Untargeted metabolomics of prostate cancer zwitterionic and positively charged compounds in urine

The emerging role of extracellular vesicles and particles in prostate cancer diagnosis, and risk stratification

Current approaches for prostate cancer (PCa) diagnosis and risk stratification require greater accuracy. Extracellular vesicles and particles (EVPs) containing diverse cargos from parent cells are released into the extracellular microenvironment and play a critical role in intercellular communication. Accumulating evidence demonstrates that EVPs are emerging as a promising focus for the exploration of cancer biomarkers and therapeutic targets. However, the precise categorisation and nomenclature of EVP subpopulations remains challenging due to their compositional complexity, inherent heterogeneity in molecular composition, and structure. The recent identification of two novel non-vesicular extracellular particle subtypes, exomeres and supermeres, has altered our understanding of the distinct subpopulations of EVPs and their roles in biological and physiological processes. Here, we discuss recent advances in the field of EVPs, describe characteristics of EVP subpopulations, focus on the application and potential of EVPs in PCa diagnosis and risk stratification by liquid biopsy, and highlight the major challenges and prospects of EVP research in PCa area.

Recent progress on media for biological sample preparation

The analysis of biological samples is highly valuable for disease diagnosis and treatment, forensic examination, and public safety. However, the serious matrix interference effect generated by biological samples severely affects the analysis of trace analytes. Sample preparation methods are introduced to address the limitation by extracting, separating, enriching, purifying trace target analytes from biological samples. With the raising demand of biological sample analysis, a review focuses on media for biological sample preparation and analysis over the last 5 years is presented. High-performance media in biological sample preparation are first reviewed, including porous organic frameworks, imprinted polymers, hydrogels, ionic liquids, and bioactive media. Then, application of media for different biological sample preparation and analysis is briefly introduced, including liquid samples of body fluids, solid samples (hair, feces, and tissues), and gas samples of exhale breath gas. Finally, conclusions and outlooks on media promoting biological sample preparation are presented.

Prostate cancer-derived extracellular vesicles metabolic biomarkers: Emerging roles for diagnosis and prognosis

Prostate cancer (PCa) is a global health concern, ranking as the most common cancer among men in Western countries. Traditional diagnostic methods are invasive with adverse effects on patients. Due to the heterogeneous nature of PCa and their multifocality, tissue biopsies often yield false-negative results. To address these challenges, researchers are exploring innovative approaches, particularly in the realms of proteomics and metabolomics, to identify more reliable biomarkers and improve PCa diagnosis. Liquid biopsy (LB) has emerged as a promising non-invasive strategy for PCa early detection, biopsy selection, active surveillance for low-risk cases, and post-treatment and progression monitoring. Extracellular vesicles (EVs) are lipid-bilayer nanovesicles released by all cell types and play an important role in intercellular communication. EVs have garnered attention as a valuable biomarker resource in LB for PCa-specific biomarkers, enhancing diagnosis, prognostication, and treatment guidance. Metabolomics provides insight into the body's metabolic response to both internal and external stimuli, offering quantitative measurements of biochemical alterations. It excels at detecting non-genetic influences, aiding in the discovery of more accurate cancer biomarkers for early detection and disease progression monitoring. This review delves into the potential of EVs as a resource for LB in PCa across various clinical applications. It also explores cancer-related metabolic biomarkers, both within and outside EVs in PCa, and summarises previous metabolomic findings in PCa diagnosis and risk assessment. Finally, the article addresses the challenges and future directions in the evolving field of EV-based metabolomic analysis, offering a comprehensive overview of its potential in advancing PCa management.

The Circular RNA Circ_0085494 Regulates Prostate Cancer Progression Through NRBP1/miR-497-5p Axis

Aberrant expression of circular RNA (circRNA) is closely linked to the progression of various human cancers, including prostate cancer (PCa). In this research, we aimed to investigate the biological role of and mechanisms of circ_0085494 in PCa. The quantitative real-time polymerase chain reaction (qRT-PCR) assay was used to detect circ_0085494, miR-497-5p, and nuclear receptor binding protein 1 (NRBP1) mRNA expression in PCa tissues and cell lines. Subsequently, colony formation, cell counting kit-8 (CCK-8), 5-ethynyl-2'-deoxyuridine (EdU), and transwell assays were performed to evaluate PCa cell proliferation, migration, and invasion. Western blot assay was applied for assessing the protein levels. Dual-luciferase reporter and RNA pull-down assays were implemented for verifying the association between miR-497-5p and circ_0085494 or NRBP1. The role of circ_0085494 in vivo was measured by establishing a mice xenograft model. Circ_0085494 was highly expressed in PCa tissues and cells, and its absence suppressed PCa cell proliferation, migration, and invasion. Circ_0085494 impacts NRBP1 content by adsorbing miR-497-5p. Meanwhile, the repression of circ_0085494 absence on tumor growth in vivo was validated. Our finding revealed that circ_0085494 downregulation might repress PCa tumor progression through in part regulating the miR-497-5p/NRBP1 pathway.

Beyond blood biomarkers: the role of SelectMDX in clinically significant prostate cancer identification

INTRODUCTION

New potential biomarkers to pre-intervention identification of a clinically significant prostate cancer (csPCa) will prevent overdiagnosis and overtreatment and limit quality of life impairment of PCa patients.

AREAS COVERED

We have developed a comprehensive review focusing our research on the increasing knowledge of the role of SelectMDX® in csPCa detection. Areas identified as clinically relevant are the ability of SelectMDX® to predict csPCa in active surveillance setting, its predictive ability when combined with multiparametric MRI and the role of SelectMDX® in the landscape of urinary biomarkers.

EXPERT OPINION

Several PCa biomarkers have been developed either alone or in combination with clinical variables to improve csPCa detection. SelectMDX® score includes genomic markers, age, PSA, prostate volume, and digital rectal examination. Several studies have shown consistency in the ability to improve detection of csPCa, avoidance of unnecessary prostate biopsies, helpful in decision-making for clinical benefit of PCa patients with future well designed, and impactful studies.

Circulating metabolite biomarkers: a game changer in the human prostate cancer diagnosis

PURPOSE

Prostate cancer (PCa) is the second most commonly diagnosed cancer in men in Western and Asian countries. Serum prostate-specific antigen (PSA) test has been the routine diagnostic method despite the tremendous research in diagnostic markers for early detection of PCa. A shift towards a promising and potential biomarker for PCa detection is through metabolomic profiling of biofluids, particularly the blood and urine samples. Finding reliable, routinely usable circulating metabolite biomarkers may not be a distant reality.

METHODS

We performed a PubMed-based literature search of metabolite biomarkers in blood and urine for the early detection of prostate cancer. The timeline of these searches was limited between 2007 and 2022 and the following keywords were used: 'metabolomics', 'liquid biopsy', 'circulating metabolites', 'serum metabolite', 'plasma metabolite', and 'urine metabolite' with respect to 'prostate cancer'. We focussed only on diagnosis-based studies with only the subject-relevant articles published in the English language and excluded all of the other irrelevant publications that included prostate tissue biomarkers and cell line biomarkers.

RESULTS

We have consolidated all the blood and urine-based potential metabolite candidates in individual as well as panels, including lipid classes, fatty acids, amino acids, and volatile organic compounds which may become useful for PCa diagnosis.

CONCLUSION

All these metabolome findings unveil the impact of different dimensions of PCa development, giving a promising strategy to diagnose the disease since suspected individuals can be subjected to repeated and largescale blood and urine testing.

Canine-Inspired Chemometric Analysis of Volatile Organic Compounds in Urine Headspace to Distinguish Prostate Cancer in Mice and Men

Canines can identify prostate cancer with high accuracy by smelling volatile organic compounds (VOCs) in urine. Previous studies have identified VOC biomarkers for prostate cancer utilizing solid phase microextraction (SPME) gas chromatography-mass spectrometry (GC-MS) but have not assessed the ability of VOCs to distinguish aggressive cancers. Additionally, previous investigations have utilized murine models to identify biomarkers but have not determined if the results are translatable to humans. To address these challenges, urine was collected from mice with prostate cancer and men undergoing prostate cancer biopsy and VOCs were analyzed by SPME GC-MS. Prior to analysis, SPME fibers/arrows were compared, and the fibers had enhanced sensitivity toward VOCs with a low molecular weight. The analysis of mouse urine demonstrated that VOCs could distinguish tumor-bearing mice with 100% accuracy. Linear discriminant analysis of six VOCs in human urine distinguished prostate cancer with sensitivity = 75% and specificity = 69%. Another panel of seven VOCs could classify aggressive cancer with sensitivity = 78% and specificity = 85%. These results show that VOCs have moderate accuracy in detecting prostate cancer and a superior ability to stratify aggressive tumors. Furthermore, the overlap in the structure of VOCs identified in humans and mice shows the merit of murine models for identifying biomarker candidates.

Artificial intelligence and radiomics in evaluation of kidney lesions: a comprehensive literature review

Radiomics and artificial intelligence (AI) may increase the differentiation of benign from malignant kidney lesions, differentiation of angiomyolipoma (AML) from renal cell carcinoma (RCC), differentiation of oncocytoma from RCC, differentiation of different subtypes of RCC, to predict Fuhrman grade, to predict gene mutation through molecular biomarkers and to predict treatment response in metastatic RCC undergoing immunotherapy. Neural networks analyze imaging data. Statistical, geometrical, textural features derived are giving quantitative data of contour, internal heterogeneity and gray zone features of lesions. A comprehensive literature review was performed, until July 2022. Studies investigating the diagnostic value of radiomics in differentiation of renal lesions, grade prediction, gene alterations, molecular biomarkers and ongoing clinical trials have been analyzed. The application of AI and radiomics could lead to improved sensitivity, specificity, accuracy in detecting and differentiating between renal lesions. Standardization of scanner protocols will improve preoperative differentiation between benign, low-risk cancers and clinically significant renal cancers and holds the premises to enhance the diagnostic ability of imaging tools to characterize renal lesions.

Development and validation of a predictive model for diagnosing prostate cancer after transperineal prostate biopsy

Objective

This study aimed to develop and validate a nomogram to predict the probability of prostate cancer (PCa) after transperineal prostate biopsy by combining patient clinical information and biomarkers.

Methods

First, we retrospectively collected the clinicopathologic data from 475 patients who underwent prostate biopsy at our hospital between January 2019 to August 2021. Univariate and multivariate logistic regression analyses were used to select risk factors. Then, we established the nomogram prediction model based on the risk factors. The model performance was assessed by receiver operating characteristic (ROC) curves, calibration plots and the Hosmer–Lemeshow test. Decision curve analysis (DCA) was used to evaluate the net benefit of the model at different threshold probabilities. The model was validated in an independent cohort of 197 patients between September 2021 and June 2022.

Results

The univariate and multivariate logistic regression analyses based on the development cohort indicated that the model should include the following factors: age (OR = 1.056, p = 0.001), NEUT (OR = 0.787, p = 0.008), HPR (OR = 0.139, p < 0.001), free/total (f/T) PSA (OR = 0.013, p = 0.015), and PI-RADS (OR = 3.356, p < 0.001). The calibration curve revealed great agreement. The internal nomogram validation showed that the C-index was 0.851 (95% CI 0.809-0.894). Additionally, the AUC was 0.851 (95% CI 0.809-0.894), and the Hosmer–Lemeshow test result presented p = 0.143 > 0.05. Finally, according to decision curve analysis, the model was clinically beneficial.

Conclusion

Herein, we provided a nomogram combining patients’ clinical data with biomarkers to help diagnose prostate cancers.

The involvement of high succinylation modification in the development of prostate cancer

OBJECTIVE

Succinylation modification of the lysine site plays an important role in tumorigenesis and development, but it is rarely reported in prostate cancer (PCa), so this study aims to elucidate its expression in and clinical correlation with PCa.

METHODS

A total of 95 tumor, 3 normal and 52 paired adjacent tissue of PCa were involved for succinylation stanning. 498 PCa samples with 20 succinylation modification-related genes from TCGA were downloaded for model construction. Statistical methods were employed to analyze the data, including Non-Negative Matrix Factorization (NMF) algorithm, t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm and Cox regression analysis.

RESULTS

The pan-succinyllysine antibody stanning indicated that tumor tissues showed higher succinyllysine level than adjacent tissues (p<0.001). Gleason grade and PDL1 expression levels were significantly different (p<0.001) among the high, medium and low succinylation staining scores. The types of PCa tissue were divided into four clusters using RNA-seq data of 20 succinylation-related genes in TCGA database. Clinical characterize of age, PSA level, and pathological stage showed differences among four clusters. The expression of succinylation-related genes (KAT5, SDHD and GLYATL1) and PCa related genes (PDL1, AR and TP53) were significantly different in 52 matched tumor and adjacent tissues (p<0.001). GLYATL1 and AR gene expression was significantly related to the pathological stage of PCa.

CONCLUSION

Succinylation was significantly increased in PCa tissues and was closely related to Gleason grade and PD-L1 expression. Model construction of 20 genes related to succinylation modification showed that the later the pathological stage of PCa, the higher the level of succinylation modification.

Metabolomic profile of prostate cancer-specific survival among 1812 Finnish men

BACKGROUND

Abnormal metabolism and perturbations in metabolic pathways play significant roles in the development and progression of prostate cancer; however, comprehensive metabolomic analyses of human data are lacking and needed to elucidate the interrelationships.

METHODS

We examined the serum metabolome in relation to prostate cancer survival in a cohort of 1812 cases in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study. Using an ultrahigh-performance LC-MS/MS platform, we identified 961 known metabolites in prospectively collected serum. Median survival time from diagnosis to prostate cancer-specific death (N=472) was 6.6 years (interquartile range=2.9-11.1 years). Cox proportional hazards regression models estimated hazard ratios and 95% confidence intervals of the associations between the serum metabolites (in quartiles) and prostate cancer death, adjusted for age at baseline and diagnosis, disease stage, and Gleason sum. In order to calculate risk scores, we first randomly divided the metabolomic data into a discovery set (70%) and validated in a replication set (30%).

RESULTS

Overall, 49 metabolites were associated with prostate cancer survival after Bonferroni correction. Notably, higher levels of the phospholipid choline, amino acid glutamate, long-chain polyunsaturated fatty acid (n6) arachidonate (20:4n6), and glutamyl amino acids gamma-glutamylglutamate, gamma-glutamylglycine, and gamma-glutamylleucine were associated with increased risk of prostate cancer-specific mortality (fourth versus first quartile HRs=2.07-2.14; P-values <5.2×10^-5). By contrast, the ascorbate/aldarate metabolite oxalate, xenobiotics S-carboxymethyl-L-cysteine, fibrinogen cleavage peptides ADpSGEGDFXAEGGGVR and fibrinopeptide B (1-12) were related to reduced disease-specific mortality (fourth versus first quartile HRs=0.82-0.84; P-value <5.2×10^-5). Further adjustment for years from blood collection to cancer diagnosis, body mass index, smoking intensity and duration, and serum total and high-density lipoprotein cholesterol did not alter the results. Participants with a higher metabolic score based on the discovery set had an elevated risk of prostate cancer-specific mortality in the replication set (fourth versus first quartile, HR=3.9, P-value for trend<0.0001).

CONCLUSIONS

The metabolic traits identified in this study, including for choline, glutamate, arachidonate, gamma-glutamyl amino acids, fibrinopeptides, and endocannabinoid and redox pathways and their composite risk score, corroborate our previous analysis of fatal prostate cancer and provide novel insights and potential leads regarding the molecular basis of prostate cancer progression and mortality.

Phenotypic and Metabolomic Characterization of 3D Lung Cell Cultures Exposed to Airborne Particulate Matter from Three Air Quality Network Stations in Catalonia

Air pollution constitutes an environmental problem that it is known to cause many serious adverse effects on the cardiovascular and respiratory systems. The chemical characterization of particulate matter (PM) is key for a better understanding of the associations between chemistry and toxicological effects. In this work, the chemical composition and biological effects of fifteen PM₁₀ air filter samples from three air quality stations in Catalonia with contrasting air quality backgrounds were investigated. Three-dimensional (3D) lung cancer cell cultures were exposed to these sample extracts, and cytotoxicity, reactive oxygen species (ROS) induction, metabolomics, and lipidomics were explored. The factor analysis method Multivariate Curve Resolution-Alternating Least-Squares (MCR-ALS) was employed for an integrated interpretation of the associations between chemical composition and biological effects, which could be related to urban traffic emission, biomass burning smoke, and secondary aerosols. In this pilot study, a novel strategy combining new approach methodologies and chemometrics provided new insights into the biomolecular changes in lung cells associated with different sources of air pollution. This approach can be applied in further research on air pollution toxicity to improve our understanding of the causality between chemistry and its effects.

Prostate cancer in omics era

Recent advances in omics technology have prompted extraordinary attempts to define the molecular changes underlying the onset and progression of a variety of complex human diseases, including cancer. Since the advent of sequencing technology, cancer biology has become increasingly reliant on the generation and integration of data generated at these levels. The availability of multi-omic data has transformed medicine and biology by enabling integrated systems-level approaches. Multivariate signatures are expected to play a role in cancer detection, screening, patient classification, assessment of treatment response, and biomarker identification. This review reports current findings and highlights a number of studies that are both novel and groundbreaking in their application of multi Omics to prostate cancer.

Fibromodulin Gene Variants (FMOD) as Potential Biomarkers for Prostate Cancer and Benign Prostatic Hyperplasia

By the year 2050, the world's elderly population may increase exponentially, raising the rate of disease characteristic of this group, such as prostate cancer (PCa) and benign prostatic hyperplasia (BPH). Prostate disorders have a multifactorial etiology, especially age and genetic factors. Currently, PCa is the second most frequent neoplasm in the male population worldwide. The fibromodulin gene encodes a small leucine-rich proteoglycan (SLRP) which acts in the collagen fibrillogenesis pathway, cell adhesion, and modulation of TGF-β signaling pathways, which has been recently associated with PCa. The present study sequenced the coding region of the FMOD in a sample of 44 PCa, 90 BPH, and 82 controls from a Brazilian population, and the results identified 6 variants: 2 missenses (p.(Tyr42Ser) and p.(Pro24Ala)); 3 synonymous (p.(His253=), p.(Asn353=), and p.(Glu79=)); and 1 intronic (c.980-114A>G). Of these, p.(Tyr42Ser), p.(Pro24Ala), and p.(Asn353=) are rare variants, and p.(Tyr42Ser) was predicted as potential pathogenic by the algorithms used here, in addition to not being observed in controls, suggesting that may be a potential biomarker for development of PCa and BPH. In conclusion, we identified for the first time, in Brazilian individuals with PCa and BPH, a potentially pathogenic variant in the analysis of FMOD gene. Further studies are needed to investigate the deleterious effect of this variant on the structure and/or function of the FMOD protein.

The Integration of Metabolomics with Other Omics: Insights into Understanding Prostate Cancer

Our understanding of prostate cancer (PCa) has shifted from solely caused by a few genetic aberrations to a combination of complex biochemical dysregulations with the prostate metabolome at its core. The role of metabolomics in analyzing the pathophysiology of PCa is indispensable. However, to fully elucidate real-time complex dysregulation in prostate cells, an integrated approach based on metabolomics and other omics is warranted. Individually, genomics, transcriptomics, and proteomics are robust, but they are not enough to achieve a holistic view of PCa tumorigenesis. This review is the first of its kind to focus solely on the integration of metabolomics with multi-omic platforms in PCa research, including a detailed emphasis on the metabolomic profile of PCa. The authors intend to provide researchers in the field with a comprehensive knowledge base in PCa metabolomics and offer perspectives on overcoming limitations of the tool to guide future point-of-care applications.

Citric Acid as a Potential Prostate Cancer Biomarker Determined in Various Biological Samples

Despite numerous studies, the molecular mechanism of prostate cancer development is still unknown. Recent investigations indicated that citric acid and lipids—with a special emphasis on fatty acids, steroids and hormones (ex. prolactin)—play a significant role in prostate cancer development and progression. However, citric acid is assumed to be a potential biomarker of prostate cancer, due to which, the diagnosis at an early stage of the disease could be possible. For this reason, the main goal of this study is to determine the citric acid concentration in three different matrices. To the best of our knowledge, this is the first time for citric acid to be determined in three different matrices (tissue, urine and blood). Samples were collected from patients diagnosed with prostate cancer and from a selected control group (individuals with benign prostatic hyperplasia). The analyses were performed using the rapid fluorometric test. The obtained results were correlated with both the histopathological data (the Gleason scale as well as the Classification of Malignant Tumors (pTNM) staging scale) and the biochemical data (the values of the following factors: prostate specific antigen, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglyceride, total cholesterol, creatinine and prolactin) using chemometric methods. For tissue samples, the results indicated a decreased level of citric acid in the case of prostate cancer. The analyte average concentrations in serum and urine appeared to be corresponding and superior in the positive cohort. This trend was statistically significant in the case of urinary citric acid. Moreover, a significant negative correlation was demonstrated between the concentration of citric acid and the tumor stage. A negative correlation between the total cholesterol and high-density lipoprotein and prolactin was particularly prominent in cancer cases. Conversely, a negative association between low-density lipoprotein and prolactin levels was observed solely in the control group. On the basis of the results, one may assume the influence of hormones, particularly prolactin, on the development of prostate cancer. The present research allowed us to verify the possibility of using citric acid as a potential biomarker for prostate cancer.

Comprehensive biomarker profiles and chemometric filtering of urinary metabolomics for effective discrimination of prostate carcinoma from benign hyperplasia

Prostate cancer (PCa) is the most commonly diagnosed cancer in male individuals, principally affecting men over 50 years old, and is the leading cause of cancer-related deaths. Actually, the measurement of prostate-specific antigen level in blood is affected by limited sensitivity and specificity and cannot discriminate PCa from benign prostatic hyperplasia patients (BPH). In the present paper, 20 urine samples from BPH patients and 20 from PCa patients were investigated to develop a metabolomics strategy useful to distinguish malignancy from benign hyperplasia. A UHPLC-HRMS untargeted approach was carried out to generate two large sets of candidate biomarkers. After mass spectrometric analysis, an innovative chemometric data treatment was employed involving PLS-DA classification with repeated double cross-validation and permutation test to provide a rigorously validated PLS-DA model. Simultaneously, this chemometric approach filtered out the most effective biomarkers and optimized their relative weights to yield the highest classification efficiency. An unprecedented portfolio of prostate carcinoma biomarkers was tentatively identified including 22 and 47 alleged candidates from positive and negative ion electrospray (ESI+ and ESI-) datasets. The PLS-DA model based on the 22 ESI+ biomarkers provided a sensitivity of 95 ± 1% and a specificity of 83 ± 3%, while that from the 47 ESI- biomarkers yielded an 88 ± 3% sensitivity and a 91 ± 2% specificity. Many alleged biomarkers were annotated, belonging to the classes of carnitine and glutamine metabolites, C21 steroids, amino acids, acetylcholine, carboxyethyl-hydroxychroman, and dihydro(iso)ferulic acid.

Untargeted Metabolomics Study of Three Matrices: Seminal Fluid, Urine, and Serum to Search the Potential Indicators of Prostate Cancer

The simultaneous determination of metabolites from biological fluids may provide more accurate information about the current body condition. So far, the metabolomics approach has been successfully applied to study the mechanism of several disorders and to search for novel biomarkers. Urine and plasma are widely accepted matrices for the evaluation of several pathologies, while prostate cancer (CaP) development is still unknown. For this reason, an alternative matrix, the seminal fluid, was proposed to expand the knowledge about the CaP pathomechanism. The main aim of this study was to develop and optimize the sample preparation protocol to ensure the highest coverage of the metabolome of ejaculate samples. Parameters like the type and composition of the solvent mixture, time of extraction, and applied volume of the solvent were tested. The optimized method was applied for the untargeted metabolomics profiling of seminal fluid samples obtained from CaP patients. Moreover, urine and serum samples were also prepared for untargeted metabolomics analysis. Analyses were carried out with the use of two complementary analytical techniques: GC-EI-QqQ/MS and LC-ESI-TOF/MS. Finally, the metabolic signature of seminal fluid (n = 7), urine (n = 7), and plasma (n = 7) samples was compared. Furthermore, the hypothesis of the increased level of metabolites in ejaculate samples related to the CaP development was evaluated. The results indicated that the developed and optimized sample preparation protocol for seminal fluid may be successfully applied for metabolomics study. Untargeted analysis of ejaculate enabled to determine the following classes of compounds: fatty acids, sphingolipids, phospholipids, sugars, and their derivatives, as well as amino acids. Finally, a comparison of the three tested matrices was carried out. To our best knowledge, it is the first time when the metabolic profile of the three matrices, namely, urine, plasma, and seminal fluid, was compared. Based on the results, it can be pointed out that ejaculate comprises the metabolic signature of both matrices (polar compounds characteristic for urine, and non-polar ones present in plasma samples). Compared to plasma, semen samples revealed to have a similar profile; however, determined levels of metabolites were lower in case of ejaculate. In case of urine samples, compared to semen metabolic profiles, the levels of detected metabolites were decreased in the latter ones.

Untargeted urine metabolite profiling by mass spectrometry aided by multivariate statistical analysis to predict prostate cancer treatment outcome

One of the key barriers to the prostate cancer is monitor treatment response. Here we described a conceptually new ‘MS-statistical analysis-metabolome’ strategy for discovery of metabolic features.

Plasma Metabolomics for Discovery of Early Metabolic Markers of Prostate Cancer Based on Ultra-High-Performance Liquid Chromatography-High Resolution Mass Spectrometry

BACKGROUND

The prevention and early screening of PCa is highly dependent on the identification of new biomarkers. In this study, we investigated whether plasma metabolic profiles from healthy males provide novel early biomarkers associated with future risk of PCa.

METHODS

Using the Supplémentation en Vitamines et Minéraux Antioxydants (SU.VI.MAX) cohort, we identified plasma samples collected from 146 PCa cases up to 13 years prior to diagnosis and 272 matched controls. Plasma metabolic profiles were characterized using ultra-high-performance liquid chromatography-high resolution mass spectrometry (UHPLC-HRMS).

RESULTS

Orthogonal partial least squares discriminant analysis (OPLS-DA) discriminated PCa cases from controls, with a median area under the receiver operating characteristic curve (AU-ROC) of 0.92 using a 1000-time repeated random sub-sampling validation. Sparse Partial Least Squares Discriminant Analysis (sPLS-DA) identified the top 10 most important metabolites (p < 0.001) discriminating PCa cases from controls. Among them, phosphate, ethyl oleate, eicosadienoic acid were higher in individuals that developed PCa than in the controls during the follow-up. In contrast, 2-hydroxyadenine, sphinganine, L-glutamic acid, serotonin, 7-keto cholesterol, tiglyl carnitine, and sphingosine were lower.

CONCLUSION

Our results support the dysregulation of amino acids and sphingolipid metabolism during the development of PCa. After validation in an independent cohort, these signatures may promote the development of new prevention and screening strategies to identify males at future risk of PCa.

SelectMDx and Multiparametric Magnetic Resonance Imaging of the Prostate for Men Undergoing Primary Prostate Biopsy: A Prospective Assessment in a Multi-Institutional Study

Prostate-specific antigen (PSA) testing as the sole indication for prostate biopsy lacks specificity, resulting in overdiagnosis of indolent prostate cancer (PCa) and missing clinically significant PCa (csPCa). SelectMDx is a biomarker-based risk score to assess urinary HOXC6 and DLX1 mRNA expression combined with traditional clinical risk factors. The aim of this prospective multi-institutional study was to evaluate the diagnostic accuracy of SelectMDx and its association with multiparametric magnetic resonance (mpMRI) when predicting PCa in prostate biopsies. Overall, 310 consecutive subjects were included. All patients underwent mpMRI and SelectMDx prior to prostate biopsy. SelectMDx and mpMRI showed sensitivity and specificity of 86.5% vs. 51.9%, and 73.8% vs. 88.3%, respectively, in predicting PCa at biopsy, and 87.1% vs. 61.3%, and 63.7% vs. 83.9%, respectively, in predicting csPCa at biopsy. SelectMDx was revealed to be a good predictor of PCa, while with regards to csPCa detection, it was demonstrated to be less effective, showing results similar to mpMRI. With analysis of strategies assessed to define the best diagnostic strategy to avoid unnecessary biopsy, SelectMDx appeared to be a reliable pathway after an initial negative mpMRI. Thus, biopsy could be proposed for all cases of mpMRI PI-RADS 4-5 score, and to those with Prostate Imaging-Reporting and Data System (PI-RADS) 1-3 score followed by a positive SelectMDx.

Biomarkers in Prostate Cancer Diagnosis: From Current Knowledge to the Role of Metabolomics and Exosomes

Early detection of prostate cancer (PC) is largely carried out using assessment of prostate-specific antigen (PSA) level; yet it cannot reliably discriminate between benign pathologies and clinically significant forms of PC. To overcome the current limitations of PSA, new urinary and serum biomarkers have been developed in recent years. Although several biomarkers have been explored in various scenarios and patient settings, to date, specific guidelines with a high level of evidence on the use of these markers are lacking. Recent advances in metabolomic, genomics, and proteomics have made new potential biomarkers available. A number of studies focused on the characterization of the specific PC metabolic phenotype using different experimental approaches has been recently reported; yet, to date, research on metabolomic application for PC has focused on a small group of metabolites that have been known to be related to the prostate gland. Exosomes are extracellular vesicles that are secreted from all mammalian cells and virtually detected in all bio-fluids, thus allowing their use as tumor biomarkers. Thanks to a general improvement of the technical equipment to analyze exosomes, we are able to obtain reliable quantitative and qualitative information useful for clinical application. Although some pilot clinical investigations have proposed potential PC biomarkers, data are still preliminary and non-conclusive.