Statistical analysis
All statistical analyses in this study were performed using R programming (R version 4.2.2, R Foundation for Statistical Computing, Vienna, Austria with IDE: R Studio Version 1.2.5, Boston, MA, USA). As a pre-requisite for the analysis, thresholds of 30% (i.e., ≥ 10 out of 32 datasets) peptide frequency (in at least one treatment group) were applied. Alongside, area under the receiver operating curve (ROC) curve (AUC) values were calculated by the DeLong approach, to compare the urinary peptide profiles between the pre- and post-treated samples; the selected urinary peptides passed a threshold of AUC ≥ 0.60. The normally distributed and continuous datasets generated from CE-MS based peptide profiles of the urine samples, obtained from pre-treatment (n =32) and post-treatment (n =32); were compared by a paired Wilcoxon rank-sum test, using the row_wilcoxon_paired() function from the matrixTests package. A p -value < 0.05 considered statistically significant, was further adjusted for false discovery rates (FDR) by the Benjamini-Hochberg method[36]. All the plots in this manuscript were created using the ggplot() function from the ggplot2 package[37].