effective groups were found before and after GCs treatment, respectively. Bioinformatic analysis revealed that the urinary proteome reflected the efficacy of GCs in active vitiligo patients. Eighty and fifty-four candidate biomarkers for treatment response prediction and treatment response evaluation were validated, respectively. By ELISA analysis, retinol binding protein-1 and torsin 1A interacting protein 1 were validated to have the potential to predict the efficacy of GCs with AUC value of 1 and 0.875, respectively. Retinol binding protein-1, torsin 1A interacting protein 1 and protein disulfide-isomerase A4 were validated to have the potential to reflect positive treatment effect to GCs treatment in active vitiligo with AUC value of 0.861, 1 and 0.868, respectively. This report is the first to identify urine biomarkers for GCs treatment efficacy prediction in vitiligo patients. These findings might contribute to the application of GCs in treating active vitiligo patients.