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].