Urinary Proteome Research Articles

Urine proteome changes in a chronic unpredictable mild stress (CUMS) mouse model of major depressive disorder

Major depressive disorder (MDD) is a prevalent complex psychiatric disorder, and there are no effective biomarkers for clinical diagnosis. Urine is not subjected to homeostatic control, allowing it to reflect the sensitive changes that occur in various diseases. In this study, we examined the urine proteome changes in a chronic unpredictable mild stress mouse model of MDD. Male C57BL/6 mice were subjected to chronic unpredictable mild stress for 5 weeks. The tail suspension test and sucrose consumption test were then applied to evaluate depression-like behaviors. The urine proteomes on day 0 and day 36 in the CUMS group were profiled by liquid chromatography coupled with tandem mass spectrometry (LCMS/MS). A total of 36 differential proteins were identified, 19 of which have been associated with the pathogenic mechanisms of MDD. There was an average of two differential proteins that were identified through 1,048,574 random combination statistical analyses, indicating that at least 95 % of the differential proteins were reliable. The differential proteins were mainly associated with blood coagulation, inflammatory responses and central nervous system development. Our preliminary results indicated that the urine proteome can reflect changes associated with MDD in the CUMS model, which provides potential clues for the diagnosis of MDD.

A NOVEL URINARY BIOMARKER PREDICTS 1 YEAR MORTALITY AFTER DISCHARGE FROM INTENSIVE CARE

Objective: The urinary proteome reflects molecular drivers of disease. The aim of this study was to construct a urinary proteomic biomarker predicting 1 year post ICU mortality. Design and method: In 1243 patients, the urinary proteome was measured on ICU admission, using capillary electrophoresis couple with mass spectrometry along with clinical variables, circulating biomarkers (BNP, hsTnT, active ADM, and NGAL) and urinary albumin. Methods included support vector modelling to construct the classifier, Cox regression, the integrated discrimination (IDI) and net reclassification (NRI) improvement and area under the curve (AUC) to assess predictive accuracy, and Proteasix and protein-proteome interactome analyses. Results: In the discovery (deaths/survivors, 70/299) and test (175/699) datasets, the new classifier ACM128, mainly consisting of collagen fragments, yielding AUCs of 0.755 (95% CI, 0.708–0.798) and 0.688 (0.656–0.719), respectively. While accounting for study site, clinical risk factors, hazard ratios in 1243 patients were 2.41 (2.00–2.91) for ACM128 (+1 SD), 1.24 (1.16–1.32) for the Charlson Comorbidity Index (+1 point), and > = 1.19 (P < = 0.022) for other biomarkers (+1 SD). ACM128 improved (P < = 0.0001) IDI (>= + 0.50), NRI (>= + 53.7) and AUC (>= + 0.037) over and beyond clinical risk indicators and other biomarkers. Interactome mapping, using parental proteins derived from sequenced peptides included in ACM128 and in-silico predicted proteases, including/excluding urinary collagen fragments (63/35 peptides), revealed as top molecular pathways protein digestion and absorption, lysosomal activity and apoptosis. Conclusions: The urinary proteomic classifier ACM128 predicts the 1-year post ICU mortality over and beyond clinical risk factors and other biomarkers and revealed molecular pathways potentially contributing to a fatal outcome.

Current applications of artificial intelligence combined with urine detection in disease diagnosis and treatment.

In recent years, the advantages of artificial intelligence (AI) in data processing and model analysis have emerged in the medical field, enabled by computer technology developments and the integration of multiple disciplines. The application of AI in the medical field has gradually deepened and broadened. Among them, the development of clinical medicine intelligent decision-making is the fastest. The advantage of clinical medicine intelligent decision-making is to make the diagnosis faster and more accurate on the basis of certain information. Urine detection technologies, such as urine proteomics, urine metabolomics, and urine RNomics, have developed rapidly with the advancements in omics and medical tests. Advances in urine testing have made it possible to obtain a wealth of information from easily accessible urine. However, it has always been a problem to extract effective information from this information and use it. AI technology provides the possibility to process and use the information in urine. AI, combined with urine detection, not only provides new possibilities for precise and individual diagnosis and disease treatment, but also helps promote non-invasive diagnosis and treatment. This article reviews the research and applications of AI combined with urine detection for disease diagnosis and treatment and discusses its existing problems and future development.

A machine learning analysis identifies three candidate urine-based biomarkers for assessing osteoarthritis treatments

A machine learning analysis identifies three candidate urine-based biomarkers for assessing osteoarthritis treatments

Proteomics Profiling of the Urine of Patients with Hyperthyroidism after Anti-Thyroid Treatment.

Hyperthyroidism, which is characterized by increased circulating thyroid hormone levels, alters the body’s metabolic and systemic hemodynamic balance and directly influences renal function. In this study, the urinary proteome of patients with hyperthyroidism was characterized using an untargeted proteomic approach with network analysis. Urine samples were collected from nine age-matched patients before and after carbimazole treatment. Differences in the abundance of urinary proteins between hyperthyroid and euthyroid states were determined using a 2D-DIGE coupled to MALDI-TOF mass spectrometry. Alterations in the abundance of urinary proteins, analyzed via Progenesis software, revealed a statistically significant difference in abundance in a total of 40 spots corresponding to 32 proteins, 25 up and 7 down (≥1.5-fold change, ANOVA, p ≤ 0.05). The proteins identified in the study are known to regulate processes associated with cellular metabolism, transport, and acute phase response. The notable upregulated urinary proteins were serotransferrin, transthyretin, serum albumin, ceruloplasmin, alpha-1B-glycoprotein, syntenin-1, and glutaminyl peptide cyclotransferase, whereas the three notable downregulated proteins were plasma kallikrein, protein glutamine gamma-glutamyl transferase, and serpin B3 (SERPINB3). Bioinformatic analysis using ingenuity pathway analysis (IPA) identified the dysregulation of pathways associated with cellular compromise, inflammatory response, cellular assembly, and organization and identified the involvement of the APP and AKT signaling pathways via their interactions with interleukins as the central nodes.

Urinary Proteomic Characteristics of Hyperuricemia and Their Possible Links with the Occurrence of Its Concomitant Diseases.

Hyperuricemia (HUA), a chronic disease caused by metabolic disorders of purine, is often accompanied by other diseases such as gout, type 2 diabetes mellitus (T2DM), and hyperlipidemia. However, little is known about the relationship between HUA and these diseases on the protein level. We performed label-free liquid chromatography MS/MS spectrometry analysis of urine samples from 26 HUA patients and 25 healthy controls, attempting to establish the possible protein links between HUA and these diseases by profiling urine proteome. A total of 2119 proteins were characterized in sample proteomes. Among them, 11 were found decreased and 2 were found increased in HUA samples. Plausible pathways found by enrichment analysis of these differentially expressed proteins (DEPs) include the processes for insulin receptor recycling and lipid metabolism, suggesting potential links between HUA and T2DM and hyperlipidemia. The abundance changes of three key proteins (VATB1, CFAD, and APOC3) involved in these processes were validated by enzyme-linked immunosorbent assay (ELISA). In conclusion, our result provides proteomic evidence, for the first time, that the aberrant pathways enriched by described key DEPs are closely related to the incidence of HUA and its concomitant diseases.

Urinary epidermal growth factor is a novel biomarker for early diagnosis of antibody mediated kidney allograft rejection: A urinary proteomics analysis

Although antibody mediated rejection (AMR) accounts for 20–30% of all acute renal allograft rejections, introducing biomarkers for a timely detection of allograft rejection has been remained challenging. This study investigated novel diagnostic biomarkers of AMR by examining of urine proteome in renal transplant patients. Thirty-six patients with kidney transplantation including 22 AMR patients and 14 patients with stable renal function (control group) were enrolled in this study. Urinary samples were collected and Label free quantification (LFQ) proteomics technique was applied on urine samples and data was subjected to Random Forest (RF) algorithm to predict main candidate proteins contributing in AMR. Finally, applicability of candidate diagnostic biomarkers was evaluated in new sets of AMR subjects, stable patients and healthy volunteers. A total of 1020 proteins were detected in urine proteome. RF algorithm predicted 20 differentially expressed proteins with the highest sensitivity and specificity and combination of EGF, COL6A, and NID-1 was identified as possible panel for early diagnosis of AMR. Applicability of EGF as diagnostic biomarker was validated in urine samples of independent set of AMR subjects. This is the first urinary proteomics study in AMR patients demonstrating that urinary EGF might be used as early diagnostic biomarker for AMR. SignificanceRenal antibody mediated rejection (AMR) accounts for 20–30% of all acute rejections of allografted kidneys. Although several possible biomarkers have been proposed to predict AMR, ineffectiveness of current urinary biomarkers in early diagnosing of AMR patients and in distinguishing AMR subjects from patients with stable kidney function casts doubts on their applicability in clinic. Here for the first time and based on the analysis of urinary proteome we showed that uEGF and uEGF/Cr might be candidate biomarkers to predict AMR with high diagnostic power.

PRYNT: a tool for prioritization of disease candidates from proteomics data using a combination of shortest-path and random walk algorithms

The urinary proteome is a promising pool of biomarkers of kidney disease. However, the protein changes observed in urine only partially reflect the deregulated mechanisms within kidney tissue. In order to improve on the mechanistic insight based on the urinary protein changes, we developed a new prioritization strategy called PRYNT (PRioritization bY protein NeTwork) that employs a combination of two closeness-based algorithms, shortest-path and random walk, and a contextualized protein–protein interaction (PPI) network, mainly based on clique consolidation of STRING network. To assess the performance of our approach, we evaluated both precision and specificity of PRYNT in prioritizing kidney disease candidates. Using four urinary proteome datasets, PRYNT prioritization performed better than other prioritization methods and tools available in the literature. Moreover, PRYNT performed to a similar, but complementary, extent compared to the upstream regulator analysis from the commercial Ingenuity Pathway Analysis software. In conclusion, PRYNT appears to be a valuable freely accessible tool to predict key proteins indirectly from urinary proteome data. In the future, PRYNT approach could be applied to other biofluids, molecular traits and diseases. The source code is freely available on GitHub at: https://github.com/Boizard/PRYNT and has been integrated as an interactive web apps to improved accessibility (https://github.com/Boizard/PRYNT/tree/master/AppPRYNT).

Proteomic analysis of urine reveals biomarkers for the diagnosis and phenotyping of abdominal-type Henoch-Schonlein purpura.

BackgroundAbdominal-type Henoch-Schonlein purpura (HSP) is a common refractory disease in children. Currently, no specific diagnostic biomarker is available for HSP.MethodsChildren with abdominal type HSP were first diagnosed with three syndromes using Chinese traditional medicine. The urinary proteomes among the three syndromes of patients with abdominal type HSP and healthy controls were compared using two label-free proteomics quantifications, including data-dependent acquisition and data-independent acquisition.ResultsFor the comparison between patients with abdominal type HSP and healthy children, a total of 75 differential urinary proteins were identified by determining the overlap of the two experiments. The ingenuity pathway analysis (IPA) analysis showed that these differential proteins were correlated with the pathogenesis of abdominal type HSP. Of these, 37 proteins were distributed in 13 solid tissues as tissue-enriched proteins. Monitoring changes in these proteins might help us detect uncommon clinical manifestations of HSP. Patients with abdominal type HSP can be further distinguished into three syndromes based on the urine proteome. Finally, a panel of six urinary proteins (P25774, P09417, Q7Z5L0, P60900, P14550 and P09668) was constructed for both the diagnosis and phenotyping of abdominal type HSP.ConclusionsUrinary protein biomarkers for the diagnosis and phenotyping of abdominal type HSP were identified, which will contribute to the personalized treatment of patients with abdominal type HSP.

Comparison of urine proteome in rats intraperitoneally infected by different microorganisms

Different microorganisms can cause intraperitoneal infection. This study was to distinguish different microbial infections by urine analysis. Rats were intraperitoneally injected with Escherichia coli, Staphylococcus aureus, and Candida albicans, separately. Urine samples were collected from rats at 0, 12, 36 and 72 h after infection. Urinary proteins were profiled using liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS). Compared with the control (without infection), a total of 69 differential proteins were identified in rats injected with E. coli. A total of 31 differences proteins were identified in rats injected with S. aureus. A total of 38 differential proteins were identified in rats injected with C. albicans. Urine proteome was different when rats were infected by different microorganisms, suggesting that urine may have the potential for differential diagnosis of different intraperitoneal infections.

Urinary proteome profiling for stratifying patients with familial Parkinson’s disease

The prevalence of Parkinson's disease (PD) is increasing but the development of novel treatment strategies and therapeutics altering the course of the disease would benefit from specific, sensitive, and non‐invasive biomarkers to detect PD early. Here, we describe a scalable and sensitive mass spectrometry (MS)‐based proteomic workflow for urinary proteome profiling. Our workflow enabled the reproducible quantification of more than 2,000 proteins in more than 200 urine samples using minimal volumes from two independent patient cohorts. The urinary proteome was significantly different between PD patients and healthy controls, as well as between LRRK2 G2019S carriers and non‐carriers in both cohorts. Interestingly, our data revealed lysosomal dysregulation in individuals with the LRRK2 G2019S mutation. When combined with machine learning, the urinary proteome data alone were sufficient to classify mutation status and disease manifestation in mutation carriers remarkably well, identifying VGF, ENPEP, and other PD‐associated proteins as the most discriminating features. Taken together, our results validate urinary proteomics as a valuable strategy for biomarker discovery and patient stratification in PD.

Identification of Protein Changes in the Urine of Hypothyroid Patients Treated with Thyroxine Using Proteomics Approach.

The thyroid gland and thyroid hormones control a multitude of homeostatic functions including maintenance of fluid and electrolyte balance and normal functioning of the kidneys. Thyroid dysfunction alters the sytemic hemodynamic and metabolic balance, thereby affecting the kidney. In this study, we aimed to identify and characterize the urinary proteome of the patients with hypothyroidism. An untargeted proteomic approach with network analysis was used to identify changes in total urinary proteome in patients with newly diagnosed overt hypothyroidism. Urine samples were collected from nine age-matched patients’ before and after l-thyroxine treatment. Differences in the abundance of urinary proteins between hypothyroid and euthyroid states were determined using a two-dimensional difference in gel electrophoresis (2D-DIGE) coupled to matrix-assisted laser desorption and ionization time-of-flight (MALDI TOF) mass spectrometry. Alterations in the abundance of urinary proteins, analyzed by Progenesis software, revealed statistically significant differential abundance in a total of 49 spots corresponding to 42 proteins, 28 up and 14 down (≥1.5-fold change, analysis of variance (ANOVA), p ≤ 0.05). The proteins identified in the study are known to regulate processes related to transport, acute phase response, oxidative stress, generation of reactive oxygen species, cellular proliferation, and endocytosis. Bioinformatic analysis using Ingenuity Pathway Analysis (IPA) identified dysregulation of pathways related to amino acid metabolism, molecular transport, and small-molecule biochemistry and involved the MAPK kinase, vascular endothelial growth factor (VEGF), PI3 kinase/Akt, protein kinase C (PKC), signaling pathways. The identified proteins were involved in the regulation of thyroglobulin (Tg) and thyrotropin (TSH) metabolism. Alterations in their levels indicate the presence of a compensatory mechanism aimed at increasing the regulation of Tg in the hypothyroid state.

Urinary proteomic profiles of prostate cancer with different risk of progression and correlation with histopathological features

Prostate cancer (PCa) is the most common tumor in men with extremely variable outcome, varying from latent or indolent form to very aggressive behavior. High grade tumors, expansions exceeding the prostatic capsule into the surrounding soft tissues and spreading through lymph vascular channels, represent the most consistent unfavorable prognostic factors. However, accuracy in the prediction of the disease progression is sometimes difficult. Along with new molecular diagnostic techniques and more accurate histopathological approaches, proteomic studies challenge to identify potential biomarkers predictive of PCa progression. In our study we analyzed the urinary proteomes of 42 patients affected by PCa through two-dimensional electrophoresis associated with mass spectrometry. Proteomic profiles were correlated to histopathological features including pTNM stage and tumor differentiation in order to provide new promising markers able to define more accurately the PCa aggressiveness and driving new therapeutic approaches.

Dynamic Changes of Urine Proteome in Rat Models Inoculated with Two Different Hepatoma Cell Lines.

Urine can accumulate systemic changes with no mechanism to be stable, which may reflect early changes associated with physiological or pathophysiological processes. To explore the potential value of the urine proteome, two rat models were established by intrahepatic injection of two different hepatoma cell lines, CBRH-7919 and RH-35. Urine samples were collected and analyzed. Compared with controls, the two models exhibited different numbers and types of differentially expressed urinary proteins despite having similar histological results. The results were compared with the urine proteome of a Walker 256 (W-256) liver tumor model. The differentially expressed urinary protein patterns in the three models were different. These findings demonstrate that changes in the urine proteomes of the two models can be detected at early stages and that the patterns of differentially expressed urinary proteins can differ even when the histological results are similar. Urinary proteins have potential utility for distinguishing among different tumor cells grown in the same organ.

Urinary proteomics reveals key markers of salt sensitivity in hypertensive patients during saline infusion.

Hypertension is a complex disease and is the major cause of cardiovascular complications. In the vast majority of individuals, the aetiology of elevated blood pressure (BP) cannot be determined, thus impairing optimized therapies and prognosis for individual patients. A more precise understanding of the molecular pathogenesis of hypertension remains a pressing priority for both basic and translational research. Here we investigated the effect of salt on naive hypertensive patients in order to better understand the salt intake-blood pressure relationship. Patients underwent an acute saline infusion and were defined as salt-sensitive or salt-resistant according to mean blood pressure changes. Urinary proteome changes during the salt load test were analysed by a label-free quantitative proteomics approach. Our data show that salt-sensitive patients display equal sodium reabsorption as salt-resistant patients, as major sodium transporters show the same behaviour during the salt load. However, salt-sensitive patients regulate the renin angiotensin system (RAS) differently from salt-resistant patients, and upregulate proteins, as epidermal growth factor (EGF) and plasminogen activator, urokinase (PLAU), involved in the regulation of epithelial sodium channel ENaC activity. Salt-sensitive and salt-resistant subjects have similar response to a saline/volume infusion as detected by urinary proteome. However, we identified glutamyl aminopeptidase (ENPEP), PLAU, EGF and Xaa-Pro aminopeptidase 2 precursor XPNPEP2 as key molecules of salt-sensitivity, through modulation of ENaC-dependent sodium reabsorption along the distal tubule.

Urinary Proteomic Profile of Arterial Stiffness as a Predictor of Mortality and Cardiovascular Outcomes

Background: The underlying mechanisms of arterial stiffness has not been fully understood. This study aimed to illuminate its pathophysiology by using urinary proteomics and to determine the prognostic value of the urinary proteomic markers for adverse outcomes. Methods: Aortic stiffness was measured with carotid-femoral pulse wave velocity (PWV) in 669 randomly recruited Flemish (mean age, 50.5 years; 51.1% women). Urinary proteome was analyzed using capillary electrophoresis coupled with mass spectrometry. PWV-derived urinary proteomic score (PWV-UP) was developed by modelling PWV with proteomics data at baseline through orthogonal projections to latent structures. Adverse outcomes included all-cause mortality and fatal and non-fatal cardiovascular (CV) events. Linear regression and Cox proportional hazard regression were used. Pathway analysis was performed with the Reactome Pathway Database. Findings: PWV-UP that consisted of 2,336 peptides explained 65% variance of PWV, which was higher than 36% explained by clinical risk factors. After adjusting for potential confounders, PWV-UP was significantly associated with PWV (β=0.73 [95% confidence interval: 0.67–0.79], P<0.0001). Over a period of 9.2 years (median), 36 participants died and 75 experienced fatal and non-fatal CV events. The incidence of mortality and CV events was higher at the upper half of PWV-UP (P≤0.0002). The adjusted hazard ratios (+1 standard derivation) were 1.46 (1.08–1.97) for all-cause mortality, 2.04 (1.07–3.87) for CV mortality and 1.39 (1.11–1.74) for CV events (P≤0.031). For PWV, the corresponding estimates were 1.25 (0.97–1.60), 1.35 (0.85–2.15), and 1.22 (1.02–1.47), respectively (P≥0.033). The proteins in PWV-UP involved in multiple pathways, such as collagen turnover, cell adhesion, inflammation, and lipid metabolism. Interpretation: PWV-UP was independently associated with arterial stiffness and explained the variance of PWV better than clinical determinants. PWV-UP, but not PWV, independently predicted all-cause mortality and CV mortality, implying that PWV-UP-associated proteins may be multifaceted and involved in diverse pathological processes beyond arterial stiffness. Funding Information: European Union (HEALTH-F7-305507 HOMAGE), the European Research Council (Advanced Researcher Grant 2011-294713-EPLORE and Proof-of-Concept Grant 713601-uPROPHET), the European Research Area Net for Cardiovascular Diseases (JTC2017-046-PROACT), and KU Leuven (STG-18-00379) currently support the Studies Coordinating Centre in Leuven. Declaration of Interests: The authors declare no conflicts of interest. Ethics Approval Statement: The study was approved by the University of Leuven Ethics Committee. All participants provided written informed consent.

On Research and Translation of Urinary Biomarkers.

From the theory of homeostasis, it can be deduced that urine is the source of sensitive disease markers reflecting early changes of the body. The study of urinary biomarkers using animal models is essential to prove this theory and encourage people to continue exploring the potential of urine. In clinical research, when disease-related changes are greater than individual variances, disease-related biomarkers with potential clinical application can be obtained by directly dividing samples into disease groups and control groups. To discover small early changes in disease, pre-and-post control of the same person can minimize most interfering factors. In this way, changes in urinary proteins before, during and after disease and/or treatment can be found, which can provide useful information for early detection and evaluation of the disease condition and treatment effect. In the study of clinical urinary biomarkers, regional and ethnic factors cannot be completely ignored. Diseases such as autism, which have only social behavior changes, may also be reflected in the urine proteome. Current research on urinary biomarkers is not sufficient to earn the recognition it deserves in the field of biomarkers. The recognition of urinary biomarkers will require the cooperation of more doctors and scientists and the participation of more foundations and companies.

A matter of perspective-Cutting-edge technology-driven urine proteome in COVID-19.

In a recent issue of Nature Communications, we highlighted in-depth urine proteomic research in which significant immunosuppression was revealed in early SARS-CoV-2- infected patients 1. The application of urine in mapping the landscape of molecular changes closely associated with human diseases has been widely accepted. Herein, we take a systematic review of the published article from the perspective of both methodology and clinical significance.

Changes in the Profile of Urine Proteins Associated with the Cardiovascular System in a Group of Healthy Young Men in Response to a Locomotor Test with a Stepwise Increasing Load

The prospects of interplanetary missions make it relevant to develop standard tests to find criteria for the correction of preventive measures in a long space flight in order to ensure a peak in physical performance by the time the interplanetary activity is completed. Currently, tests are being developed to assess physical performance. One of them can be the currently developed locomotor test with a stepwise increasing load in the active mode of movement of the treadmill. It is of interest to assess the effect of this load on changes in the profile of proteins associated with the cardiovascular system from the standpoint of the possibility of using them as markers of its response to physical activity. In article analyzes the results of an experimental study of the proteome of human urine after a dosed step-increasing load and discusses the possible role of the identified proteins that can be attributed to the functioning of the cardiovascular system. The study involved practically healthy volunteers aged 18.6 ± 0.7 years, weighing 75.7 ± 8 kg (n = 12). The urine proteome was evaluated by chromatography-mass spectrometry and analyzed by bioinformatics methods. For the first time, data on the proteomic response to a locomotor test with a stepwise increasing load in the active mode of web movement in a group homogeneous in autonomic status are presented. As a result of the analysis, 429 proteins were identified, 69 of which significantly changed. Based on bioinformatics analysis, processes related to the work of the heart, vascular tone, and vascular permeability were identified. Ten proteins are described that are associated with the processes of the quick response of the cardiovascular system to dosed physical activity. The obtained results will help in choosing standard criteria for assessing the physiological cost of physical activity.

Bioinformatic analysis of dysregulated proteins in prostate cancer patients reveals putative urinary biomarkers and key biological pathways.

Prostate cancer (PCa) is one of the most common cancer types among men. The quantification of prostate-specific antigen used for PCa detection has revealed limited applicability. Thus, it is crucial to identify new minimally invasive biomarkers for PCa. It is believed that the integration of proteomics data from different studies is vital for identifying new biomarkers for PCa, but studies carried out in this regard have few converging results. Using a different approach, this study aimed to unveil molecular features consistently dysregulated in PCa and potential urinary biomarkers for PCa. The novelty of this analysis relies on the comparison of urinary and tissue proteomes from PCa patients and consequent exclusion of kidney and bladder cancer interference. The conducted bioinformatic analysis revealed molecular processes dysregulated in urine from PCa patients that mirror the alterations in prostate tumor tissue. To identify putative urinary biomarkers, proteins previously detected in kidney and bladder tissues were eliminated from the final list of potential urinary biomarkers for PCa. After a detailed analysis, MSMB, KLK3, ITIH4, ITIH2, HPX, GP2, APOA2 and AZU1 proteins stood out as candidate urinary biomarkers for PCa.