No abstract

BackgroundSeveral promising biomarkers have been found for RCC, but none of them has been used in clinical practice for predicting tumour progression. The most widely used features for predicting tumour aggressiveness still remain the cancer stage, size and grade. Therefore, the aim of our study is to investigate the urinary peptidome to search and identify peptides whose concentrations in urine are linked to tumour growth measure and clinical data.MethodsA proteomic approach applied to ccRCC urinary peptidome (n = 117) based on prefractionation with activated magnetic beads followed by MALDI-TOF profiling was used. A systematic correlation study was performed on urinary peptide profiles obtained from MS analysis. Peptide identity was obtained by LC–ESI–MS/MS.ResultsFifteen, twenty-six and five peptides showed a statistically significant alteration of their urinary concentration according to tumour size, pT and grade, respectively. Furthermore, 15 and 9 signals were observed to have urinary levels statistically modified in patients at different pT or grade values, even at very early stages. Among them, C1RL, A1AGx, ZAG2G, PGBM, MMP23, GP162, ADA19, G3P, RSPH3, DREB, NOTC2 SAFB2 and CC168 were identified.ConclusionsWe identified several peptides whose urinary abundance varied according to tumour size, stage and grade. Among them, several play a possible role in tumorigenesis, progression and aggressiveness. These results could be a useful starting point for future studies aimed at verifying their possible use in the managements of RCC patients.Electronic supplementary materialThe online version of this article (doi:10.1186/s12967-015-0693-8) contains supplementary material, which is available to authorized users.

Renal Cell Carcinoma (RCC) is typically asymptomatic and surgery usually increases patient's lifespan only for early stage tumours. Moreover, solid renal masses cannot be confidently differentiated from RCC. Therefore, markers to distinguish malignant kidney tumours and for their detection are needed. Two different peptide signatures were obtained by a MALDI-TOF profiling approach based on urine pre-purification by C8 magnetic beads. One cluster of 12 signals could differentiate malignant tumours (n = 137) from benign renal masses and controls (n = 153) with sensitivity of 76% and specificity of 87% in the validation set. A second cluster of 12 signals distinguished clear cell RCC (n = 118) from controls (n = 137) with sensitivity and specificity values of 84% and 91%, respectively. Most of the peptide signals used in the two models were observed at higher abundance in patient urines and could be identified as fragments of proteins involved in tumour pathogenesis and progression. Among them: the Meprin 1α with a pro-angiogenic activity, the Probable G-protein coupled receptor 162, belonging to the GPCRs family and known to be associated with several key functions in cancer, the Osteopontin that strongly correlates to tumour stages and invasiveness, the Phosphorylase b kinase regulatory subunit alpha and the SeCreted and TransMembrane protein 1.

Our UEVs preparation protocol provides a simplified way to investigate the intraluminal proteome and peptidome, in particular the subpopulation of UEVs of the trypsin-resistant class of exosomes (positive for tumor susceptibility gene101) and eliminates the majority of interfering proteins such as THP. This method allows the possibility to study endoproteome and endopeptidome of UEVs, thus greatly facilitating biomarker discovery.

Renal Cell Carcinoma (RCC) is the most common kidney cancer, accounting for 3% of adult malignancies, with high metastatic potential and radio-/chemo-resistance. To investigate the protein profile of membrane microdomains (MD), plasma membrane supramolecular structures involved in cell signaling, transport, and neoplastic transformation, we set up a proteomic bottom-up approach as a starting point for the identification of potential RCC biomarkers. We purified MD from RCC and adjacent normal kidney (ANK) tissues, through their resistance to non-ionic detergents followed by ultracentrifugation in sucrose density gradient. MD from 5 RCC/ANK tissues were then pooled and analysed by LC-ESI-MS/MS. In order to identify the highest number of proteins and increase the amount of membrane and hydrophobic ones, we first optimized an enzymatic digestion protocol based on Filter Aided Sample Preparation (FASP), coupled to MD delipidation. The MS analysis led to the identification of 742 ANK MD and 721 RCC MD proteins, of which, respectively, 53.1% and 52.6% were membrane- bound. Additionally, we evaluated RCC MD differential proteome by label-free quantification; 170 and 126 proteins were found to be, respectively, up-regulated and down-regulated in RCC MD. Some differential proteins, namely CA2, CD13, and ANXA2, were subjected to validation by immunodecoration. These results show the importance of setting up different protocols for the proteomic analysis of membrane proteins, specific to the different molecular features of the samples. Furthermore, the subcellular proteomic approach provided a list of differentially expressed proteins among which RCC biomarkers may be looked for.