Application of Holistic Liquid Chromatography-High Resolution Mass Spectrometry Based Urinary Metabolomics for Prostate Cancer Detection and Biomarker Discovery

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.

LC/MS-based discrimination between plasma and urine metabolomic changes following exposure to ultraviolet radiation by using data modelling

INTRODUCTION

This study sought to compare between metabolomic changes of human urine and plasma to investigate which one can be used as best tool to identify metabolomic profiling and novel biomarkers associated to the potential effects of ultraviolet (UV) radiation.

METHOD

A pilot study of metabolomic patterns of human plasma and urine samples from four adult healthy individuals at before (S1) and after (S2) exposure (UV) and non-exposure (UC) were carried out by using liquid chromatography-mass spectrometry (LC-MS).

RESULTS

The best results which were obtained by normalizing the metabolites to their mean output underwent to principal components analysis (PCA) and Orthogonal Partial least squares-discriminant analysis (OPLS-DA) to separate pre-from post-of exposure and non-exposure of UV. This separation by data modeling was clear in urine samples unlike plasma samples. In addition to overview of the scores plots, the variance predicted-Q2 (Cum), variance explained-R2X (Cum) and p-value of the cross-validated ANOVA score of PCA and OPLS-DA models indicated to this clear separation. Q2 (Cum) and R2X (Cum) values of PCA model for urine samples were 0.908 and 0.982, respectively, and OPLS-DA model values were 1.0 and 0.914, respectively. While these values in plasma samples were Q2 = 0.429 and R2X = 0.660 for PCA model and Q2 = 0.983 and R2X = 0.944 for OPLS-DA model. LC-MS metabolomic analysis showed the changes in numerous metabolic pathways including: amino acid, lipids, peptides, xenobiotics biodegradation, carbohydrates, nucleotides, Co-factors and vitamins which may contribute to the evaluation of the effects associated with UV sunlight exposure.

CONCLUSIONS

The results of pilot study indicate that pre and post-exposure UV metabolomics screening of urine samples may be the best tool than plasma samples and a potential approach to predict the metabolomic changes due to UV exposure. Additional future work may shed light on the application of available metabolomic approaches to explore potential predictive markers to determine the impacts of UV sunlight.

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.

Noninvasive urine metabolomics of prostate cancer and its therapeutic approaches: a current scenario and future perspective

INTRODUCTION

The sensitive, specific, fast, robust and noninvasive biomarkers for the evaluation of prostate cancer (PC) remain elusive in medical research. However, efforts are in full sway to investigate and resolve these puzzles for clinical practice. Advances in modern analytical techniques, sample processing, and the emergence of multiple omics approaches have created a great hope for the development of better detection modalities for PC. The objective of the present review is to provide a concise overview of the PC metabolomics-based potential discriminating molecules in urine samples using nuclear magnetic resonance spectroscopy and mass spectrometry.

AREA COVERED

A literature search was executed to find the studies reporting the noninvasive urine-based biomarkers for the diagnosis and prognosis of underlying disease. Most studies have extensivelyreported PC discriminating molecules with their respective controls. Additionally, pathophysiology and the treatment paradigm of PC are summarized and related to the insights underpinning the therapeutic intervention of PC.

EXPERT OPINION

With multi-centric, global, comprehensive omics approaches via either a non- or least-invasive bio-matrix may open new avenues of research for PC biomarker discovery, backed by a molecular mechanistic outline.

Novel Metabolic Signatures of Prostate Cancer Revealed by 1H-NMR Metabolomics of Urine

Prostate cancer (PC) is one of the most common male cancers worldwide. Until now, there is no consensus about using urinary metabolomic profiling as novel biomarkers to identify PC. In this study, urine samples from 50 PC patients and 50 non-cancerous individuals (control group) were collected. Based on 1H nuclear magnetic resonance (1H-NMR) analysis, 20 metabolites were identified. Subsequently, principal component analysis (PCA), partial least squares-differential analysis (PLS-DA) and ortho-PLS-DA (OPLS-DA) were applied to find metabolites to distinguish PC from the control group. Furthermore, Wilcoxon test was used to find significant differences between the two groups in metabolite urine levels. Guanidinoacetate, phenylacetylglycine, and glycine were significantly increased in PC, while L-lactate and L-alanine were significantly decreased. The receiver operating characteristics (ROC) analysis revealed that the combination of guanidinoacetate, phenylacetylglycine, and glycine was able to accurately differentiate 77% of the PC patients with sensitivity = 80% and a specificity = 64%. In addition, those three metabolites showed significant differences in patients stratified for Gleason score 6 and Gleason score ≥7, indicating potential use to detect significant prostate cancer. Pathway enrichment analysis using the KEGG (Kyoto Encyclopedia of Genes and Genomes) and the SMPDB (The Small Molecule Pathway Database) revealed potential involvement of KEGG "Glycine, Serine, and Threonine metabolism" in PC. The present study highlights that guanidinoacetate, phenylacetylglycine, and glycine are potential candidate biomarkers of PC. To the best knowledge of the authors, this is the first study identifying guanidinoacetate, and phenylacetylglycine as potential novel biomarkers in PC.

Evaluation of heavy metals and metabolites in the urine of patients with breast cancer

Epidemiologic studies demonstrated that the environment serves a crucial role in cancer development. Heavy metals, including arsenic (As), cadmium (cd), chromium (Cr), lead and mercury, are considered to be carcinogens or co-carcinogens. Furthermore, Cd has been detected in breast cancer (BC) tissue at high concentrations. The present study aimed to investigate the correlation between heavy metals detected in urine and urine metabolome of patients with BC, and their association with cancer development. Nuclear magnetic resonance was used to determine urine metabolites and an inductively coupled plasma mass spectrometry system was used to detect heavy metals in urine samples. The results demonstrated that Cd was markedly increased in the urine of patients with BC compared with the control population (approximately 2-fold). Cr and As were also increased in the urine of patients with BC. In addition, numerous small molecule metabolites were altered in the urine of patients with BC compared with the control population. This study also demonstrated that alterations in small molecule metabolites in the urine of patients with BC were very similar to results from a previous report. These findings indicated that environmental exposure to Cd, As, or Cr could influence the urine levels of metabolites, which may be involved in BC development. Further investigation is therefore required to examine a larger range of samples from different countries or areas in order to understand the impact of heavy metals on metabolism and BC development.

Current Trends in Cancer Biomarker Discovery Using Urinary Metabolomics: Achievements and New Challenges

BACKGROUND

The development of effective screening methods for early cancer detection is one of the foremost challenges facing modern cancer research. Urinary metabolomics has recently emerged as a potentially transformative approach to cancer biomarker discovery owing to its noninvasive sampling characteristics and robust analytical feasibility.

OBJECTIVE

To provide an overview of new developments in urinary metabolomics, cover the most promising aspects of hyphenated techniques in untargeted and targeted metabolomics, and to discuss technical and clinical limitations in addition to the emerging challenges in the field of urinary metabolomics and its application to cancer biomarker discovery.

METHODS

A systematic review of research conducted in the past five years on the application of urinary metabolomics to cancer biomarker discovery was performed. Given the breadth of this topic, our review focused on the five most widely studied cancers employing urinary metabolomics approaches, including lung, breast, bladder, prostate, and ovarian cancers.

RESULTS

As an extension of conventional metabolomics, urinary metabolomics has benefitted from recent technological developments in nuclear magnetic resonance, mass spectrometry, gas and liquid chromatography, and capillary electrophoresis that have improved urine metabolome coverage and analytical reproducibility. Extensive metabolic profiling in urine has revealed a significant number of altered metabolic pathways and putative biomarkers, including pteridines, modified nucleosides, and acylcarnitines, that have been associated with cancer development and progression.

CONCLUSION

Urinary metabolomics presents a transformative new approach toward cancer biomarker discovery with high translational capacity to early cancer screening.

Treatment of Active Crohn's Disease With an Ordinary Food-based Diet That Replicates Exclusive Enteral Nutrition

BACKGROUND & AIMS

Exclusive enteral nutrition (EEN) is the only established dietary treatment for Crohn's disease (CD), but its acceptability is limited. There is a need for novel dietary treatments for CD.

METHODS

We evaluated the effects of an individualized food-based diet (CD-TREAT), with similar composition to EEN, on the gut microbiome, inflammation, and clinical response in a rat model, healthy adults, and children with relapsing CD. Twenty-five healthy adults randomly received EEN or CD-TREAT for 7 days, followed by a 14-day washout period, followed by the alternate diet. Fecal microbiome and metabolome were assessed before and after each diet. HLA-B7 and HLA-B27 transgenic rats with gut inflammation received EEN, CD-TREAT, or standard chow for 4 weeks. Fecal, luminal, and tissue microbiome, fecal metabolites, and gut inflammation were assessed. Five children with active CD activity received CD-TREAT and their clinical activity and calprotectin were evaluated after 8 weeks of treatment.

RESULTS

For healthy adults, CD-TREAT was easier to comply with and more acceptable than EEN. CD-TREAT induced similar effects to EEN (EEN vs CD-TREAT) on fecal microbiome composition, metabolome, mean total sulfide (increase 133.0 ± 80.5 vs 54.3 ± 47.0 nmol/g), pH (increase 1.3 ± 0.5 vs 0.9 ± 0.6), and the short-chain fatty acids (μmol/g) acetate (decrease 27.4 ± 22.6 vs 21.6 ± 20.4), propionate (decrease 5.7 ± 7.8 vs 5.2 ± 7.9), and butyrate (decrease 7.0 ± 7.4 vs 10.2 ± 8.5). In the rat model, CD-TREAT and EEN produced similar changes in bacterial load (decrease 0.3 ± 0.3 log₁₀ 16S rRNA gene copies per gram), short-chain fatty acids, microbiome, and ileitis severity (mean histopathology score decreases of 1.25 for EEN [P = .015] and 1.0 for CD-TREAT [P = .044] vs chow). In children receiving CD-TREAT, 4 (80%) had a clinical response and 3 (60%) entered remission, with significant concurrent decreases in fecal calprotectin (mean decrease 918 ± 555 mg/kg; P = .002).

CONCLUSION

CD-TREAT replicates EEN changes in the microbiome, decreases gut inflammation, is well tolerated, and is potentially effective in patients with active CD. ClinicalTrials.gov, numbers NCT02426567 and NCT03171246.

Metabolomics Biomarkers of Prostate Cancer: A Systematic Review

Prostate cancer (PCa) diagnosis with current biomarkers is difficult and often results in unnecessary invasive procedures as well as over-diagnosis and over-treatment, highlighting the need for novel biomarkers. The aim of this review is to provide a summary of available metabolomics PCa biomarkers, particularly for clinically significant disease. A systematic search was conducted on PubMed for publications from July 2008 to July 2018 in accordance with PRISMA guidelines to report biomarkers with respect to their application in PCa diagnosis, progression, aggressiveness, recurrence, and treatment response. The vast majority of studies report biomarkers with the ability to distinguish malignant from benign prostate tissue with a few studies investigating biomarkers associated with disease progression, treatment response or tumour recurrence. In general, these studies report high dimensional datasets and the number of analysed metabolites often significantly exceeded the number of available samples. Hence, observed multivariate differences between case and control samples in the datasets might potentially also be associated with pre-analytical, technical, statistical and confounding factors. Giving the technical and methodological hurdles, there are nevertheless a number of metabolites and pathways repeatedly reported across various technical approaches, cohorts and sample types that appear to play a predominant role in PCa tumour biology, progression and recurrence.

The crucial role of multiomic approach in cancer research and clinically relevant outcomes

Cancer with heavily economic and social burden is the hot point in the field of medical research. Some remarkable achievements have been made; however, the exact mechanisms of tumor initiation and development remain unclear. Cancer is a complex, whole-body disease that involves multiple abnormalities in the levels of DNA, RNA, protein, metabolite and medical imaging. Biological omics including genomics, transcriptomics, proteomics, metabolomics and radiomics aims to systematically understand carcinogenesis in different biological levels, which is driving the shift of cancer research paradigm from single parameter model to multi-parameter systematical model. The rapid development of various omics technologies is driving one to conveniently get multi-omics data, which accelerates predictive, preventive and personalized medicine (PPPM) practice allowing prediction of response with substantially increased accuracy, stratification of particular patients and eventual personalization of medicine. This review article describes the methodology, advances, and clinically relevant outcomes of different "omics" technologies in cancer research, and especially emphasizes the importance and scientific merit of integrating multi-omics in cancer research and clinically relevant outcomes.

Modulating the therapeutic response of tumours to dietary serine and glycine starvation

The non-essential amino acids serine and glycine are used in multiple anabolic processes that support cancer cell growth and proliferation (reviewed in ref. 1). While some cancer cells upregulate de novo serine synthesis, many others rely on exogenous serine for optimal growth. Restriction of dietary serine and glycine can reduce tumour growth in xenograft and allograft models. Here we show that this observation translates into more clinically relevant autochthonous tumours in genetically engineered mouse models of intestinal cancer (driven by Apc inactivation) or lymphoma (driven by Myc activation). The increased survival following dietary restriction of serine and glycine in these models was further improved by antagonizing the anti-oxidant response. Disruption of mitochondrial oxidative phosphorylation (using biguanides) led to a complex response that could improve or impede the anti-tumour effect of serine and glycine starvation. Notably, Kras-driven mouse models of pancreatic and intestinal cancers were less responsive to depletion of serine and glycine, reflecting an ability of activated Kras to increase the expression of enzymes that are part of the serine synthesis pathway and thus promote de novo serine synthesis.

The role of urinary pteridines as disease biomarkers

Pteridines and their derivatives function as intermediates in the metabolism of several vitamins and cofactors, and their relevance to disease has inspired new efforts to study their roles as disease biomarkers. Recent analytical advances, such as the emergence of sensitive mass spectrometry techniques, new workflows for measuring pteridine derivatives in their native oxidation states and increased multiplexing capacities for the simultaneous determination of many pteridine derivatives, have enabled researchers to explore the roles of urinary pteridines as disease biomarkers at much lower levels with greater accuracy than with previous technologies or methods. As a result, urinary pteridines are being increasingly studied as putative cancer biomarkers with promising results being reported from exploratory studies. In addition, the role of urinary neopterin as a universal biomarker for immune system activation is being investigated in new diseases where it is anticipated to become a useful supplementary marker in clinical diagnostic settings. In summary, this review provides an overview of recent developments in the clinical study of urinary pteridines as disease biomarkers, covers the most promising aspects of advanced analytical techniques being developed for the determination of urinary pteridines and discusses the major challenges associated with implementing pteridine biomarkers in clinical laboratory settings.

Non-invasive urinary metabolomic profiling discriminates prostate cancer from benign prostatic hyperplasia

INTRODUCTION

Prostate cancer (PCa) is one of the most common malignancies in men worldwide. Serum prostate specific antigen (PSA) level has been extensively used as a biomarker to detect PCa. However, PSA is not cancer-specific and various non-malignant conditions, including benign prostatic hyperplasia (BPH), can cause a rise in PSA blood levels, thus leading to many false positive results.

OBJECTIVES

In this study, we evaluated the potential of urinary metabolomic profiling for discriminating PCa from BPH.

METHODS

Urine samples from 64 PCa patients and 51 individuals diagnosed with BPH were analysed using 1H nuclear magnetic resonance (1H-NMR). Comparative analysis of urinary metabolomic profiles was carried out using multivariate and univariate statistical approaches.

RESULTS

The urine metabolomic profile of PCa patients is characterised by increased concentrations of branched-chain amino acids (BCAA), glutamate and pseudouridine, and decreased concentrations of glycine, dimethylglycine, fumarate and 4-imidazole-acetate compared with individuals diagnosed with BPH.

CONCLUSION

PCa patients have a specific urinary metabolomic profile. The results of our study underscore the clinical potential of metabolomic profiling to uncover metabolic changes that could be useful to discriminate PCa from BPH in a clinical context.

Metabolomic Profiling of the Effects of Melittin on Cisplatin Resistant and Cisplatin Sensitive Ovarian Cancer Cells Using Mass Spectrometry and Biolog Microarray Technology

In the present study, liquid chromatography-mass spectrometry (LC-MS) was employed to characterise the metabolic profiles of two human ovarian cancer cell lines A2780 (cisplatin-sensitive) and A2780CR (cisplatin-resistant) in response to their exposure to melittin, a cytotoxic peptide from bee venom. In addition, the metabolomics data were supported by application of Biolog microarray technology to examine the utilisation of carbon sources by the two cell lines. Data extraction with MZmine 2.14 and database searching were applied to provide metabolite lists. Principal component analysis (PCA) gave clear separation between the cisplatin-sensitive and resistant strains and their respective controls. The cisplatin-resistant cells were slightly more sensitive to melittin than the sensitive cells with IC₅₀ values of 4.5 and 6.8 μg/mL respectively, although the latter cell line exhibited the greatest metabolic perturbation upon treatment. The changes induced by melittin in the cisplatin-sensitive cells led mostly to reduced levels of amino acids in the proline/glutamine/arginine pathway, as well as to decreased levels of carnitines, polyamines, adenosine triphosphate (ATP) and nicotinamide adenine dinucleotide (NAD+). The effects on energy metabolism were supported by the data from the Biolog assays. The lipid compositions of the two cell lines were quite different with the A2780 cells having higher levels of several ether lipids than the A2780CR cells. Melittin also had some effect on the lipid composition of the cells. Overall, this study suggests that melittin might have some potential as an adjuvant therapy in cancer treatment.

HILIC-MS for metabolomics: An attractive and complementary approach to RPLC-MS

Hydrophilic interaction chromatography (HILIC) is an emerging separation mode of liquid chromatography (LC). Using highly hydrophilic stationary phases capable of retaining polar/ionic metabolites, and accompany with high organic content mobile phase that offer readily compatibility with mass spectrometry (MS) has made HILIC an attractive complementary tool to the widely used reverse-phase (RP) chromatographic separations in metabolomic studies. The combination of HILIC and RPLC coupled with an MS detector expands the number of detected analytes and provides more comprehensive metabolite coverage than use of only RP chromatography. This review describes the recent applications of HILIC-MS/MS in metabolomic studies, ranging from amino acids, lipids, nucleotides, organic acids, pharmaceuticals, and metabolites of specific nature. The biological systems investigated include microbials, cultured cell line, plants, herbal medicine, urine, and serum as well as tissues from animals and humans. Owing to its unique capability to measure more-polar biomolecules, the HILIC separation technique would no doubt enhance the comprehensiveness of metabolite detection, and add significant value for metabolomic investigations. © 2014 Wiley Periodicals, Inc. Mass Spec Rev 35:574-600, 2016.

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

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

Methods used to increase the comprehensive coverage of urinary and plasma metabolomes by MS

Metabolomics, focusing on comprehensive analysis of all the metabolites in a biological system, provides a direct signature of biochemical activity. Using emerging technologies in MS, it is possible to simultaneously and rapidly analyze thousands of metabolites. However, due to the chemical and physical diversity of metabolites, it is difficult to acquire a comprehensive and reliable profiling of the whole metabolome. Here, we summarize the state of the art in metabolomics research, focusing on efforts to provide a more comprehensive metabolome coverage via improvements in two fundamental processes: sample preparation and MS analysis. Additionally, the reliable analysis is also highlighted via the combinations of multiple methods (e.g., targeted and untargeted approaches), and analytical quality control and calibration methods.

Metabolomic Biomarkers of Prostate Cancer: Prediction, Diagnosis, Progression, Prognosis, and Recurrence

Metabolite profiling is being increasing employed in the study of prostate cancer as a means of identifying predictive, diagnostic, and prognostic biomarkers. This review provides a summary and critique of the current literature. Thirty-three human case-control studies of prostate cancer exploring disease prediction, diagnosis, progression, or treatment response were identified. All but one demonstrated the ability of metabolite profiling to distinguish cancer from benign, tumor aggressiveness, cases who recurred, and those who responded well to therapy. In the subset of studies where biomarker discriminatory ability was quantified, high AUCs were reported that would potentially outperform the current gold standards in diagnosis, prognosis, and disease recurrence, including PSA testing. There were substantial similarities between the metabolites and the associated pathways reported as significant by independent studies, and important roles for abnormal cell growth, intensive cell proliferation, and dysregulation of lipid metabolism were highlighted. The weight of the evidence therefore suggests metabolic alterations specific to prostate carcinogenesis and progression that may represent potential metabolic biomarkers. However, replication and validation of the most promising biomarkers is currently lacking and a number of outstanding methodologic issues remain to be addressed to maximize the utility of metabolomics in the study of prostate cancer. Cancer Epidemiol Biomarkers Prev; 25(6); 887-906. ©2016 AACR.

Metabolomic Profiling of Post-Mortem Brain Reveals Changes in Amino Acid and Glucose Metabolism in Mental Illness Compared with Controls

Metabolomic profiling was carried out on 53 post-mortem brain samples from subjects diagnosed with schizophrenia, depression, bipolar disorder (SDB), diabetes, and controls. Chromatography on a ZICpHILIC column was used with detection by Orbitrap mass spectrometry. Data extraction was carried out with m/z Mine 2.14 with metabolite searching against an in-house database. There was no clear discrimination between the controls and the SDB samples on the basis of a principal components analysis (PCA) model of 755 identified or putatively identified metabolites. Orthogonal partial least square discriminant analysis (OPLSDA) produced clear separation between 17 of the controls and 19 of the SDB samples (R2CUM 0.976, Q2 0.671, p-value of the cross-validated ANOVA score 0.0024). The most important metabolites producing discrimination were the lipophilic amino acids leucine/isoleucine, proline, methionine, phenylalanine, and tyrosine; the neurotransmitters GABA and NAAG and sugar metabolites sorbitol, gluconic acid, xylitol, ribitol, arabinotol, and erythritol. Eight samples from diabetic brains were analysed, six of which grouped with the SDB samples without compromising the model (R2 CUM 0.850, Q2 CUM 0.534, p-value for cross-validated ANOVA score 0.00087). There appears on the basis of this small sample set to be some commonality between metabolic perturbations resulting from diabetes and from SDB.

Metabolomic Profiling of Submaximal Exercise at a Standardised Relative Intensity in Healthy Adults

Ten physically active subjects underwent two cycling exercise trials. In the first, aerobic capacity (VO2max) was determined and the second was a 45 min submaximal exercise test. Urine samples were collected separately the day before (day 1) , the day of (day 2), and the day after (day 3) the submaximal exercise test (12 samples per subject). Metabolomic profiling of the samples was carried out using hydrophilic interaction chromatography (HILIC) coupled to an Orbitrap Exactive mass spectrometer. Data were extracted, database searched and then subjected to principle components (PCA) and orthogonal partial least squares (OPLSDA) modelling. The best results were obtained from pre-treating the data by normalising the metabolites to their mean output on days 1 and 2 of the trial. This allowed PCA to separate the day 2 first void samples (D2S1) from the day 2 post-exercise samples (D2S3) PCA also separated the equivalent samples obtained on day 1 (D1S1 and D1S3). OPLSDA modelling separated both the D2S1 and D2S3 samples and D1S1 and D1S3 samples. The metabolites affected by the exercise samples included a range of purine metabolites and several acyl carnitines. Some metabolites were subject to diurnal variation these included bile acids and several amino acids, the variation of these metabolites was similar on day 1 and day 2 despite the exercise intervention on day 2. Using OPLS modelling it proved possible to identify a single abundant urinary metabolite provisionally identified as oxo-aminohexanoic acid (OHA) as being strongly correlated with VO2max when the levels in the D2S3 samples were considered.

Changes in the Milk Metabolome of the Giant Panda (Ailuropoda melanoleuca) with Time after Birth--Three Phases in Early Lactation and Progressive Individual Differences

Ursids (bears) in general, and giant pandas in particular, are highly altricial at birth. The components of bear milks and their changes with time may be uniquely adapted to nourish relatively immature neonates, protect them from pathogens, and support the maturation of neonatal digestive physiology. Serial milk samples collected from three giant pandas in early lactation were subjected to untargeted metabolite profiling and multivariate analysis. Changes in milk metabolites with time after birth were analysed by Principal Component Analysis, Hierarchical Cluster Analysis and further supported by Orthogonal Partial Least Square-Discriminant Analysis, revealing three phases of milk maturation: days 1–6 (Phase 1), days 7–20 (Phase 2), and beyond day 20 (Phase 3). While the compositions of Phase 1 milks were essentially indistinguishable among individuals, divergences emerged during the second week of lactation. OPLS regression analysis positioned against the growth rate of one cub tentatively inferred a correlation with changes in the abundance of a trisaccharide, isoglobotriose, previously observed to be a major oligosaccharide in ursid milks. Three artificial milk formulae used to feed giant panda cubs were also analysed, and were found to differ markedly in component content from natural panda milk. These findings have implications for the dependence of the ontogeny of all species of bears, and potentially other members of the Carnivora and beyond, on the complexity and sequential changes in maternal provision of micrometabolites in the immediate period after birth.

Metabolomic profiling of hormone-dependent cancers: a bird's eye view

Hormone-dependent cancers present a significant public health challenge, because they are among the most common cancers in the world. One factor associated with cancer development and progression is metabolic reprogramming. By understanding these alterations, we can identify potential markers and novel biochemical therapeutic targets. Metabolic profiling is an advanced technology that allows investigators to assess low-molecular-weight compounds that reflect physiological alterations. Current research in metabolomics on prostate (PCa) and breast cancer (BCa) have made great strides in uncovering specific metabolic pathways that are associated with cancer development, progression, and resistance. In this review, we highlight some of the major findings and potential therapeutic advances that have been reported utilizing this technology.

Translational metabolomics in cancer research

Over the last decade there has been a bottleneck in the introduction of new validated cancer metabolic biomarkers into clinical practice. Unfortunately, there are no biomarkers with adequate sensitivity for the early detection of cancer, and there remain a reliance on cancer antigens for monitoring treatment. The need for new diagnostics has led to the exploration of untargeted metabolomics for discovery of early biomarkers of specific cancers and targeted metabolomics to elucidate mechanistic aspects of tumor progression. The successful translation of such strategies to the treatment of cancer would allow earlier intervention to improve survival. We have reviewed the methodology that is being used to achieve these goals together with recent advances in implementing translational metabolomics in cancer.

Metabolomics changes in a rat model of obstructive jaundice: mapping to metabolism of amino acids, carbohydrates and lipids as well as oxidative stress

The study examined the global metabolic and some biochemical changes in rats with cholestasis induced by bile duct ligation (BDL). Serum samples were collected in male Wistar rats with BDL (n = 8) and sham surgery (n = 8) at day 3 after surgery for metabolomics analysis using a combination of reversed phase chromatography and hydrophilic interaction chromatography (HILIC) and quadrupole-time-of-flight mass spectrometry (Q-TOF MS). The serum levels of malondialdehyde (MDA), total antioxidative capacity (T-AOC), glutathione (GSH) and glutathione disulfide (GSSG), the activities of superoxide dismutase (SOD) and glutathion peroxidase (GSH-Px) were measured to estimate the oxidative stress state. Key changes after BDL included increased levels of l-phenylalanine, l-glutamate, l-tyrosine, kynurenine, l-lactic acid, LysoPC^c (14:0), glycine and succinic acid and decreased levels of l-valine, PC^b (19:0/0:0), taurine, palmitic acid, l-isoleucine and citric acid metabolism products. And treatment with BDL significantly decreased the levels of GSH, T-AOC as well as SOD, GSH-Px activities, and upregulated MDA levels. The changes could be mapped to metabolism of amino acids and lipids, Krebs cycle and glycolysis, as well as increased oxidative stress and decreased antioxidant capability. Our study indicated that BDL induces major changes in the metabolism of all 3 major energy substances, as well as oxidative stress.

Metabolomic profiles illuminate the efficacy of Chinese herbal Da-Cheng-Qi decoction on acute pancreatitis in rats

BACKGROUND AND OBJECTIVES

Chinese herbal drug Da-Cheng-Qi decoction (DCQD) has been widely used for decades to treat acute pancreatitis (AP). Previous trials are mostly designed to state the potential mechanisms of the therapeutic effects rather than to detect its whole effect on metabolism. This study aimed to investigate the efficacy of DCQD on metabolism in AP.

METHODS

Twenty-two male adult Sprague-Dawley rats were randomized into three groups. AP was induced by retrograde ductal infusion of 3.5% sodium taurocholate solution in DCQD and AP group, while 0.9% saline solution was used in sham operation (SO) group. Blood samples were obtained 12 h after drug administration and a 600 MHz superconducting Nuclear Magnetic Resonance (NMR) spectrometer was used to detected plasma metabolites. Principal Components Analysis (PCA) and Partial Least Squares-Discriminant Analysis after Orthogonal Signal Correction (OSC-PLS-DA) were applied to analyze the Longitudinal Eddy-delay (LED) and Carr-Purcell-Meiboom-Gill (CPMG) spectra.

RESULTS

Differences in concentrations of metabolites among the three groups were detected by OSC-PLS-DA of 1HNMR spectra (both LED and CPMG). Compared with SO group, DCQD group had higher levels of plasma glycerol, glutamic acid, low density lipoprotein (LDL), saturated fatty acid (FA) and lower levels of alanine and glutamine, while the metabolic changes were reversed in the AP group.

CONCLUSIONS

Our results demonstrated that DCQD was capable of altering the changed concentrations of metabolites in rats with AP and 1HNMR-based metabolomic approach provided a new methodological cue for systematically investigating the efficacies and mechanisms of DCQD in treating AP.

Optimized Analytical Procedures for the Untargeted Metabolomic Profiling of Human Urine and Plasma by Combining Hydrophilic Interaction (HILIC) and Reverse-Phase Liquid Chromatography (RPLC)-Mass Spectrometry

Profiling of body fluids is crucial for monitoring and discovering metabolic markers of health and disease and for providing insights into human physiology. Since human urine and plasma each contain an extreme diversity of metabolites, a single liquid chromatographic system when coupled to mass spectrometry (MS) is not sufficient to achieve reasonable metabolome coverage. Hydrophilic interaction liquid chromatography (HILIC) offers complementary information to reverse-phase liquid chromatography (RPLC) by retaining polar metabolites. With the objective of finding the optimal combined chromatographic solution to profile urine and plasma, we systematically investigated the performance of five HILIC columns with different chemistries operated at three different pH (acidic, neutral, basic) and five C18-silica RPLC columns. The zwitterionic column ZIC-HILIC operated at neutral pH provided optimal performance on a large set of hydrophilic metabolites. The RPLC columns Hypersil GOLD and Zorbax SB aq were proven to be best suited for the metabolic profiling of urine and plasma, respectively. Importantly, the optimized HILIC-MS method showed excellent intrabatch peak area reproducibility (CV < 12%) and good long-term interbatch (40 days) peak area reproducibility (CV < 22%) that were similar to those of RPLC-MS procedures. Finally, combining the optimal HILIC- and RPLC-MS approaches greatly expanded metabolome coverage with 44% and 108% new metabolic features detected compared with RPLC-MS alone for urine and plasma, respectively. The proposed combined LC-MS approaches improve the comprehensiveness of global metabolic profiling of body fluids and thus are valuable for monitoring and discovering metabolic changes associated with health and disease in clinical research studies.

A study of the effects of exercise on the urinary metabolome using normalisation to individual metabolic output

Aerobic exercise, in spite of its multi-organ benefit and potent effect on the metabolome, has yet to be investigated comprehensively via an untargeted metabolomics technology. We conducted an exploratory untargeted liquid chromatography mass spectrometry study to investigate the effects of a one-h aerobic exercise session in the urine of three physically active males. Individual urine samples were collected over a 37-h protocol (two pre-exercise and eight post-exercise). Raw data were subjected to a variety of normalization techniques, with the most effective measure dividing each metabolite by the sum response of that metabolite for each individual across the 37-h protocol expressed as a percentage. This allowed the metabolite responses to be plotted on a normalised scale. Our results highlight significant metabolites located in the following systems: purine pathway, tryptophan metabolism, carnitine metabolism, cortisol metabolism, androgen metabolism, amino acid oxidation, as well as metabolites from the gastrointestinal microbiome. Many of the significant changes observed in our pilot investigation mirror previous research studies, of various methodological designs, published within the last 15 years, although they have never been reported at the same time in a single study.

Current state-of-the-art of nontargeted metabolomics based on liquid chromatography-mass spectrometry with special emphasis in clinical applications

Metabolomics, as a part of systems biology, has been widely applied in different fields of life science by studying the endogenous metabolites. The development and applications of liquid chromatography (LC) coupled with high resolution mass spectrometry (MS) greatly improve the achievable data quality in non-targeted metabolic profiling. However, there are still some emerging challenges to be covered in LC-MS based metabolomics. Here, recent approaches about sample collection and preparation, instrumental analysis, and data handling of LC-MS based metabolomics are summarized, especially in the analysis of clinical samples. Emphasis is put on the improvement of analytical techniques including the combination of different LC columns, isotope coded derivatization methods, pseudo-targeted LC-MS method, new data analysis algorithms and structural identification of important metabolites.

Metabolomic investigation of human diseases biomarkers by CE and LC coupled to MS

Metabolomics is one of the most recent trends in the "omics" era that investigates the end products of an organism activity, that is, all metabolites in a biological system, which are small molecules (less than 1000 Da) from different chemical classes. Metabolomics represents a tool to assess the biochemical activity of a living system through the analysis of substrates and products processed during the metabolism. The analysis of the metabolic profile (nontargeted analysis, i.e. a comparison between samples profiles of individuals) and of specific metabolites (targeted analysis, which quantifies a selected group of metabolites) in biological samples provides an insight into the metabolic state and the biochemical processes of the organism and, therefore, may indicate the onset and the stage of different diseases. An early and accurate diagnosis is essential for successful treatment and probable cure of most illnesses; hence, the investigation of metabolites as disease biomarkers has increased considerably in recent years. This review aims to present the most relevant works that address the nontargeted and targeted analysis of metabolites in different diseases for the past 10 years, including kidney and neurological disorders, cardiovascular diseases, diabetes, and cancer, using CE and LC coupled with the accurate detection of mass spectroscopy.

Metabolomics coupled with pattern recognition and pathway analysis on potential biomarkers in liver injury and hepatoprotective effects of yinchenhao

Metabolomics can provide an opportunity to develop the systematic analysis of the metabolites in biological samples and has been increasingly applied to discovering and identifying biomarkers and perturbed pathways. It enables us to better understand the metabolic pathways which can clarify the mechanism of traditional Chinese medicines (TCM). Yinchenhao (YCH, Artemisia annua L), a famous TCM plant, has been used clinically for more than a thousand years to relieve liver diseases in Asia, and its mechanisms are not still completely clear. Here, metabolomic techniques may provide additional insight, and our investigation was designed to assess the effects and possible mechanisms of YCH on α-naphthylisothiocyanate (ANIT)-induced liver injury. Metabolite profiling was performed by ultra-performance liquid chromatography/electrospray ionization quadruple time-of-flight mass spectrometry (UPLC/ESI-Q-TOF/MS) combined with pathway analysis and pattern recognition approaches including independent component analysis (ICA) and partial least squares-discriminant analysis (PLS-DA). Biochemistry test was also performed for the liver tissue and plasma samples. The changes in metabolic profiling were restored to their baseline values after YCH treatment according to the ICA score plots. Of note, YCH has a potential pharmacological effect through regulating multiple perturbed pathways to normal state, correlating well to the assessment of biochemistry test. Five different potential biomarkers in the positive mode contributing to the treatment of YCH were discovered. Pathway analysis showed that these metabolites were associated with perturbations in pyrimidine metabolism, primary bile acid biosynthesis, and propanoate metabolism, which may be helpful to further understand the action mechanisms of YCH. It showed that changed biomarkers and pathways may provide evidence to insight into drug action mechanisms and drug discovery.

Metabolomic analysis of diet-induced type 2 diabetes using UPLC/MS integrated with pattern recognition approach

Metabolomics represents an emerging discipline concerned with comprehensive assessment of small molecule endogenous metabolites in biological systems and provides a powerful approach insight into the mechanisms of diseases. Type 2 diabetes (T2D), called the burden of the 21^st century, is growing with an epidemic rate. However, its precise molecular mechanism has not been comprehensively explored. In this study, we applied urinary metabolomics based on the UPLC/MS integrated with pattern recognition approaches to discover differentiating metabolites, to characterize and explore metabolic pathway disruption in an experimental model for high-fat-diet induced T2D. Six differentiating urinary metabolites were found in the negative mode, and two (2-(4-hydroxy-3-methoxy-phenyl) acetaldehyde sulfate, 2-phenylethanol glucuronide) of which were identified involving the key metabolic pathways linked to pentose and glucuronate interconversions, starch, sucrose metabolism and tyrosine metabolism. Our study provides new insight into pathophysiologic mechanisms and may enhance the understanding of T2D pathogenesis.

Detection of prostate cancer by an electronic nose: a proof of principle study

PURPOSE

We evaluate the ability of an electronic nose to discriminate prostate cancer from benign prostatic hyperplasia using urine headspace, potentially offering a clinically applicable noninvasive and rapid diagnostic method.

MATERIALS AND METHODS

The ChemPro® 100-eNose was used to discriminate prostate cancer from benign prostatic hyperplasia using urine sample headspace. Its performance was tested with 50 patients with confirmed prostate cancer and 24 samples from 15 patients with benign prostatic hyperplasia (15 patients provided urine preoperatively and 9 patients provided samples 3 months postoperatively) scheduled to undergo robotic assisted laparoscopic radical prostatectomy or transurethral resection of prostate, respectively. The patients provided urine sample preoperatively and those with benign prostatic hyperplasia also provided samples 3 months postoperatively to be used as a pooled control sample population. A discrimination classifier was identified for eNose and subsequently, sensitivity and specificity values were determined. Leave-one-out cross-validation was performed.

RESULTS

Using leave-one-out cross-validation the eNose reached a sensitivity of 78%, a specificity of 67% and AUC 0.77.

CONCLUSIONS

The electronic nose is capable of rapidly and noninvasively discriminating prostate cancer and benign prostatic hyperplasia using urine headspace in patients undergoing surgery.