Project PXD013837



Tissue Proteome Signatures Associated with Five Grades of Prostate Cancer and Benign Prostatic Hyperplasia


Prostate cancer (PCa) remains a prevalent and deadly disease. The histology-based Gleason score (GS) of PCa tissue biopsy is the most accurate predictor of disease aggressiveness and an important measure to guide decision-making with respect to treatment strategies and patient management. However, inherent variability associated with PCa tumour sampling and the subjective determination of the GS are still key challenges precluding accurate diagnostication and prognostication. Thus, novel molecular signatures are urgently needed to distinguish between indolent and aggressive forms of PCa for better patient management and outcomes. Herein, we have used label-free LC-MS/MS-based proteomics to profile the proteome of 50 PCa tissues spanning five GS-based PCa grades (n = 10 per group) relative to five tissues from individuals with benign prostatic hyperplasia (BPH). Over 2,000 proteins were consistently identified albeit at different levels between and within the patient groups, revealing biological processes associated with specific grades. Excitingly, a panel of 11 prostate-derived proteins including IGKV3D-20, RNASET2, TACC2, ANXA7, LMOD1, PRCP, GYG1, NDUFV1, H1FX, APOBEC3C, CTSZ displayed the potential to accurately stratify patients displaying low and high GS. This is the first study to characterise the prostate tissue proteome signatures of the five PCa grades relative to BPH. We report a panel of proteins that accurately can distinguish low and high GS PCa tissues. These promising proteins can be further explored as candidate biomarkers for PCa aggressiveness.

Sample Processing Protocol

Prostate sample collection The study was approved by the ethics review board of the Faculdade de Medicina do Estado de São Paulo (FM-USP), under the protocol nº2695126. Written informed consents were obtained from all participants. The methods were performed in accordance with the approved guidelines and regulations. All biopsy cores were examined for the presence of prostate cancer by a pathologist. Fresh tissues were collected after radical prostatectomy from a cohort of 55 patients, which included ten biological replicates from each prostate cancer grade (1 to 5) and five biological replicates from benign hyperplasia (BPH). Tissue protein extraction Fresh prostate tissues (~60 mg), stored in RNAlater, were washed in 80% acetonitrile (cold) to remove the RNAlater and resuspended in an extraction buffer containing 6 M urea, 10 mM dithiothreitol (DTT) extraction buffer containing 6 M urea, 10 mM DTT, 1 mM NaF, 1 mM Sodium orthovanadate and a cocktail inhibitor protease. Prostate tissues were lysed through a 2 min shaking at 30 Hz with a 5 mm stainless steel bead in a TissueLyser (Qiagen, Chadstone, VIC, Australia). Protein concentration was determined by Qubit fluorimetric detection method. Protein digestion and desalting Proteins (500 µg) were reduced by addition of DTT to a final concentration of 10 mM and incubated for 30 min at 30°C. Proteins were alkylated prior to digestion by the addition of iodoacetamide (IAA) to a final concentration of 40 mM and incubation for 30 min in the dark at room temperature. To quench the reaction, DTT was added to a final concentration of 10 mM. Porcine trypsin (1:50, w/w) was added, and the mixture was incubated overnight at 37 °C. The reaction was stopped with 1% trifluoroacetic acid (TFA). Resulting peptide mixtures were desalted with hydrophilic−lipophilic-balanced solid phase extraction (SPE) (Waters) and peptides eluted in 1 ml of 50% (v/v) acetonitrile (ACN)/ 0.1% (v/v) trifluoroacetic acid (TFA) and 70% ACN/0.1% TFA. Samples were dried in a vacuum concentrator and reconstituted in 0.1% formic acid (FA) for mass spectrometry analysis. Mass spectrometry analysis The peptides were separated at 250 nL/min on an analytical ReproSil-Pur C18 AQ (Dr. Maisch, Ammerbuch-Entringen, Germany) column packed in-house (17 cm x 75 µm; 3 µm) by reversed phase chromatography which was operated on an EASY-nanoLC system (Thermo Fisher Scientific, Odense, Denmark). The mobile phased were 95% ACN/ 0.1% FA (B) and water/0.1% FA (A). The gradient was from 3% to 28% solvent B in 52 mins, 28 - 47% B in 5 min, 45 - 100% B in 5 min and 8 min at 100% B. The nanoLC was connected directly to a Q Exactive HF Hybrid Quadrupole-Orbitrap mass spectrometer (Thermo Fisher Scientific) operating in positive ion mode and using data-dependent acquisition. The Orbitrap acquired the full MS scan with automatic gain control (AGC) target value of 3x106 ions and a maximum fill time of 100 ms. Each MS scan was acquired at high-resolution 120,000 full width half maximum (FWHM) at m/z 200 in the Orbitrap with an m/z acquisition range of 400-1,600. The ten most abundant precursor ions (z ≥ 2) were selected from each MS1 scan for higher energy collision-induced dissociation (HCD) fragmentation (normalised collision energy: 29 eV). The fragment ions were measured at high resolution (60,000 FWHM) with a target of 1 x 105 ions per scan and maximum injection time of 200 ms using an isolation window of m/z 1.2 and a dynamic exclusion of 30 s.

Data Processing Protocol

Protein identification and quantification For protein identification and quantification, raw files were imported into MaxQuant v1.6.0.1 (29). The database search engine Andromeda (30) was used to search the HCD-MS/MS spectra against the reviewed UniProtKB Human Protein Database (release April 15, 2015; 45,185 entries) with a tolerance threshold of 4.5 ppm for MS and 20 ppm for HCD-MS/MS. Carbamidomethylation of cysteine (57.021 Da) was set as a fixed modification. Oxidation of methionine (15.994 Da) and protein N-terminal acetylation (42.010 Da) were selected as variable modifications. All identifications were filtered in order to achieve a protein FDR of 1% using a conventional decoy approach. The quantitation of identified proteins was determined by reporter ion intensities using at least one razor/unique peptide. The minimum accepted peptide length was seven amino acid residues. For label-free quantification, the ‘match between runs’ feature of the MaxQuant was enabled with a 0.7 min match time window and 20 min alignment time window. Label-free protein quantification was based on the MaxQuant label-free algorithm by using both unique and razor peptides for the protein quantification; a minimum of two ratio counts was required for a protein quantification to be considered valid. Protein abundance was calculated based on the normalized spectral protein intensity (LFQ intensity) (31). Statistical analyses of the proteome data were performed by using Perseus v. available in the MaxQuant environment. First, proteins identified in the reverse database, potential contaminants and proteins only identified by modified peptides were excluded for further analysis. Statistical tests including the T-test analysis were performed between BPH and each PCa grade. Corrected p < 0.05 was considered as the minimum confidence level to claim statistical significance.


Rebeca Sakuma, University of Sao Paulo
Giuseppe Palmisano, 1Instituto de Ciências Biomédicas, Departamento de Parasitologia, Universidade de São Paulo, USP, São Paulo, Brazil. ( lab head )

Submission Date


Publication Date



    Kawahara R, Recuero S, Nogueira FCS, Domont GB, Leite KRM, Srougi M, Thaysen-Andersen M, Palmisano G. Tissue Proteome Signatures Associated with Five Grades of Prostate Cancer and Benign Prostatic Hyperplasia. Proteomics. 2019:e1900174 PubMed: 31576646