Metabolomics Biomarkers of Prostate Cancer: A Systematic Review

The prowess of metabolomics in cancer research: current trends, challenges and future perspectives

Cancer due to its heterogeneous nature and large prevalence has tremendous socioeconomic impacts on populations across the world. Therefore, it is crucial to discover effective panels of biomarkers for diagnosing cancer at an early stage. Cancer leads to alterations in cell growth and differentiation at the molecular level, some of which are very unique. Therefore, comprehending these alterations can aid in a better understanding of the disease pathology and identification of the biomolecules that can serve as effective biomarkers for cancer diagnosis. Metabolites, among other biomolecules of interest, play a key role in the pathophysiology of cancer whose levels are significantly altered while 'reprogramming the energy metabolism', a cellular condition favored in cancer cells which is one of the hallmarks of cancer. Metabolomics, an emerging omics technology has tremendous potential to contribute towards the goal of investigating cancer metabolites or the metabolic alterations during the development of cancer. Diverse metabolites can be screened in a variety of biofluids, and tumor tissues sampled from cancer patients against healthy controls to capture the altered metabolism. In this review, we provide an overview of different metabolomics approaches employed in cancer research and the potential of metabolites as biomarkers for cancer diagnosis. In addition, we discuss the challenges associated with metabolomics-driven cancer research and gaze upon the prospects of this emerging field.

Olaris Global Panel (OGP): A Highly Accurate and Reproducible Triple Quadrupole Mass Spectrometry-Based Metabolomics Method for Clinical Biomarker Discovery

Mass spectrometry (MS)-based clinical metabolomics is very promising for the discovery of new biomarkers and diagnostics. However, poor data accuracy and reproducibility limit its true potential, especially when performing data analysis across multiple sample sets. While high-resolution mass spectrometry has gained considerable popularity for discovery metabolomics, triple quadrupole (QqQ) instruments offer several benefits for the measurement of known metabolites in clinical samples. These benefits include high sensitivity and a wide dynamic range. Here, we present the Olaris Global Panel (OGP), a HILIC LC-QqQ MS method for the comprehensive analysis of ~250 metabolites from all major metabolic pathways in clinical samples. For the development of this method, multiple HILIC columns and mobile phase conditions were compared, the robustness of the leading LC method assessed, and MS acquisition settings optimized for optimal data quality. Next, the effect of U-13C metabolite yeast extract spike-ins was assessed based on data accuracy and precision. The use of these U-13C-metabolites as internal standards improved the goodness of fit to a linear calibration curve from r2 < 0.75 for raw data to >0.90 for most metabolites across the entire clinical concentration range of urine samples. Median within-batch CVs for all metabolite ratios to internal standards were consistently lower than 7% and less than 10% across batches that were acquired over a six-month period. Finally, the robustness of the OGP method, and its ability to identify biomarkers, was confirmed using a large sample set.

Metabolomics and Proteomics in Prostate Cancer Research: Overview, Analytical Techniques, Data Analysis, and Recent Clinical Applications

Prostate cancer (PCa) is a significant global contributor to mortality, predominantly affecting males aged 65 and above. The field of omics has recently gained traction due to its capacity to provide profound insights into the biochemical mechanisms underlying conditions like prostate cancer. This involves the identification and quantification of low-molecular-weight metabolites and proteins acting as crucial biochemical signals for early detection, therapy assessment, and target identification. A spectrum of analytical methods is employed to discern and measure these molecules, revealing their altered biological pathways within diseased contexts. Metabolomics and proteomics generate refined data subjected to detailed statistical analysis through sophisticated software, yielding substantive insights. This review aims to underscore the major contributions of multi-omics to PCa research, covering its core principles, its role in tumor biology characterization, biomarker discovery, prognostic studies, various analytical technologies such as mass spectrometry and Nuclear Magnetic Resonance, data processing, and recent clinical applications made possible by an integrative "omics" approach. This approach seeks to address the challenges associated with current PCa treatments. Hence, our research endeavors to demonstrate the valuable applications of these potent tools in investigations, offering significant potential for understanding the complex biochemical environment of prostate cancer and advancing tailored therapeutic approaches for further development.

HCMMD: systematic evaluation of metabolites in body fluids as liquid biopsy biomarker for human cancers

Metabolomics is a rapidly expanding field in systems biology used to measure alterations of metabolites and identify metabolic biomarkers in response to disease processes. The discovery of metabolic biomarkers can improve early diagnosis, prognostic prediction, and therapeutic intervention for cancers. However, there are currently no databases that provide a comprehensive evaluation of the relationship between metabolites and cancer processes. In this review, we summarize reported metabolites in body fluids across pan-cancers and characterize their clinical applications in liquid biopsy. We conducted a search for metabolic biomarkers using the keywords ("metabolomics" OR "metabolite") AND "cancer" in PubMed. Of the 22,254 articles retrieved, 792 were deemed potentially relevant for further review. Ultimately, we included data from 573,300 samples and 17,083 metabolic biomarkers. We collected information on cancer types, sample size, the human metabolome database (HMDB) ID, metabolic pathway, area under the curve (AUC), sensitivity and specificity of metabolites, sample source, detection method, and clinical features were collected. Finally, we developed a user-friendly online database, the Human Cancer Metabolic Markers Database (HCMMD), which allows users to query, browse, and download metabolite information. In conclusion, HCMMD provides an important resource to assist researchers in reviewing metabolic biomarkers for diagnosis and progression of cancers.

Comprehensive plasma steroidomics reveals subtle alterations of systemic steroid profile in patients at different stages of prostate cancer disease

The steroid submetabolome, or steroidome, is of particular interest in prostate cancer (PCa) as the dependence of PCa growth on androgens is well known and has been routinely exploited in treatment for decades. Nevertheless, the community is still far from a comprehensive understanding of steroid involvement in PCa both at the tissue and at systemic level. In this study we used liquid chromatography/high resolution mass spectrometry (LC/HRMS) backed by a dynamic retention time database DynaSTI to obtain a readout on circulating steroids in a cohort reflecting a progression of the PCa. Hence, 60 relevant compounds were annotated in the resulting LC/HRMS data, including 22 unknown steroid isomers therein. Principal component analysis revealed only subtle alterations of the systemic steroidome in the study groups. Next, a supervised approach allowed for a differentiation between the healthy state and any of the stages of the disease. Subsequent clustering of steroid metabolites revealed two groups responsible for this outcome: one consisted primarily of the androgens, whereas another contained corticosterone and its metabolites. The androgen data supported the currently established involvement of a hypothalamic-pituitary-gonadal axis in the development of PCa, whereas biological role of corticosterone remained elusive. On top of that, current results suggested a need for improvement in the dynamic range of the analytical methods to better understand the role of low abundant steroids, as the analysis revealed an involvement of estrogen metabolites. In particular, 2-hydroxyestradiol-3-methylether, one of the compounds present in the disease phenotype, was annotated and reported for the first time in men.

Salivary metabolomics in oral potentially malignant disorders and oral cancer patients-a systematic review with meta-analysis

OBJECTIVES

The aim of this systematic review and meta-analysis is to assess the diagnostic potential of salivary metabolomics in the detection of oral potentially malignant disorders (OPMDs) and oral cancer (OC).

MATERIALS AND METHODS

A systematic review was performed in accordance with the 3rd edition of the Centre for Reviews and Dissemination (CRD) and Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) statement. Electronic searches for articles were carried out in the PubMed, Web of Science, and Scopus databases. The quality assessment of the included studies was evaluated using the Newcastle-Ottawa Quality Assessment Scale (NOS) and the new version of the QUADOMICS tool. Meta-analysis was conducted whenever possible. The effect size was presented using the Forest plot, whereas the presence of publication bias was examined through Begg's funnel plot.

RESULTS

A total of nine studies were included in the systematic review. The metabolite profiling was heterogeneous across all the studies. The expression of several salivary metabolites was found to be significantly altered in OPMDs and OCs as compared to healthy controls. Meta-analysis was able to be conducted only for N-acetylglucosamine. There was no significant difference (SMD = 0.15; 95% CI - 0.25-0.56) in the level of N-acetylglucosamine between OPMDs, OC, and the control group.

CONCLUSION

Evidence for N-acetylglucosamine as a salivary biomarker for oral cancer is lacking. Although several salivary metabolites show changes between healthy, OPMDs, and OC, their diagnostic potential cannot be assessed in this review due to a lack of data. Therefore, further high-quality studies with detailed analysis and reporting are required to establish the diagnostic potential of the salivary metabolites in OPMDs and OC.

CLINICAL RELEVANCE

While some salivary metabolites exhibit significant changes in oral potentially malignant disorders (OPMDs) and oral cancer (OC) compared to healthy controls, the current evidence, especially for N-acetylglucosamine, is inadequate to confirm their reliability as diagnostic biomarkers. Additional high-quality studies are needed for a more conclusive assessment of salivary metabolites in oral disease diagnosis.

3D plasmonic hexaplex paper sensor for label-free human saliva sensing and machine learning-assisted early-stage lung cancer screening

A label-free detection method for noninvasive biofluids enables rapid on-site disease screening and early-stage cancer diagnosis by analyzing metabolic alterations. Herein, we develop three-dimensional plasmonic hexaplex nanostructures coated on a paper substrate (3D-PHP). This flexible and highly absorptive 3D-PHP sensor is integrated with commercial saliva collection tube to create an efficient on-site sensing platform for lung cancer screening via surface-enhanced Raman scattering (SERS) measurement of human saliva. The multispike hexaplex-shaped gold nanostructure enhances contact with saliva viscosity, enabling effective sampling and SERS enhancement. Through testing patient salivary samples, the 3D-PHP sensor demonstrates successful lung cancer detection and diagnosis. A logistic regression-based machine learning model successfully classifies benign and malignant patients, exhibiting high clinical sensitivity and specificity. Additionally, important Raman peak positions related to different lung cancer stages are investigated, suggesting insights for early-stage cancer diagnosis. Integrating 3D-PHP senor with the conventional saliva collection tube platform is expected to offer promising practicality for rapid on-site disease screening and diagnosis, and significant advancements in cancer detection and patient care.

Urinary volatile organic compounds in prostate cancer biopsy pathologic risk stratification using logistic regression and multivariate analysis models

Prostate cancer (PCa) is the second leading cause of cancer-related death in American men after lung cancer. The current PCa diagnostic method, the serum prostate-specific antigen (PSA) test, is not specific, thus, alternatives are needed to avoid unnecessary biopsies and over-diagnosis of clinically insignificant PCa. To explore the application of metabolomics in such effort, urine samples were collected from 386 male adults aged 44-93 years, including 247 patients with biopsy-proven PCa and 139 with biopsy-proven negative results. The PCa-positive group was further subdivided into two groups: low-grade (ISUP Grade Group = 1; n = 139) and intermediate/high-grade (ISUP Grade Group ≥ 2; n = 108). Volatile organic compounds (VOCs) in urine were extracted by stir bar sorptive extraction (SBSE) and analyzed using thermal desorption with gas chromatography and mass spectrometry (GC-MS). We used machine learning tools to develop and evaluate models for PCa diagnosis and prognosis. In total, 22,538 VOCs were identified in the urine samples. With regularized logistic regression, our model for PCa diagnosis yielded an area under the curve (AUC) of 0.99 and 0.88 for the training and testing sets respectively. Furthermore, the model for differentiating between low-grade and intermediate/high-grade PCa yielded an average AUC of 0.78 based on a repeated test-sample approach for cross-validation. These novel methods using urinary VOCs and logistic regression were developed to fill gaps in PCa screening and assessment of PCa grades prior to biopsy. Our study findings provide a promising alternative or adjunct to current PCa screening and diagnostic methods to better target patients for biopsy and mitigate the challenges associated with over-diagnosis and over-treatment of PCa.

Plasma metabolomics profiling of 580 patients from an Early Detection Research Network prostate cancer cohort

Prostate cancer is the second most common cancer in men and affects 1 in 9 men in the United States. Early screening for prostate cancer often involves monitoring levels of prostate-specific antigen (PSA) and performing digital rectal exams. However, a prostate biopsy is always required for definitive cancer diagnosis. The Early Detection Research Network (EDRN) is a consortium within the National Cancer Institute aimed at improving screening approaches and early detection of cancers. As part of this effort, the Weill Cornell EDRN Prostate Cancer has collected and biobanked specimens from men undergoing a prostate biopsy between 2008 and 2017. In this report, we describe blood metabolomics measurements for a subset of this population. The dataset includes detailed clinical and prospective records for 580 patients who underwent prostate biopsy, 287 of which were subsequentially diagnosed with prostate cancer, combined with profiling of 1,482 metabolites from plasma samples collected at the time of biopsy. We expect this dataset to provide a valuable resource for scientists investigating prostate cancer metabolism.

Unveiling Disrupted Lipid Metabolism in Benign Prostate Hyperplasia, Prostate Cancer, and Metastatic Patients: Insights from a Colombian Nested Case-Control Study

Prostate cancer is a significant global health concern, and its prevalence is increasing worldwide. Despite extensive research efforts, the complexity of the disease remains challenging with respect to fully understanding it. Metabolomics has emerged as a powerful approach to understanding prostate cancer by assessing comprehensive metabolite profiles in biological samples. In this study, metabolic profiles of patients with benign prostatic hyperplasia (BPH), prostate cancer (PCa), and metastatic prostate cancer (Met) were characterized using an untargeted approach that included metabolomics and lipidomics via liquid chromatography and gas chromatography coupled with high-resolution mass spectrometry. Comparative analysis among these groups revealed distinct metabolic profiles, primarily associated with lipid biosynthetic pathways, such as biosynthesis of unsaturated fatty acids, fatty acid degradation and elongation, and sphingolipid and linoleic acid metabolism. PCa patients showed lower levels of amino acids, glycerolipids, glycerophospholipids, sphingolipids, and carnitines compared to BPH patients. Compared to Met patients, PCa patients had reduced metabolites in the glycerolipid, glycerophospholipid, and sphingolipid groups, along with increased amino acids and carbohydrates. These altered metabolic profiles provide insights into the underlying pathways of prostate cancer's progression, potentially aiding the development of new diagnostic, and therapeutic strategies.

The Olfaction Ability of Medical Detection Canine to Detect Prostate Cancer From Urine Samples: Progress Captured in Systematic Review and Meta-Analysis

Background

To date, early cancer detection is considered vital to reduce the global cancer burden through low-cost, but accurate screening modalities. The anatomical positioning of prostate cancer (PCa) created a potentially distinctive diagnostic method through the identification of volatile organic compounds (VOCs) in urine, which might be detectable not by humans but by canine species. This review aimed to capture the potential of the medical detection canine (MDC) to detect PCa by providing its diagnostic accuracy estimation on urine odor testing.

Methods

Databases, e.g., MEDLINE, Cochrane, ScienceDirect, and ProQuest, were searched to identify the studies. We focused on accessible original research, comparing the diagnostic utility of trained female MDC and histopathology examination as the gold standard for PCa diagnosis. The statistical analysis was performed in Meta-DiSc 1.4 and presented in diagnostic values, i.e., sensitivity (Sn), specificity (Sp), positive or negative likelihood ratio (LR+ or LR-), diagnostic odd ratio (DOR), and area under the curve (AUC) value, to conclude the Sn-Sp in a single outcome.

Results

Female German Shepherds were the most commonly utilized MDC from the five studies included in the final analysis. We estimate the pooled diagnostic value of eight different MDCs, with the findings as follows: Sn (0.95 (0.94 - 0.97)), Sp (0.92 (0.90 - 0.93)), LR+ (4.48 (1.90 - 10.58)), LR- (0.12 (0.01 - 1.42)), DOR (35.39 (2.90 - 432.53)), and an AUC value of 0.9232.

Conclusions

MDC's olfaction ability holds considerable potential on its diagnostic accuracies to distinguish the urine of PCa individuals by identifying its volatilome property.

Effectiveness of Treatments That Alter Metabolomics in Cancer Patients-A Systematic Review

INTRODUCTION

Cancer is the leading cause of death worldwide, with the most frequent being breast cancer in women, prostate cancer in men and colon cancer in both sexes. The use of metabolomics to find new biomarkers can provide knowledge about possible interventions based on the presence of oncometabolites in different cancer types.

OBJECTIVES

The primary purpose of this review is to analyze the characteristic metabolome of three of the most frequent cancer types. We further want to identify the existence and success rate of metabolomics-based intervention in patients suffering from those cancer types. Our conclusions are based on the analysis of the methodological quality of the studies.

METHODS

We searched for studies that investigated the metabolomic characteristics in patients suffering from breast cancer, prostate cancer or colon cancer in clinical trials. The data were analyzed, as well as the effects of specific interventions based on identified metabolomics and one or more oncometabolites. The used databases were PubMed, Virtual Health Library, Web of Science, EBSCO and Cochrane Library. Only nine studies met the selection criteria. Study bias was analyzed using the Cochrane risk of bias tool. This systematic review protocol was registered at the International Prospective Register of Systematic Reviews (PROSPERO: CRD42023401474).

RESULTS

Only nine studies about clinical trials were included in this review and show a moderate quality of evidence. Metabolomics-based interventions related with disease outcome were conflictive with no or small changes in the metabolic characteristics of the different cancer types.

CONCLUSIONS

This systematic review shows some interesting results related with metabolomics-based interventions and their effects on changes in certain cancer oncometabolites. The small number of studies we identified which fulfilled our inclusion criteria in this systematic review does not allow us to draw definitive conclusions. Nevertheless, some results can be considered as promising although further research is needed. That research must focus not only on the presence of possible oncometabolites but also on possible metabolomics-based interventions and their influence on the outcome in patients suffering from breast cancer, prostate cancer or colon cancer.

Potential Diagnostic Biomarker Detection for Prostate Cancer Using Untargeted and Targeted Metabolomic Profiling

Prostate cancer (PCa) remains one of the leading causes of cancer mortality in men worldwide, currently lacking specific, early detection and staging biomarkers. In this regard, modern research focuses efforts on the discovery of novel molecules that could represent potential future non-invasive biomarkers for the diagnosis of PCa, as well as therapeutic targets. Mounting evidence shows that cancer cells express an altered metabolism in their early stages, making metabolomics a promising tool for the discovery of altered pathways and potential biomarker molecules. In this study, we first performed untargeted metabolomic profiling on 48 PCa plasma samples and 23 healthy controls using ultra-high-performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-[ESI+]-MS) for the discovery of metabolites with altered profiles. Secondly, we selected five molecules (L-proline, L-tryptophan, acetylcarnitine, lysophosphatidylcholine C18:2 and spermine) for the downstream targeted metabolomics and found out that all the molecules, regardless of the PCa stage, were decreased in the PCa plasma samples when compared to the controls, making them potential biomarkers for PCa detection. Moreover, spermine, acetylcarnitine and L-tryptophan had very high diagnostic accuracy, with AUC values of 0.992, 0.923 and 0.981, respectively. Consistent with other literature findings, these altered metabolites could represent future specific and non-invasive candidate biomarkers for PCa detection, which opens novel horizons in the field of metabolomics.

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.

Metabolomic and transcriptomic response to imatinib treatment of gastrointestinal stromal tumour in xenograft-bearing mice

BACKGROUND

Although imatinib is a well-established first-line drug for treating a vast majority of gastrointestinal stromal tumours (GIST), GISTs acquire secondary resistance during therapy. Multi-omics approaches provide an integrated perspective to empower the development of personalised therapies through a better understanding of functional biology underlying the disease and molecular-driven selection of the best-targeted individualised therapy. In this study, we applied integrative metabolomic and transcriptomic analyses to elucidate tumour biochemical processes affected by imatinib treatment.

MATERIALS AND METHODS

A GIST xenograft mouse model was used in the study, including 10 mice treated with imatinib and 10 non-treated controls. Metabolites in tumour extracts were analysed using gas chromatography coupled with mass spectrometry (GC-MS). RNA sequencing was also performed on the samples subset (n=6).

RESULTS

Metabolomic analysis revealed 21 differentiating metabolites, whereas next-generation RNA sequencing data analysis resulted in 531 differentially expressed genes. Imatinib significantly changed the profile of metabolites associated mainly with purine and pyrimidine metabolism, butanoate metabolism, as well as alanine, aspartate, and glutamate metabolism. The related changes in transcriptomic profiles included genes involved in kinase activity and immune responses, as well as supported its impact on the purine biosynthesis pathway.

CONCLUSIONS

Our multi-omics study confirmed previously known pathways involved in imatinib anticancer activity as well as correlated imatinib-relevant downregulation of expression of purine biosynthesis pathway genes with the reduction of respectful metabolites. Furthermore, considering the importance of the purine biosynthesis pathway for cancer proliferation, we identified a potentially novel mechanism for the anti-tumour activity of imatinib. Based on the results, we hypothesise metabolic modulations aiming at the reduction in purine and pyrimidine pool may ensure higher imatinib efficacy or re-sensitise imatinib-resistant tumours.

Serum organic acid metabolites can be used as potential biomarkers to identify prostatitis, benign prostatic hyperplasia, and prostate cancer

Background

Noninvasive methods for the early identify diagnosis of prostatitis, benign prostatic hyperplasia (BPH), and prostate cancer (PCa) are current clinical challenges.

Methods

The serum metabolites of 20 healthy individuals and patients with prostatitis, BPH, or PCa were identified using untargeted liquid chromatography-mass spectrometry (LC-MS). In addition, targeted LC-MS was used to verify the organic acid metabolites in the serum of a validation cohort.

Results

Organic acid metabolites had good sensitivity and specificity in differentiating prostatitis, BPH, and PCa. Three diagnostic models identified patients with PROSTATITIS: phenyllactic acid (area under the curve [AUC]=0.773), pyroglutamic acid (AUC=0.725), and pantothenic acid (AUC=0.721). Three diagnostic models identified BPH: citric acid (AUC=0.859), malic acid (AUC=0.820), and D-glucuronic acid (AUC=0.810). Four diagnostic models identified PCa: 3-hydroxy-3-methylglutaric acid (AUC=0.804), citric acid (AUC=0.918), malic acid (AUC=0.862), and phenyllactic acid (AUC=0.713). Two diagnostic models distinguished BPH from PCa: phenyllactic acid (AUC=0.769) and pyroglutamic acid (AUC=0.761). Three diagnostic models distinguished benign BPH from PROSTATITIS: citric acid (AUC=0.842), ethylmalonic acid (AUC=0.814), and hippuric acid (AUC=0.733). Six diagnostic models distinguished BPH from prostatitis: citric acid (AUC=0.926), pyroglutamic acid (AUC=0.864), phenyllactic acid (AUC=0.850), ethylmalonic acid (AUC=0.843), 3-hydroxy-3-methylglutaric acid (AUC=0.817), and hippuric acid (AUC=0.791). Three diagnostic models distinguished PCa patients with PROSTATITISA < 4.0 ng/mL from those with PSA > 4.0 ng/mL: 5-hydromethyl-2-furoic acid (AUC=0.749), ethylmalonic acid (AUC=0.750), and pyroglutamic acid (AUC=0.929). Conclusions: These results suggest that serum organic acid metabolites can be used as biomarkers to differentiate prostatitis, BPH, and PCa.

Metabolic Reprogramming of Castration-Resistant Prostate Cancer Cells as a Response to Chemotherapy

Prostate cancer at the castration-resistant stage (CRPC) is a leading cause of death among men due to resistance to anticancer treatments, including chemotherapy. We set up an in vitro model of therapy-induced cancer repopulation and acquired cell resistance (CRAC) on etoposide-treated CRPC PC3 cells, witnessing therapy-induced epithelial-to-mesenchymal-transition (EMT) and chemoresistance among repopulating cells. Here, we explore the metabolic changes leading to chemo-induced CRAC, measuring the exchange rates cell/culture medium of 36 metabolites via Nuclear Magnetic Resonance spectroscopy. We studied the evolution of PC3 metabolism throughout recovery from etoposide, encompassing the degenerative, quiescent, and repopulating phases. We found that glycolysis is immediately shut off by etoposide, gradually recovering together with induction of EMT and repopulation. Instead, OXPHOS, already high in untreated PC3, is boosted by etoposide to decline afterward, though stably maintaining values higher than control. Notably, high levels of EMT, crucial in the acquisition of chemoresistance, coincide with a strong acceleration of metabolism, especially in the exchange of principal nutrients and their end products. These results provide novel information on the energy metabolism of cancer cells repopulating from cytotoxic drug treatment, paving the way for uncovering metabolic vulnerabilities to be possibly pharmacologically targeted and providing novel clinical options for CRPC.

Molecular and metabolic alterations of 2,3-dihydroquinazolin-4(1H)-one derivatives in prostate cancer cell lines

Prostate cancer (PC) is the second most common tumor in males worldwide. The lack of effective medication and the development of multidrug resistance towards current chemotherapeutic agents urge the need to discover novel compounds and therapeutic targets for PC. Herein, seven synthesized 2,3-dihydroquinazolin-4(1H)-one analogues were evaluated for their anticancer activity against PC3 and DU145 cancer cell lines using MTT, scratch-wound healing, adhesion and invasion assays. Besides, a liquid chromatography mass spectrometry (LC-MS)-based metabolomics approach was followed to identify the biochemical pathways altered in DU145 cancer cells upon exposure to dihydroquinazolin derivatives. The seven compounds showed sufficient cytotoxicity and significantly suppressed DU145 and PC3 migration after 48 and 72 h. C2 and C5 had the most potent effect with IC₅₀ < 15 µM and significantly inhibited PC cell adhesion and invasion. Metabolomics revealed that C5 disturbed the level of metabolites involved in essential processes for cancer cell proliferation, progression and growth including energy production, redox homeostasis, amino acids and polyamine metabolisms and choline phospholipid metabolism. The data presented herein highlighted the importance of these compounds as potential anticancer agents particularly C5, and pointed to the promising role of metabolomics as a new analytical approach to investigate the antiproliferative activity of synthesized compounds and identify new therapeutic targets.

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.

Application of gold nanoparticles to determine spermine in the presence of other polyamines

The development of methods for the detection of polyamines in biological fluids is essential to improve early diagnosis and treatment of patients with prostate cancer. One of the promising areas is the use of noble metal nanoparticles. According to the literature data, there is no methodological approach have been developed to reliably distinguish spermine from other polyamines, in particular, from their acetylated forms and related compounds present in biological fluids. The paper presents the results of spectrophotometric determination of spermine both alone and in the presence of putrescine, spermidine or urea in the urine using gold nanoparticles. The results of the experiments proved that the developed method is suitable for the selective determination of spermine. It was shown that the presence of spermidine, putrescine, acetylated forms of polyamines or carbamide does not affect the results of the analysis.

Identification of characteristic metabolic panels for different stages of prostate cancer by 1H NMR-based metabolomics analysis

Background

Prostate cancer (PCa) is the second most prevalent cancer in males worldwide, yet detecting PCa and its metastases remains a major challenging task in clinical research setups. The present study aimed to characterize the metabolic changes underlying the PCa progression and investigate the efficacy of related metabolic panels for an accurate PCa assessment.

Methods

In the present study, 75 PCa subjects, 62 PCa patients with bone metastasis (PCaB), and 50 benign prostatic hyperplasia (BPH) patients were enrolled, and we performed a cross-sectional metabolomics analysis of serum samples collected from these subjects using a ¹H nuclear magnetic resonance (NMR)-based metabolomics approach.

Results

Multivariate analysis revealed that BPH, PCa, and PCaB groups showed distinct metabolic divisions, while univariate statistics integrated with variable importance in the projection (VIP) scores identified a differential metabolite series, which included energy, amino acid, and ketone body metabolism. Herein, we identified a series of characteristic serum metabolic changes, including decreased trends of 3-HB and acetone as well as elevated trends of alanine in PCa patients compared with BPH subjects, while increased levels of 3-HB and acetone as well as decreased levels of alanine in PCaB patients compared with PCa. Additionally, our results also revealed the metabolic panels of discriminant metabolites coupled with the clinical parameters (age and body mass index) for discrimination between PCa and BPH, PCaB and BPH, PCaB and PCa achieved the AUC values of 0.828, 0.917, and 0.872, respectively.

Conclusions

Overall, our study gave successful discrimination of BPH, PCa and PCaB, and we characterized the potential metabolic alterations involved in the PCa progression and its metastases, including 3-HB, acetone and alanine. The defined biomarker panels could be employed to aid in the diagnosis and classification of PCa in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03478-5.

Metabolomic Profile in Patients with Malignant Disturbances of the Prostate: An Experimental Approach

Purpose To identify metabolites in humans that can be associated with the presence of malignant disturbances of the prostate.

Methods In the present study, we selected male patients aged between 46 and 82 years who were considered at risk of prostate cancer due to elevated levels of prostate-specific antigen (PSA) or abnormal results on the digital rectal examination. All selected patients came from two university hospitals (Hospital Universitario del Valle and Clínica Rafael Uribe Uribe) and were divided into 2 groups: cancer (12 patients) and non-cancer (20 patients). Cancer was confirmed by histology, and none of the patients underwent any previous treatment. Standard protocols were applied to all the collected blood samples. The resulting plasma samples were kept at -80°C, and a profile of each one was acquired by nuclear magnetic resonance (NMR) using established experiments. Multivariate analyses were applied to this dataset, first to establish the quality of the data and identify outliers, and then, to model the data.

Results We included 12 patients with cancer and 20 without it. Two patients were excluded due to contamination with ethanol. The remaining ones were used to build an Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA) model (including 15 non-cancer and 10 cancer patients), with acceptable discrimination (Q2 = 0.33). This model highlighted the role of lactate and lipids, with a positive association of these two metabolites and prostate cancer.

Conclusions The primary discriminative metabolites between patients with and without prostate cancer were lactate and lipids. These might be the most reliable biomarkers to trace the development of cancer in the prostate.

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.

Microbiomes of Urine and the Prostate Are Linked to Human Prostate Cancer Risk Groups

BACKGROUND

Bacteria play a suspected role in the development of several cancer types, and associations between the presence of particular bacteria and prostate cancer have been reported.

OBJECTIVE

To provide improved characterisation of the prostate and urine microbiome and to investigate the prognostic potential of the bacteria present.

DESIGN, SETTING, AND PARTICIPANTS

Microbiome profiles were interrogated in sample collections of patient urine (sediment microscopy: n = 318, 16S ribosomal amplicon sequencing: n = 46; and extracellular vesicle RNA-seq: n = 40) and cancer tissue (n = 204).

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Microbiomes were assessed using anaerobic culture, population-level 16S analysis, RNA-seq, and whole genome DNA sequencing.

RESULTS AND LIMITATIONS

We demonstrate an association between the presence of bacteria in urine sediments and higher D'Amico risk prostate cancer (discovery, n = 215 patients, p < 0.001; validation, n = 103, p < 0.001, χ² test for trend). Characterisation of the bacterial community led to the (1) identification of four novel bacteria (Porphyromonas sp. nov., Varibaculum sp. nov., Peptoniphilus sp. nov., and Fenollaria sp. nov.) that were frequently found in patient urine, and (2) definition of a patient subgroup associated with metastasis development (p = 0.015, log-rank test). The presence of five specific anaerobic genera, which includes three of the novel isolates, was associated with cancer risk group, in urine sediment (p = 0.045, log-rank test), urine extracellular vesicles (p = 0.039), and cancer tissue (p = 0.035), with a meta-analysis hazard ratio for disease progression of 2.60 (95% confidence interval: 1.39-4.85; p = 0.003; Cox regression). A limitation is that functional links to cancer development are not yet established.

CONCLUSIONS

This study characterises prostate and urine microbiomes, and indicates that specific anaerobic bacteria genera have prognostic potential.

PATIENT SUMMARY

In this study, we investigated the presence of bacteria in patient urine and the prostate. We identified four novel bacteria and suggest a potential prognostic utility for the microbiome in 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.

Metabolic Phenotyping in Prostate Cancer Using Multi-Omics Approaches

Prostate cancer (PCa), one of the most frequently diagnosed cancers among men worldwide, is characterized by a diverse biological heterogeneity. It is well known that PCa cells rewire their cellular metabolism to meet the higher demands required for survival, proliferation, and invasion. In this context, a deeper understanding of metabolic reprogramming, an emerging hallmark of cancer, could provide novel opportunities for cancer diagnosis, prognosis, and treatment. In this setting, multi-omics data integration approaches, including genomics, epigenomics, transcriptomics, proteomics, lipidomics, and metabolomics, could offer unprecedented opportunities for uncovering the molecular changes underlying metabolic rewiring in complex diseases, such as PCa. Recent studies, focused on the integrated analysis of multi-omics data derived from PCa patients, have in fact revealed new insights into specific metabolic reprogramming events and vulnerabilities that have the potential to better guide therapy and improve outcomes for patients. This review aims to provide an up-to-date summary of multi-omics studies focused on the characterization of the metabolomic phenotype of PCa, as well as an in-depth analysis of the correlation between changes identified in the multi-omics studies and the metabolic profile of PCa tumors.

Potential of nuclear magnetic resonance metabolomics in the study of prostate cancer

Nuclear magnetic resonance (NMR) metabolomics is a powerful analytical technique and a tool which has unique characteristics and capabilities for the evaluation of a number of biochemicals/metabolites of cancer and other disease processes that are present in biofluids (urine and blood) and tissues. The potential of NMR metabolomics in prostate cancer (PCa) has been explored by researchers and its usefulness has been documented. A large number of metabolites such as citrate, choline, and sarcosine were detected by NMR metabolomics from biofluids and tissues related to PCa and their levels were compared with controls and benign prostatic hyperplasia. The changes in the levels of these metabolites aid in the diagnosis and help to understand the dysregulated metabolic pathways in PCa. We review recent studies on in vitro and ex vivo NMR spectroscopy-based PCa metabolomics and its possible role as a diagnostic tool.

Surface-enhanced Raman scattering (SERS) spectroscopy for prostate cancer diagnosis: A review

The present review focuses on the diagnosis of prostate cancer using surface enhanced Raman scattering (SERS) spectroscopy. On the basis of literature search, SERS-based analysis for prostate cancer detection of different sample types is reported in the present study. Prostate cancer is responsible for nearly one-tenth of all cell cancer deaths among men. Significant efforts have been dedicated to establish precise and sensitive monitoring techniques to detect prostate cancer biomarkers in different types of body samples. Among the various spectro-analytical techniques investigated to achieve this objective, SERS spectroscopy has been proven as a promising approach that provides noticeable enhancements of the Raman sensitivity when the target biomolecules interact with a nanostructured surface. The purpose of this review is to give a brief overview of the SERS-basedapproach and other spectro-analytical strategies being used for the detection and quantification of prostate cancer biomarkers. The revolutionary development of SERS methods for the diagnosis of prostate cancer has been discussed in more details based on the reported literature. It has been noticed that the SERS-based immunoassay presents reliable results for the prostate cancer quantification. The EC-SERS, which integrates electrochemistry with the SERS model, could also offer a potential ultrasensitive strategy, although its application in prostate cancer analysis has been still limited.

Metabolomics Profiling Discriminates Prostate Cancer From Benign Prostatic Hyperplasia Within the Prostate-Specific Antigen Gray Zone

Objective

Prostate cancer (PCa) is the second most common male malignancy globally. Prostate-specific antigen (PSA) is an important biomarker for PCa diagnosis. However, it is not accurate in the diagnostic gray zone of 4–10 ng/ml of PSA. In the current study, the performance of serum metabolomics profiling in discriminating PCa patients from benign prostatic hyperplasia (BPH) individuals with a PSA concentration in the range of 4–10 ng/ml was explored.

Methods

A total of 220 individuals, including patients diagnosed with PCa and BPH within PSA levels in the range of 4–10 ng/ml and healthy controls, were enrolled in the study. Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS)-based non-targeted metabolomics method was utilized to characterize serum metabolic profiles of participants. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) methods were used for multivariate analysis. Receiver operating characteristic (ROC) curve analysis was performed to explore the diagnostic value of candidate metabolites in differentiating PCa from BPH. Correlation analysis was conducted to explore the relationship between serum metabolites and common clinically used fasting lipid profiles.

Results

Several differential metabolites were identified. The top enriched pathways in PCa subjects such as glycerophospholipid and glycerolipid metabolisms were associated with lipid metabolism. Lipids and lipid-like compounds were the predominant metabolites within the top 50 differential metabolites selected using fold-change threshold >1.5 or <2/3, variable importance in projection (VIP) > 1, and Student’s t-test threshold p < 0.05. Eighteen lipid or lipid-related metabolites were selected including 4-oxoretinol, anandamide, palmitic acid, glycerol 1-hexadecanoate, dl-dihydrosphingosine, 2-methoxy-6Z-hexadecenoic acid, 3-oxo-nonadecanoic acid, 2-hydroxy-nonadecanoic acid, N-palmitoyl glycine, 2-palmitoylglycerol, hexadecenal, d-erythro-sphingosine C-15, N-methyl arachidonoyl amine, 9-octadecenal, hexadecyl acetyl glycerol, 1-(9Z-pentadecenoyl)-2-eicosanoyl-glycero-3-phosphate, 3Z,6Z,9Z-octadecatriene, and glycidyl stearate. Selected metabolites effectively discriminated PCa from BPH when PSA levels were in the range of 4–10 ng/ml (area under the curve (AUC) > 0.80). Notably, the 18 identified metabolites were negatively corrected with total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and Apo-B levels in PCa patients; and some were negatively correlated with high-density lipoprotein cholesterol (HDL-C) and Apo-A levels. However, the metabolites were not correlated with triglycerides (TG).

Conclusion

The findings of the present study indicate that metabolic reprogramming, mainly lipid metabolism, is a key signature of PCa. The 18 lipid or lipid-associated metabolites identified in this study are potential diagnostic markers for differential diagnosis of PCa patients and BPH individuals within a PSA level in the gray zone of 4–10 ng/ml.

Metabolomics of prostate cancer: Knock-in versus knock-out prostate

Several metabolomics-derived biomarkers of prostate cancer (PC) have been reported with pre-radical prostatectomy (RP) (knock-in PC) conditions; however, uncontested PC biomarkers panel appraisal and investigation of correlative evidence of these measures is lacking through post-RP (knock-out PC). We sought to explore patients' filtered serum-based metabolomics derived signature measures in knock-in PC (n = 90) using nuclear magnetic resonance spectroscopy and multiple rigorous statistical analyses, and to develop the correlative evidence of these measures through knock-out PC (n = 90) follow-up on the 15th and 30th days. The glutamate, citrate and glycine were observed as hallmarks of PC. Observed trends revealed; augmented glutamate level in knock-in PC following a sudden drop and subsequently upside of glutamate at 15th and 30th days of knock-out PC, reduction of citrate in knock-in PC subsequently gradual increase of citrate in knock-out PC, and glycine lessening in knock-in PC following augmentation on 30th day of knock-out PC. This study-based evidence clears the doubts regarding the discovery of metabolomics-derived PC biomarkers.

Identification of potential serum metabolic biomarkers for patient with keratoconus using untargeted metabolomics approach

This study aimed to investigate the metabolite differences between patients with keratoconus and control subjects and identify potential serum biomarkers for keratoconus using a non-targeted metabolomics approach. Venous blood samples were obtained from patients with keratoconus (n = 20) as well as from age-, gender- and race-matched control subjects (n = 20). Metabolites extracted from serum were separated and analyzed by liquid chromatography/quadrupole time-of-flight mass spectrometer. Processing of raw data and analysis of the data files was performed using Agilent Mass Hunter Qualitative software. The identified metabolites were subjected to a principal component and hierarchical cluster analysis. Appropriate statistical tests were used to analyze the metabolomic profiling data. Together, the analysis revealed that the dehydroepiandrosterone sulfate from the steroidal hormone synthesis pathway was significantly upregulated in patients with keratoconus (p < 0.05). Also, a combination of eicosanoids from the arachidonic acid pathway, mainly prostaglandin F2α, prostaglandin A2, 16,16-dimethyl prostaglandin E2, and 5-hydroxyeicosatetraenoic acid were collectively up-regulated as a group in keratoconus patients (p < 0.05). On the other hand, glycerophospholipid PS(17:2(9Z,12Z)/20:4(5Z,8Z,11Z,14Z)) was found to be significantly upregulated in the metabolomics profiles of control subjects (p < 0.05). The differently regulated metabolites provide insights into the pathophysiology of keratoconus and could potentially be used as biomarkers for keratoconus to aid in screening for individuals at risk hence, enabling early diagnosis and timely monitoring of disease.

Novel Prostate Cancer Biomarkers: Aetiology, Clinical Performance and Sensing Applications

The review initially provides a short introduction to prostate cancer (PCa) incidence, mortality, and diagnostics. Next, the need for novel biomarkers for PCa diagnostics is briefly discussed. The core of the review provides details about PCa aetiology, alternative biomarkers available for PCa diagnostics besides prostate specific antigen and their biosensing. In particular, low molecular mass biomolecules (ions and metabolites) and high molecular mass biomolecules (proteins, RNA, DNA, glycoproteins, enzymes) are discussed, along with clinical performance parameters.

Metabolomics in cancer research and emerging applications in clinical oncology

Cancer has myriad effects on metabolism that include both rewiring of intracellular metabolism to enable cancer cells to proliferate inappropriately and adapt to the tumor microenvironment, and changes in normal tissue metabolism. With the recognition that fluorodeoxyglucose-positron emission tomography imaging is an important tool for the management of many cancers, other metabolites in biological samples have been in the spotlight for cancer diagnosis, monitoring, and therapy. Metabolomics is the global analysis of small molecule metabolites that like other -omics technologies can provide critical information about the cancer state that are otherwise not apparent. Here, the authors review how cancer and cancer therapies interact with metabolism at the cellular and systemic levels. An overview of metabolomics is provided with a focus on currently available technologies and how they have been applied in the clinical and translational research setting. The authors also discuss how metabolomics could be further leveraged in the future to improve the management of patients with cancer.

Review of novel liquid-based biomarkers for prostate cancer: towards personalised and targeted medicine

Background:

Prostate cancer is the most commonly diagnosed cancer in men and it is responsible for about 10% of all cancer mortalities in both American and Canadian men. At present, serum prostate-specific antigen levels remain the most commonly used test to detect prostate cancer, and the standard and definitive diagnosis of the disease is via prostate biopsy. Conventional tissue biopsies are usually invasive, expensive, painful, time-consuming, and unsuitable for screening and need to be consistently evaluated by expert pathologists and have limited repeatability. Consequently, liquid biopsies are emerging as a favourable alternative to conventional tissue biopsies, providing a non-invasive and cost-effective approach for screening, diagnosis, treatment and monitoring of prostate cancer patients.

Materials and methods:

We searched several databases from August to December 2020 for relevant studies published in English between 2000 and 2020 and reporting on liquid-based biomarkers available in detectable quantities in patient bodily fluid samples. In this narrative review paper, we describe seven novel and promising liquid-based biomarkers that potentially account for individual patient variability as well as used in disease risk assessment, screening for early disease detection and diagnosis, identification of patients’ risk for metastatic disease and subsequent relapse, monitoring patient response to specific treatment and providing clinicians the potential to stratify patients likely to benefit from a particular treatment.

Conclusions:

The concept of precision medicine from prevention to treatment techniques that take individual patient variability into account will depend on the development of effective clinical biomarkers that interrogate key aberrant pathways potentially targetable with molecular targets or immunologic therapies. Liquid-based biomarkers with high sensitivity and specificity for prostate cancer are emerging as minimally invasive, lower risk, readily obtainable and easily repeatable technique for screening for early disease detection and diagnosis, patient stratification at diagnosis into different risk categories, identification of patients’ risk for metastatic disease and subsequent relapse, and real-time monitoring of patient response to specific treatment. Thus, effective liquid-based biomarkers will potentially shift the treatment paradigm of prostate cancer towards more personalised and targeted medicine.

Compound Sophorae Decoction: treating ulcerative colitis by affecting multiple metabolic pathways

Ulcerative colitis (UC) is a chronic refractory non-specific intestinal inflammatory disease that is difficult to be cured. The discovery of new ulcerative colitis-related metabolite biomarkers may help further understand UC and facilitate early diagnosis. It may also provide a basis for explaining the mechanism of drug action in the treatment of UC. Compound Sophorae Decoction (CSD) is an empirical formula used in the clinical treatment of UC. Although it is known to be efficacious, its mechanism of action in the treatment of UC is unclear. The purpose of this study was to investigate the changes in endogenous substances in UC rats and the effects of CSD on metabolic pathways using the metabonomics approach. Metabolomics studies in rats with UC and normal rats were performed using LC-MS/MS. Rats with UC induced using TNBS enema were used as the study models. Metabolic profiling and pathway analysis of biomarkers was performed using statistical and pathway enrichment analyses. 36 screened potential biomarkers were found to be significantly different between the UC and the normal groups; it was also found that CSD could modulate the levels of these potential biomarkers. CSD was found to be efficacious in UC by regulating multiple metabolic pathways.

A Caenorhabditis elegans behavioral assay distinguishes early stage prostate cancer patient urine from controls

Current methods for non-invasive prostate cancer (PrCa) detection have a high false-positive rate and often result in unnecessary biopsies. Previous work has suggested that urinary volatile organic compound (VOC) biomarkers may be able to distinguish PrCa cases from benign disease. The behavior of the nematode Caenorhabditis elegans has been proposed as a tool to take advantage of these potential VOC profiles. To test the ability of C. elegans Bristol N2 to distinguish PrCa cases from controls, we performed chemotaxis assays using human urine samples collected from men screened for PrCa. Behavioral response of nematodes towards diluted urine from PrCa cases was compared to response to samples from cancer-free controls. Overall, we observed a significant attraction of young adult-stage C. elegans nematodes to 1:100 diluted urine from confirmed PrCa cases and repulsion of C. elegans to urine from controls. When C. elegans chemotaxis index was considered alongside prostate-specific antigen levels for distinguishing cancer from cancer-free controls, the accuracy of patient classification was 81%. We also observed behavioral attraction of C. elegans to two previously reported VOCs to be increased in PrCa patient urine. We conclude nematode behavior distinguishes PrCa case urine from controls in a dilution-dependent manner.

Summary: The nematode Caenorhabditis elegans shows behavioral attraction to urine from prostate cancer patients, but not to controls, and this phenomenon may be a useful tool for designing diagnostic assays or biomarker discovery.

Label-Free SERS Analysis of Urine Using a 3D-Stacked AgNW-Glass Fiber Filter Sensor for the Diagnosis of Pancreatic Cancer and Prostate Cancer

Metabolomics shows tremendous potential for the early diagnosis and screening of cancer. For clinical application as an effective diagnostic tool, however, improved analytical methods for complex biological fluids are required. Here, we developed a reliable rapid urine analysis system based on surface-enhanced Raman spectroscopy (SERS) using 3D-stacked silver nanowires (AgNWs) on a glass fiber filter (GFF) sensor and applied it to the diagnosis of pancreatic cancer and prostate cancer. Urine samples were pretreated with centrifugation to remove large debris and with calcium ion addition to improve the binding of metabolites to AgNWs. The label-free urine-SERS detection using the AgNW-GFF SERS sensor showed different spectral patterns and distinguishable specific peaks in three groups: normal control (n = 30), pancreatic cancer (n = 22), and prostate cancer (n = 22). Multivariate analyses of SERS spectra using unsupervised principal component analysis and supervised orthogonal partial least-squares discriminant analysis showed excellent discrimination between the pancreatic cancer group and the prostate cancer group as well as between the normal control group and the combined cancer groups. The results demonstrate the great potential of the urine-SERS analysis system using the AgNW-GFF SERS sensor for the noninvasive diagnosis and screening of cancers.

Pseudouridine as a novel biomarker in prostate cancer

Epitranscriptomic analysis has recently led to the profiling of modified nucleosides in cancer cell biological matrices, helping to elucidate their functional roles in cancer and reigniting interest in exploring their use as potential markers of cancer development and progression. Pseudouridine, one of the most well-known and the most abundant of the RNA nucleotide modifications, is the C5-glycoside isomer of uridine and its distinctive physiochemical properties allows it to perform many essential functions. Pseudouridine functionally (a) confers rigidity to local RNA structure by enhancing RNA stacking, engaging in a cooperative effect on neighboring nucleosides that overall contributes to RNA stabilization (b) refines the structure of tRNAs, which influences their decoding activity (c) facilitates the accuracy of decoding and proofreading during translation and efficiency of peptide bond formation, thus collectively improving the fidelity of protein biosynthesis and (e) dynamically regulates mRNA coding and translation. Biochemical synthesis of pseudouridine is carried out by pseudouridine synthases. In this review we discuss the evidence supporting an association between elevated pseudouridine levels with the incidence and progression of human prostate cancer and the translational significance of the value of this modified nucleotide as a novel biomarker in prostate cancer progression to advanced disease.

Functional Metabolomics and Chemoproteomics Approaches Reveal Novel Metabolic Targets for Anticancer Therapy

Cancer cells exhibit different metabolic patterns compared to their normal counterparts. Although the reprogrammed metabolism has been indicated as strong biomarkers of cancer initiation and progression, increasing evidences suggest that metabolic alteration tuned by oncogenic drivers contributes to the occurrence and development of cancers rather than just being a hallmark of cancer. With this notion, targeting cancer metabolism holds promise as a novel anticancer strategy and is embracing its renaissance during the past two decades. Herein we have summarized the most recent developments in omics technology, including both metabolomics and proteomics, and how the combined use of these analytical tools significantly impacts this field by comprehensively and systematically recording the metabolic changes in cancer and hence reveals potential therapeutic targets that function by modulating the disrupted metabolic pathways.

Emerging computational tools and models for studying gut microbiota composition and function

The gut microbiota and its metabolites play critical roles in human health and disease. Advances in high-throughput sequencing, mass spectrometry, and other omics assay platforms have improved our ability to generate large volumes of data exploring the temporal variations in the compositions and functions of microbial communities. To elucidate mechanisms, methods and tools are needed that can rigorously model the dependencies within time-series data. Longitudinal data are often sparse and unevenly sampled, and nontrivial challenges remain in determining statistical significance, normalization across different data types, and model validation. In this review, we highlight recent developments in models and software tools for the analysis of time series microbiome and metabolome data, as well as integration of these data.

Identification of Plasma Glycosphingolipids as Potential Biomarkers for Prostate Cancer (PCa) Status

Prostate cancer (PCa) is the most common male cancer and the second leading cause of cancer death in United States men. Controversy continues over the effectiveness of prostate-specific antigen (PSA) for distinguishing aggressive from indolent PCa. There is a critical need for more specific and sensitive biomarkers to detect and distinguish low- versus high-risk PCa cases. Discovery metabolomics were performed utilizing ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS) on plasma samples from 159 men with treatment naïve prostate cancer participating in the North Carolina-Louisiana PCa Project to determine if there were metabolites associated with aggressive PCa. Thirty-five identifiable plasma small molecules were associated with PCa aggressiveness, 15 of which were sphingolipids; nine common molecules were present in both African-American and European-American men. The molecules most associated with PCa aggressiveness were glycosphingolipids; levels of trihexosylceramide and tetrahexosylceramide were most closely associated with high-aggressive PCa. The Cancer Genome Atlas was queried to determine gene alterations within glycosphingolipid metabolism that are associated with PCa and other cancers. Genes that encode enzymes associated with the metabolism of glycosphingolipids were altered in 12% of PCa and >30% of lung, uterine, and ovarian cancers. These data suggest that the identified plasma (glyco)sphingolipids should be further validated for their association with aggressive PCa, suggesting that specific sphingolipids may be included in a diagnostic signature for PCa.

Distinct lipid signatures are identified in the plasma of rats with chronic inflammation induced by estradiol benzoate and sex hormones

INTRODUCTION

Prostatitis is likely to occur in younger or middle-aged men, while prostate cancer is likely to occur in older men. Although amino acids and lipids as biomarkers of prostate cancer have been examined using prostate cancer cell lines/tissues, no previous studies have evaluated amino acids or lipids as potential chronic prostatitis biomarkers.

OBJECTIVES

The study's aim was to identify amino acids and lipids that could serve as potential biomarkers of chronic prostatitis.

METHODS

We profiled the amino acids and lipids found in plasma from rats collected in a previous study. In brief, a total of 148 Sprague-Dawley rats (offspring) were dosed with estradiol benzoate (EB) on postnatal days (PNDs) 1, 3 and 5, and subsequently dosed with testosterone (T)/estradiol (E) tubes via subcutaneous implants from PND 90 to 200. Plasma was collected on PNDs 30, 90, 100, 145 and 200. Analysis was conducted with a Xevo TQ-S triple-quadrupole mass spectrometer using a Biocrates AbsoluteIDQ p180 kit.

RESULTS

Plasma acylcarnitines [(C2, C16:1, C18, C18:1, C18:1-OH, and C18:2)], glycerophospholipids (lysophosphatidylcholine-acyl, -di-acyl, and -di-acyl acyl-alkyl) and sphingomyelins [SM (OH) C16:1, SM C18:0, SM C18:1, and SM C20:2] significantly increased on PND 145, when chronic inflammation was observed in the dorsolateral prostate of rats dosed with EB, T, and E. No statistical significances of amino acid levels were observed in the EB + T + E group on PND 145.

CONCLUSION

Exposure to EB, T, and E altered lipid levels in rat plasma with chronic prostate inflammation. These findings suggest that the identified lipids may be predictive chronic prostatitis biomarkers. The results require confirmation through additional nonclinical and human studies.

A Panel of Urinary Volatile Biomarkers for Differential Diagnosis of Prostate Cancer from Other Urological Cancers

Our group recently developed a urinary 6-biomarker panel for the diagnosis of prostate cancer (PCa) which has a higher level of accuracy compared to the serum prostate specific antigen (PSA) test. Herein, urine from an independent cohort of PCa patients and cancer-free controls was analyzed to further validate the discriminative power of that panel. Additionally, urine from patients diagnosed with bladder cancer (BC) and renal cancer (RC) were included to evaluate the site-specificity of the panel. Results confirmed the ability of the 6-biomarker panel to discriminate PCa patients from controls, but not from other urological cancers. To overcome this limitation, an untargeted approach was performed to unveil discriminant metabolites among the three cancer types. A 10-biomarker panel comprising the original panel plus four new metabolites was established to discriminate PCa from controls, BC, and RC, with 76% sensitivity, 90% specificity, and 92% accuracy. This improved panel also disclosed better accuracy than serum PSA test and provides the basis for a new non-invasive early detection tool for PCa.

Comprehensive metabolomics analysis of prostate cancer tissue in relation to tumor aggressiveness and TMPRSS2-ERG fusion status

Background

Prostate cancer (PC) can display very heterogeneous phenotypes ranging from indolent asymptomatic to aggressive lethal forms. Understanding how these PC subtypes vary in their striving for energy and anabolic molecules is of fundamental importance for developing more effective therapies and diagnostics. Here, we carried out an extensive analysis of prostate tissue samples to reveal metabolic alterations during PC development and disease progression and furthermore between TMPRSS2-ERG rearrangement-positive and -negative PC subclasses.

Methods

Comprehensive metabolomics analysis of prostate tissue samples was performed by non-destructive high-resolution magic angle spinning nuclear magnetic resonance (¹H HR MAS NMR). Subsequently, samples underwent moderate extraction, leaving tissue morphology intact for histopathological characterization. Metabolites in tissue extracts were identified by ¹H/³¹P NMR and liquid chromatography-mass spectrometry (LC-MS). These metabolomics profiles were analyzed by chemometric tools and the outcome was further validated using proteomic data from a separate sample cohort.

Results

The obtained metabolite patterns significantly differed between PC and benign tissue and between samples with high and low Gleason score (GS). Five key metabolites (phosphocholine, glutamate, hypoxanthine, arginine and α-glucose) were identified, who were sufficient to differentiate between cancer and benign tissue and between high to low GS. In ERG-positive PC, the analysis revealed several acylcarnitines among the increased metabolites together with decreased levels of proteins involved in β-oxidation; indicating decreased acyl-CoAs oxidation in ERG-positive tumors. The ERG-positive group also showed increased levels of metabolites and proteins involved in purine catabolism; a potential sign of increased DNA damage and oxidative stress.

Conclusions

Our comprehensive metabolomic analysis strongly indicates that ERG-positive PC and ERG-negative PC should be considered as different subtypes of PC; a fact requiring different, sub-type specific treatment strategies for affected patients.

Rapid Prostate Cancer Noninvasive Biomarker Screening Using Segmented Flow Mass Spectrometry-Based Untargeted Metabolomics

Spectrometric methods with rapid biomarker detection capacity through untargeted metabolomics are becoming essential in the clinical cancer research. Liquid chromatography-mass spectrometry (LC-MS) is a rapidly developing metabolomic-based biomarker technique due to its high sensitivity, reproducibility, and separation efficiency. However, its translation to clinical diagnostics is often limited due to long data acquisition times (∼20 min/sample) and laborious sample extraction procedures when employed for large-scale metabolomics studies. Here, we developed a segmented flow approach coupled with high-resolution mass spectrometry (SF-HRMS) for untargeted metabolomics, which has the capability to acquire data in less than 1.5 min/sample with robustness and reproducibility relative to LC-HRMS. The SF-HRMS results demonstrate the capability for screening metabolite-based urinary biomarkers associated with prostate cancer (PCa). The study shows that SF-HRMS-based global metabolomics has the potential to evolve into a rapid biomarker screening tool for clinical research.

NMR-based metabolomics analysis identifies discriminatory metabolic disturbances in tissue and biofluid samples for progressive prostate cancer

BACKGROUND

Prostate cancer (PCa) is one of the most common cancers in men, but its metabolic characteristics during tumor progression are still far from being fully understood.

METHODS

The metabolic profiles of matched tissue, serum and urine samples from the same patients were analyzed using a ¹H NMR-based metabolomics approach. We identified several important metabolites that significantly altered at different stages of PCa, including benign prostatic hyperplasia (BPH), early PCa (EPC), advanced PCa (APC), metastatic PCa (MPC) and castration-resistant PCa (CRPC). Metabolic correlation networks among tissue, serum and urine samples were examined using Pearson's correlation.

RESULTS

The changes in metabolic phenotypes during the progression of PCa were more noticeable in tissue samples when compared with serum and urine samples. Herein we identified a series of important metabolic disturbances, including decreased trends of citrate, creatinine, acetate, leucine, valine, glycine, lysine, histidine, glutamine and choline as well as increased trends of uridine and formate. These metabolites are mainly implicated in energy metabolism, amino acid metabolism, choline and fatty acid metabolism as well as uridine metabolism. We also found that energy metabolism in tumor tissues was positively associated with amino acid metabolism in serum and urine. Additionally, CRPC patients had a peculiar metabolic phenotype, especially decreased amino acid metabolism in serum.

CONCLUSIONS

The present study characterizes metabolic disturbances in both tissue and biofluid samples during PCa progression and provides potential diagnostic biomarkers and therapeutic targets for PCa.

Benign Prostatic Hyperplasia and Lower Urinary Tract Symptoms: What Is the Role and Significance of Inflammation?

PURPOSE OF REVIEW

The purpose of this review is to summarize the role and significance of inflammation as a putative additional factor contributing to lower urinary tract symptoms and the progression of benign prostatic hyperplasia. We review (1) the histologic definition of prostatic inflammation and its prevalence, (2) the effects inflammation in the prostate including on risk of acute urinary retention, and (3) the effects of systemic inflammation on the prostate and on voiding.

RECENT FINDINGS

Inflammation is a highly prevalent finding in the prostate, both on a histological and biochemical level. Men with inflammation have higher IPSS scores and increased prostate size; however, these differences appear to be imperceptibly small. Men with inflammation do experience a significantly increased risk of developing acute urinary retention, an event that is associated with significant morbidity. Recently, attempts have been made to identify more specific biochemical markers of local inflammation, and to identify regional patterns of inflamed tissue within the prostate which may be associated with higher IPSS scores, accelerated progression, and AUR. The effects of systemic inflammatory states, most notably MetS, and their role in LUTS have also been examined. Inflammation is a common finding in prostates of aging men, but its contribution to lower urinary tract symptoms and benign prostatic hyperplasia progression appears to be small when considered as a clinically relevant entity. Advances in the understanding of different forms of inflammation, and their impact when experienced in different locations within the prostate, may refine this knowledge. Systemic inflammation affects voiding, including in the absence of a prostate, but again significant effects of systemic inflammation on the prostate itself are also difficult to demonstrate. Prostatic inflammation is associated with a significantly increased risk of acute urinary retention.