The proteomics of prostate cancer exosomes

Exosomes and other microvesicles are emerging as rich reservoirs of tumor-specific proteins and biomarkers for cancer detection and progression. For prostate cancer, exosomes secreted by the prostate can be isolated from prostatic secretions, seminal fluid, tissue, urine or blood for further proteomic analysis. Structurally, prostate-derived exosomes are distinct in size, membrane composition and specific prostate protein content, potentially providing a novel and easily isolatable source of biomarkers from clinical biofluids. The key to these isolation strategies will be the targeting of specific prostatic proteins expressed in these exosomes, thus requiring detailed proteomic characterizations. A summary of ongoing efforts to characterize the proteome of these unique prostate cancer-associated exosomes and their potential applications for use in biomarker assays is presented.     

Relative quantitation of proteins in expressed prostatic secretion with a stable isotope labeled secretome standard

Expressed prostatic secretion (EPS) is a proximal fluid directly derived from the prostate and, in the case of prostate cancer (PCa), is hypothesized to contain a repertoire of cancer-relevant proteins. Quantitative analysis of the EPS proteome may enable identification of proteins with utility for PCa diagnosis and prognosis. The present investigation demonstrates selective quantitation of proteins in EPS samples from PCa patients using a stable isotope labeled proteome standard (SILAP) generated through the selective harvest of the "secretome" from the PC3 prostate cancer cell line grown in stable isotope labeled cell culture medium. This stable isotope labeled secretome was digested with trypsin and equivalently added to each EPS digest, after which the resultant mixtures were analyzed by liquid chromatography-tandem mass spectrometry for peptide identification and quantification. Relative quantification of endogenous EPS peptides was accomplished by comparison of reconstructed mass chromatograms to those of the chemically identical SILAP peptides. A total of 86 proteins were quantified from 263 peptides in all of the EPS samples, 38 of which were found to be relevant to PCa. This work demonstrates the feasibility of using a SILAP secretome standard to simultaneously quantify many PCa-relevant proteins in EPS samples.     

Detection of pre-neoplastic and neoplastic prostate disease by MALDI profiling of urine

The heterogeneous progression to the development of prostate cancer (PCa) has precluded effective early detection screens. Existing prostate cancer screening paradigms have relatively poor specificity for cancer relative to other prostate diseases, commonly benign prostatic hyperplasia (BPH). A method for discrimination of BPH, HGPIN, and PCa urine proteome was developed through testing 407 patient samples using matrix assisted laser desorption-mass spectrometry time of flight (MALDI-TOF). Urine samples were adsorbed to reverse phase resin, washed, and the eluant spotted directly for MALDI-TOF analysis of peptides. The processing resolved over 130 verifiable signals of a mass range of 1000-5000 m/z to suggest 71.2% specificity and 67.4% sensitivity in discriminating PCa vs. BPH. Comparing BPH and HGPIN resulted in 73.6% specificity and 69.2% sensitivity. Comparing PCa and HGPIN resulted in 80.8% specificity and 81.0% sensitivity. The high throughput, low-cost assay method developed is amenable for large patient numbers required for supporting biomarker identification.     

Search for potential markers for prostate cancer diagnosis, prognosis and treatment in clinical tissue specimens using amine-specific isobaric tagging (iTRAQ) with two-dimensional liquid chromatography and tandem mass spectrometry

This study aimed to identify candidate new diagnosis and prognosis markers and medicinal targets of prostate cancer (PCa), using state of the art proteomics. A total of 20 prostate tissue specimens from 10 patients with benign prostatic hyperplasia (BPH) and 10 with PCa (Tumour Node Metastasis [TNM] stage T1-T3) were analyzed by isobaric stable isotope labeling (iTRAQ) and two-dimensional liquid chromatography-tandem mass spectrometry (2DLC-MS/MS) approaches using a hybrid quadrupole time-of-flight system (QqTOF). The study resulted in the reproducible identification of 825 nonredundant gene products (p < or = 0.05) of which 30 exhibited up-regulation (> or =2-fold) and another 35 exhibited down-regulation (< or =0.5-fold) between the BPH and PCa specimens constituting a major contribution toward their global proteomic assessment. Selected findings were confirmed by immunohistochemical analysis of prostate tissue specimens. The proteins determined support existing knowledge and uncover novel and promising PCa biomarkers. The PCa proteome found can serve as a useful aid for the identification of improved diagnostic and prognostic markers and ultimately novel chemopreventive and therapeutic targets.     

Identification of Differentially Expressed Proteins in Direct Expressed Prostatic Secretions of Men with Organ-confined Versus Extracapsular Prostate Cancer

Current protocols for the screening of prostate cancer cannot accurately discriminate clinically indolent tumors from more aggressive ones. One reliable indicator of outcome has been the determination of organ-confined versus nonorgan-confined disease but even this determination is often only made following prostatectomy. This underscores the need to explore alternate avenues to enhance outcome prediction of prostate cancer patients. Fluids that are proximal to the prostate, such as expressed prostatic secretions (EPS), are attractive sources of potential prostate cancer biomarkers as these fluids likely bathe the tumor. Direct-EPS samples from 16 individuals with extracapsular (n = 8) or organ-confined (n = 8) prostate cancer were used as a discovery cohort, and were analyzed in duplicate by a nine-step MudPIT on a LTQ-Orbitrap XL mass spectrometer. A total of 624 unique proteins were identified by at least two unique peptides with a 0.2% false discovery rate. A semiquantitative spectral counting algorithm identified 133 significantly differentially expressed proteins in the discovery cohort. Integrative data mining prioritized 14 candidates, including two known prostate cancer biomarkers: prostate-specific antigen and prostatic acid phosphatase, which were significantly elevated in the direct-EPS from the organ-confined cancer group. These and five other candidates (SFN, MME, PARK7, TIMP1, and TGM4) were verified by Western blotting in an independent set of direct-EPS from patients with biochemically recurrent disease (n = 5) versus patients with no evidence of recurrence upon follow-up (n = 10). Lastly, we performed proof-of-concept SRM-MS-based relative quantification of the five candidates using unpurified heavy isotope-labeled synthetic peptides spiked into pools of EPS-urines from men with extracapsular and organ-confined prostate tumors. This study represents the first efforts to define the direct-EPS proteome from two major subclasses of prostate cancer using shotgun proteomics and verification in EPS-urine by SRM-MS.Prostate cancer is the most common malignancy to affect men in the Western world, but only 15–20% of these men will present with aggressive, lethal disease (1, 2) whereas the majority of patients will die of other causes. Although the implementation of large-scale screening for prostate cancer using serum prostate-specific antigen (PSA) has dramatically improved early detection of disease, unnecessary biopsies and patient overtreatment are becoming increasingly evident (2, 3). Consequently, there has been a shift in emphasis away from detection of prostate cancer and toward identification of lethal disease. Currently, Gleason grading is considered to be one of the best outcome predictors; however, patients with Gleason 7 tumors are in the clinical “gray zone,” whereby the predictive ability of Gleason grading is mixed (4, 5). A recent study constructed a 157-gene signature based on the comparison of Gleason score ≤6 and ≥8 patients, and could show that their panel could predict lethality in the cohort of Gleason 7 patients (5). Nonetheless, the development and large-scale implementation of prognostic markers of prostate cancer has been hampered by numerous factors owing, in part, to the heterogeneous and multifocal nature of the disease (6). Although the widely used Gleason grading system attempts to control for heterogeneity of the glands and multifocality of cancerous lesions by summing the 2–3 most commonly observed histological patterns via inspection of multiple (typically 8–12) core biopsies, cancerous foci are still often missed (2, 6) providing only partial information that can lead to imprecise diagnoses and prognoses. Pathologic staging remains the gold standard for disease staging and risk assessment (7, 8); however, this process lacks timeliness in discriminating organ-confined from extracapsular disease. Indeed, one-third of individuals with nonorgan-confined disease are identified only after surgery (9). Furthermore, ∼35% of men treated with radical prostatectomy with curative intent subsequently develop biochemical recurrence (10–13) and the mean time from surgery to recurrence is 3.5 years (4). Significant risk factors for time to prostate-specific mortality following biochemical recurrence after radical prostatectomy are PSA doubling time, pathological Gleason score, and time from surgery to biochemical recurrence (4). Estimates place the percent of lethal cases at 20–25% of all patients that show biochemical recurrence, suggesting that nearly 75–80% of patients in this group may be overtreated (14).There is an emerging trend toward recruitment of men with perceived low-risk disease to an “active surveillance” monitoring approach. This is based on the supposition that most prostate cancers are slow growing, and that the more aggressive forms can be identified during a period of observation with little increased risk of death. Although a consensus may not exist for defining the disease stage where active surveillance is warranted, there is considerable agreement that men who have a PSA level less than 10 ng/ml, impalpable disease (clinical stage T1c) and only 1 biopsy core out of 12 or more that show Gleason 6 cancer are most likely to harbor indolent disease (15). Even so, these candidates for active surveillance will still contain individuals who will have disease progression and die from their cancer. Thus, despite efforts to recruit individuals to active surveillance protocols, overtreatment of prostate cancer is fueled by the lack of reliable means to accurately discriminate between men with clinically indolent prostate cancer from those with more aggressive disease (16, 17). This inability to accurately predict prostate cancer aggressiveness based solely on standard clinicopathologic features clearly underscores the need to explore the ability of additional biomarkers to enhance outcome prediction for men with prostate cancer. Furthermore, it is important to acknowledge that a single biomarker alone is unlikely to have sufficient prognostic power; rather, the integration of a panel of biomarkers hold the promise for improved prostate cancer detection and prognosis (2).Fluids that are proximal to the prostate are attractive sources of potential prostate cancer biomarkers (2, 18), as they house secreted proteins and sloughed cells that provide a presumably more comprehensive assessment of the organ and extent of disease. Further, fluids such as urine are clinically favorable for their ease of collection, the volume and frequency at which they can be obtained, and their adaptability to routine clinical assays. Prostate-proximal fluids include seminal fluid, semen, and expressed prostatic secretions (EPS)¹. Here, we focus on the analysis of EPS as our biological specimen, using direct-EPS samples for the discovery of candidate prognostic biomarkers and both direct-EPS and pooled EPS-urines derived from independent sets of patients for candidate biomarker verification. Direct-EPS is a prostatic fluid that is collected from patients undergoing prostatectomy by massaging the organ and expelling 0.5–1 ml of the fluid just prior to surgical removal. It was chosen as our discovery fluid as it is expected to house prostate-secreted proteins at a higher concentration and purity, and we have developed a workflow for the in-depth proteomic analysis of this fluid (19). Following discovery proteomics in 16 clinically stratified direct-EPS samples, verification studies were performed using independent sample sets of direct-EPS. Next, we focused our attention on the verification and quantitative analysis of candidate proteins in pooled EPS-urines. Before EPS-urine collection, men undergo digital rectal examination (DRE), often as part of a routine procedure, which causes direct-EPS to be expelled from the prostate and subsequently voided in urine. Because EPS-urine can be collected with substantial ease and in greater volumes and frequencies than direct-EPS, much attention has been paid to this fluid as a valuable resource of prostate cancer biomarkers amenable to routine clinical analysis. Following the recent FDA approval of the EPS-urine assay for prostate cancer gene 3 (PCA3), standardized clinical collection protocols will be widely implemented and easier access to this fluid is expected. Moreover, we have recently identified a number of prostate-enriched proteins in EPS-urine by comparing its proteome to a urine background (20).The present study used multidimensional protein identification technology (MudPIT) coupled with bioinformatics to first catalog and comparatively analyze the direct-EPS proteomes from a small cohort of patients with extracapsular versus organ-confined prostate cancers. A semiquantitative algorithm based on spectral counts (QSpec) (21) and an integrative data mining strategy led to the selection of a number of putative biomarkers that were verified by Western blotting in direct-EPS. Lastly, to demonstrate accurate quantitative measurements of verified candidates in EPS-urine, a pilot study utilizing SRM-MS was undertaken as a proof-of-concept.     

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

The histology‐based Gleason score (GS) of prostate cancer (PCa) tissue biopsy is the most accurate predictor of disease aggressiveness and an important measure to guide treatment strategies and patient management. The variability associated with PCa tumor sampling and the subjective determination of the GS are challenges that limit accurate diagnostication and prognostication. Thus, novel molecular signatures are needed to distinguish between indolent and aggressive forms of PCa for better patient management and outcomes. Herein, label‐free LC‐MS/MS proteomics is used to profile the proteome of 50 PCa tissues spanning five grade groups (n = 10 per group) relative to tissues from individuals with benign prostatic hyperplasia (BPH). Over 2000 proteins are identified albeit at different levels between and within the patient groups, revealing biological processes associated with specific grades. A panel of 11 prostate‐derived proteins including IGKV3D‐20, RNASET2, TACC2, ANXA7, LMOD1, PRCP, GYG1, NDUFV1, H1FX, APOBEC3C, and CTSZ display the potential to stratify patients from low and high PCa grade groups. Parallel reaction monitoring of the same sample cohort validate the differential expression of LMOD1, GYG1, IGKV3D‐20, and RNASET2. The four proteins associated with low and high PCa grades reported here warrant further exploration as candidate biomarkers for PCa aggressiveness.     

Human body fluid proteome analysis

The focus of this article is to review the recent advances in proteome analysis of human body fluids, including plasma/serum, urine, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, synovial fluid, nipple aspirate fluid, tear fluid, and amniotic fluid, as well as its applications to human disease biomarker discovery. We aim to summarize the proteomics technologies currently used for global identification and quantification of body fluid proteins, and elaborate the putative biomarkers discovered for a variety of human diseases through human body fluid proteome (HBFP) analysis. Some critical concerns and perspectives in this emerging field are also discussed. With the advances made in proteomics technologies, the impact of HBFP analysis in the search for clinically relevant disease biomarkers would be realized in the future.     

Proteomic analysis of formalin-fixed prostate cancer tissue

Proteomic analysis of formalin-fixed paraffin-embedded (FFPE) tissue would enable retrospective biomarker investigations of this vast archive of pathologically characterized clinical samples that exist worldwide. These FFPE tissues are, however, refractory to proteomic investigations utilizing many state of the art methodologies largely due to the high level of covalently cross-linked proteins arising from formalin fixation. A novel tissue microdissection technique has been developed and combined with a method to extract soluble peptides directly from FFPE tissue for mass spectral analysis of prostate cancer (PCa) and benign prostate hyperplasia (BPH). Hundreds of proteins from PCa and BPH tissue were identified, including several known PCa markers such as prostate-specific antigen, prostatic acid phosphatase, and macrophage inhibitory cytokine-1. Quantitative proteomic profiling utilizing stable isotope labeling confirmed similar expression levels of prostate-specific antigen and prostatic acid phosphatase in BPH and PCa cells, whereas the expression of macrophage inhibitory cytokine-1 was found to be greater in PCa as compared with BPH cells.     

Characterization of the human gastric fluid proteome reveals distinct pH-dependent protein profiles: implications for biomarker studies

Gastric fluid is a source of gastric cancer biomarkers. However, very little is known about the normal gastric fluid proteome and its biological variations. In this study, we performed a comprehensive analysis of the human gastric fluid proteome using samples obtained from individuals with benign gastric conditions. Gastric fluid proteins were prefractionated using ultracentrifuge filters (3 kDa cutoff) and analyzed by two-dimensional gel electrophoresis (2-DE) and multidimensional LC-MS/MS. Our 2-DE analysis of 170 gastric fluid samples revealed distinct protein profiles for acidic and neutral samples, highlighting pH effects on protein composition. By 2D LC-MS/MS analysis of pooled samples, we identified 284 and 347 proteins in acidic and neutral samples respectively (FDR ≤1%), of which 265 proteins (72.4%) overlapped. However, unlike neutral samples, most proteins in acidic samples were identified from peptides in the filtrate (i.e., <3 kDa). Consistent with this finding, immunoblot analysis of six potential gastric cancer biomarkers rarely detected full-length proteins in acidic samples. These findings have important implications for biomarker studies because a majority of gastric cancer patients have neutral gastric fluid compared to noncancer controls. Consequently, sample stratification, choice of proteomic approaches, and validation strategy can profoundly affect the interpretation of biomarker findings. These observations should help to refine gastric fluid biomarker studies.     

A proteomic glimpse into human ureter proteome

Urine has evolved as one of the most important biofluids in clinical proteomics due to its noninvasive sampling and its stability. Yet, it is used in clinical diagnostics of several disorders by detecting changes in its components including urinary protein/polypeptide profile. Despite the fact that majority of proteins detected in urine are primarily originated from the urogenital (UG) tract, determining its precise source within the UG tract remains elusive. In this article, we performed a comprehensive analysis of ureter proteome to assemble the first unbiased ureter dataset. Next, we compared these data to urine, urinary exosome, and kidney mass spectrometric datasets. Our result concluded that among 2217 nonredundant ureter proteins, 751 protein candidates (33.8%) were detected in urine as urinary protein/polypeptide or exosomal protein. On the other hand, comparing ureter protein hits (48) that are not shown in corresponding databases to urinary bladder and prostate human protein atlas databases pinpointed 21 proteins that might be unique to ureter tissue. In conclusion, this finding offers future perspectives for possible identification of ureter disease‐associated biomarkers such as ureter carcinoma. In addition, the ureter proteomic dataset published in this article will provide a valuable resource for researchers working in the field of urology and urine biomarker discovery. All MS data have been deposited in the ProteomeXchange with identifier PXD002620 ( http://proteomecentral.proteomexchange.org/dataset/PXD002620 ).     

Quantitative analysis of the intra- and inter-individual variability of the normal urinary proteome

Urine is a readily and noninvasively obtainable body fluid. Mass spectrometry (MS)-based proteomics has shown that urine contains thousands of proteins. Urine is a potential source of biomarkers for diseases of proximal and distal tissues but it is thought to be more variable than the more commonly used plasma. By LC-MS/MS analysis on an LTQ-Orbitrap without prefractionation we characterized the urinary proteome of seven normal human donors over three consecutive days. Label-free quantification of triplicate single runs covered the urinary proteome to a depth of more than 600 proteins. The median coefficient of variation (cv) of technical replicates was 0.18. Interday variability was markedly higher with a cv of 0.48 and the overall variation of the urinary proteome between individuals was 0.66. Thus technical variability in our data was 7.5%, whereas intrapersonal variability contributed 45.5% and interpersonal variability contributed 47.1% to total variability. Determination of the normal fluctuation of individual urinary proteins should be useful in establishing significance thresholds in biomarker studies. Our data also allowed definition of a common and abundant set of 500 proteins that were readily detectable in all studied individuals. This core urinary proteome has a high proportion of secreted, membrane, and relatively high-molecular weight proteins.     

Proteomics analysis of bodily fluids in pancreatic cancer

Proteomics study of pancreatic cancer using bodily fluids emphasizes biomarker discovery and clinical application, presenting unique prospect and challenges. Depending on the physiological nature of the bodily fluid and its proximity to pancreatic cancer, the proteomes of bodily fluids, such as pancreatic juice, pancreatic cyst fluid, blood, bile, and urine, can be substantially different in terms of protein constitution and the dynamic range of protein concentration. Thus, a comprehensive discovery and specific detection of cancer‐associated proteins within these varied fluids is a complex task, requiring rigorous experiment design and a concerted approach. While major challenges still remain, fluid proteomics studies in pancreatic cancer to date have provided a wealth of information in revealing proteome alterations associated with pancreatic cancer in various bodily fluids.     

Seminal Plasma as a Source of Prostate Cancer Peptide Biomarker Candidates for Detection of Indolent and Advanced Disease

Background

Extensive prostate specific antigen screening for prostate cancer generates a high number of unnecessary biopsies and over-treatment due to insufficient differentiation between indolent and aggressive tumours. We hypothesized that seminal plasma is a robust source of novel prostate cancer (PCa) biomarkers with the potential to improve primary diagnosis of and to distinguish advanced from indolent disease.

Methodology/Principal Findings

In an open-label case/control study 125 patients (70 PCa, 21 benign prostate hyperplasia, 25 chronic prostatitis, 9 healthy controls) were enrolled in 3 centres. Biomarker panels a) for PCa diagnosis (comparison of PCa patients versus benign controls) and b) for advanced disease (comparison of patients with post surgery Gleason score <7 versus Gleason score >7) were sought. Independent cohorts were used for proteomic biomarker discovery and testing the performance of the identified biomarker profiles. Seminal plasma was profiled using capillary electrophoresis mass spectrometry. Pre-analytical stability and analytical precision of the proteome analysis were determined. Support vector machine learning was used for classification. Stepwise application of two biomarker signatures with 21 and 5 biomarkers provided 83% sensitivity and 67% specificity for PCa detection in a test set of samples. A panel of 11 biomarkers for advanced disease discriminated between patients with Gleason score 7 and organ-confined (<pT3a) or advanced (≥pT3a) disease with 80% sensitivity and 82% specificity in a preliminary validation setting. Seminal profiles showed excellent pre-analytical stability. Eight biomarkers were identified as fragments of N-acetyllactosaminide beta-1,3-N-acetylglucosaminyltransferase, prostatic acid phosphatase, stabilin-2, GTPase IMAP family member 6, semenogelin-1 and -2. Restricted sample size was the major limitation of the study.

Conclusions/Significance

Seminal plasma represents a robust source of potential peptide makers for primary PCa diagnosis. Our findings warrant further prospective validation to confirm the diagnostic potential of identified seminal biomarker candidates.     

Analysis of the urine proteome via a combination of multi-dimensional approaches

Urine is a biological fluid that is non-invasively and easily harvested, and exhibits high stability from the proteomics point of view. At the downside, the overall low protein content of urine as well as the presence of low- and high-abundance proteins underscores the need for protein enrichment. As a continuation of previous efforts towards the comprehensive characterization of the urine proteome, the current study targeted the mining of urine proteins through the combined application of different protein separation methodologies, specifically, liquid chromatography and preparative electrophoresis along with 1D gel electrophoresis and protein identification by mass spectrometry. In order to enhance comparison and integration of different experimental data sets, the "standard" urine sample developed within the European Kidney and Urine Proteomics (EuroKUP) COST Action, was employed. As a contribution to the existing knowledge, we focused on maintaining and providing information about experimental mass of the identified proteins as well as information pertaining to their relative abundance--as allowed by technical limitations--thus providing an initial view of different isoforms representation and facilitating their future characterization. The difficulties in comparing proteome mining data sets become once more evident, underscoring the need for adopting standardized ways for data reporting as well as for potential new approaches for data analysis involving a thorough investigation of received information at the peptide level.     

Biomarker Discovery in Human Prostate Cancer: an Update in Metabolomics Studies

Prostate cancer (PCa) is the most frequently diagnosed cancer and the second leading cause of cancer death among men in Western countries. Current screening techniques are based on the measurement of serum prostate specific antigen (PSA) levels and digital rectal examination. A decisive diagnosis of PCa is based on prostate biopsies; however, this approach can lead to false-positive and false-negative results. Therefore, it is important to discover new biomarkers for the diagnosis of PCa, preferably noninvasive ones. Metabolomics is an approach that allows the analysis of the entire metabolic profile of a biological system. As neoplastic cells have a unique metabolic phenotype related to cancer development and progression, the identification of dysfunctional metabolic pathways using metabolomics can be used to discover cancer biomarkers and therapeutic targets. In this study, we review several metabolomics studies performed in prostatic fluid, blood plasma/serum, urine, tissues and immortalized cultured cell lines with the objective of discovering alterations in the metabolic phenotype of PCa and thus discovering new biomarkers for the diagnosis of PCa. Encouraging results using metabolomics have been reported for PCa, with sarcosine being one of the most promising biomarkers identified to date. However, the use of sarcosine as a PCa biomarker in the clinic remains a controversial issue within the scientific community. Beyond sarcosine, other metabolites are considered to be biomarkers for PCa, but they still need clinical validation. Despite the lack of metabolomics biomarkers reaching clinical practice, metabolomics proved to be a powerful tool in the discovery of new biomarkers for PCa detection.     

Periostin identified as a potential biomarker of prostate cancer by iTRAQ-proteomics analysis of prostate biopsy

Background  

Proteomics may help us better understand the changes of multiple proteins involved in oncogenesis and progression of prostate cancer(PCa) and identify more diagnostic and prognostic biomarkers. The aim of this study was to screen biomarkers of PCa by the proteomics analysis using isobaric tags for relative and absolute quantification(iTRAQ).     

The study of DJ-1 protein in tissue specimens,cultured cells and serum of prostate cancer patients

Two electrophoretically distinguishable isoforms of Dj-1 protein have been identified in a proteomic study of tissue specimens obtained from patients with confirmed prostate cancer (PCa) and benign prostatic hyperplasia (BPH). Dj-1 was also found in the cell lines PC-3, DU-145, LNCaP, BPH-1, and the lowest level of Dj-1 was found in BPH-1. An immunochemical study (ELISA) of serum levels of Dj-1, Bcl-2, IGF-1 and IGFBP-3 proteins revealed statistically significant differences between these two groups of patients only for Dj-1 (p = 0.004, the Wilcoxon-Mann-Whitney test). These data suggest that Dj-1 protein is a perspective candidate biomarker for PCa.