Urinary extracellular vesicles (uEVs) reflect the biological conditions of the producing cells. The protein profiling of uEVs allow us to better understand cancer progression in several cancers such as bladder cancer, prostate cancer and kidney cancer but has not been reported in breast cancer. We have, herein, aimed at quantifying the concentration and at generating the proteomic profile of uEVs in patients with breast cancer (BC) as compared to that of healthy controls (CT). Urine samples were collected from 29 CT and 47 patients with BC. uEVs were isolated by using differential ultracentrifugation, and were then characterized by Western blotting and transmission electron microscopy. Moreover, a nanoparticle tracking analysis was used in order to measure the concentration and the size distribution of urine particles and uEVs. The proteomic profiling of the uEVs was facilitated through LC-MS/MS. The uEV concentration was not significantly different between the assessed groups. The undertaken proteomic analysis revealed 15,473 and 11,278 proteins in the BC patients' group and the CT group, respectively. Furthermore, a heat map analysis revealed a differential protein expression, while a principal component analysis highlighted two clusters. The volcano plot indicated 259 differentially expressed proteins (DEPs; 155 up- and 104 down-regulated proteins) in patients with BC compared with CT. The up-regulated proteins from BC-derived uEVs were enriched in pathways related to cancer progression (i.e., cell proliferation, cell survival, cell cycle, cell migration, carbohydrate metabolism, and angiogenesis). Moreover, we verified the expression of the upregulated DEPs using UALCAN for web-based validation. Remarkably, the results indicated that 6 of 155 up-regulated proteins (POSTN, ATAD2, BCAS4, GSK3β, HK1, and Ki-67) were overexpressed in BC compared with normal samples. Since these six proteins often act as markers of cell proliferation and progression, they may be potential biomarkers for BC screening and diagnosis. However, this requires validation in larger cohorts.
Copyright: © 2023 Jeanmard et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.