Metabonomic profiling of bladder cancer

Mass Spectrometric Identification of Metabolites after Magnetic-Pulse Treatment of Infected Pyrus communis L. Microplants

The major goal of this study is to create a venue for further work on the effect of pulsed magnetic fields on plant metabolism. It deals with metabolite synthesis in the aforementioned conditions in microplants of Pyrus communis L. So far, there have been glimpses into the governing factors of plant biochemistry in vivo, and low-frequency pulsed magnestatic fields have been shown to induce additional electric currents in plant tissues, thus perturbing the value of cell membrane potential and causing the biosynthesis of new metabolites. In this study, sixty-seven metabolites synthesized in microplants within 3-72 h after treatment were identified and annotated. In total, thirty-one metabolites were produced. Magnetic-pulse treatment caused an 8.75-fold increase in the concentration of chlorogenic acid (RT = 8.33 ± 0.0197 min) in tissues and the perturbation of phenolic composition. Aucubin, which has antiviral and antistress biological activity, was identified as well. This study sheds light on the effect of magnetic fields on the biochemistry of low-molecular-weight metabolites of pear plants in vitro, thus providing in-depth metabolite analysis under optimized synthetic conditions. This study utilized high-resolution gas chromatography-mass spectrometry, metabolomics methods, stochastic dynamics mass spectrometry, quantum chemistry, and chemometrics, respectively. Stochastic dynamics uses the relationships between measurands and molecular structures of silylated carbohydrates, showing virtually identical mass spectra and comparable chemometrics parameters.

p53 Activates the Lipoxygenase Activity of ALOX15B via Inhibiting SLC7A11 to Induce Ferroptosis in Bladder Cancer Cells

Bladder cancer is a malignant tumor of the urinary system and is one of the most common cancers worldwide. Lipoxygenases are closely related to the development of various cancers. However, the relationship between lipoxygenases and p53/SLC7A11-dependent ferroptosis in bladder cancer has not been reported. Here, we aimed to investigate the roles and internal mechanisms of lipid peroxidation and p53/SLC7A11-dependent ferroptosis in the development and progression of bladder cancer. First, ultraperformance liquid chromatography-tandem mass spectrometry was performed to measure the metabolite production of lipid oxidation in patients' plasma. The metabolic changes in patients with bladder cancer were discovered, revealing that stevenin, melanin, and octyl butyrate were upregulated. Then, the expressions of lipoxygenase family members were measured to screen out candidates with significant changes in bladder cancer tissues. Among various lipoxygenases, ALOX15B was significantly downregulated in bladder cancer tissues. Moreover, p53 and 4-hydroxynonenal (4-HNE) levels were decreased in bladder cancer tissues. Next, sh-ALOX15B, oe-ALOX15B, or oe-SLC7A11 plasmids were constructed and transfected into bladder cancer cells. Then, the p53 agonist Nutlin-3a, tert-butyl hydroperoxide, iron chelator deferoxamine, and the selective ferroptosis inhibitor ferr1 were added. The effects of ALOX15B and p53/SLC7A11 on bladder cancer cells were evaluated by in vitro and in vivo experiments. We revealed that knockdown of ALOX15B promoted bladder cancer cell growth, which was also found to protect bladder cancer cells from p53-induced ferroptosis. Furthermore, p53 activated ALOX15B lipoxygenase activity by suppressing SLC7A11. Taken together, p53 activated the lipoxygenase activity of ALOX15B via inhibiting SLC7A11 to induce ferroptosis in bladder cancer cells, which provided insight into the molecular mechanism of the occurrence and development of bladder cancer.

Extraction of High-Value Chemicals from Plants for Technical and Medical Applications

Plants produce a variety of high-value chemicals (e.g., secondary metabolites) which have a plethora of biological activities, which may be utilised in many facets of industry (e.g., agrisciences, cosmetics, drugs, neutraceuticals, household products, etc.). Exposure to various different environments, as well as their treatment (e.g., exposure to chemicals), can influence the chemical makeup of these plants and, in turn, which chemicals will be prevalent within them. Essential oils (EOs) usually have complex compositions (>300 organic compounds, e.g., alkaloids, flavonoids, phenolic acids, saponins and terpenes) and are obtained from botanically defined plant raw materials by dry/steam distillation or a suitable mechanical process (without heating). In certain cases, an antioxidant may be added to the EO (EOs are produced by more than 17,500 species of plants, but only ca. 250 EOs are commercially available). The interesting bioactivity of the chemicals produced by plants renders them high in value, motivating investment in their production, extraction and analysis. Traditional methods for effectively extracting plant-derived biomolecules include cold pressing and hydro/steam distillation; newer methods include solvent/Soxhlet extractions and sustainable processes that reduce waste, decrease processing times and deliver competitive yields, examples of which include microwave-assisted extraction (MAE), ultrasound-assisted extraction (UAE), subcritical water extraction (SWE) and supercritical CO2 extraction (scCO2). Once extracted, analytical techniques such as chromatography and mass spectrometry may be used to analyse the contents of the high-value extracts within a given feedstock. The bioactive components, which can be used in a variety of formulations and products (e.g., displaying anti-aging, antibacterial, anticancer, anti-depressive, antifungal, anti-inflammatory, antioxidant, antiparasitic, antiviral and anti-stress properties), are biorenewable high-value chemicals.

Metabolomic Approaches for Detection and Identification of Biomarkers and Altered Pathways in Bladder Cancer

Metabolomic analysis has proven to be a useful tool in biomarker discovery and the molecular classification of cancers. In order to find new biomarkers, and to better understand its pathological behavior, bladder cancer also has been studied using a metabolomics approach. In this article, we review the literature on metabolomic studies of bladder cancer, focusing on the different available samples (urine, blood, tissue samples) used to perform the studies and their relative findings. Moreover, the multi-omic approach in bladder cancer research has found novel insights into its metabolic behavior, providing excellent start-points for new diagnostic and therapeutic strategies. Metabolomics data analysis can lead to the discovery of a “signature pathway” associated with the progression of bladder cancer; this aspect could be potentially valuable in predictions of clinical outcomes and the introduction of new treatments. However, further studies are needed to give stronger evidence and to make these tools feasible for use in clinical practice.

Metabolomic credentialing of murine carcinogen-induced urothelial cancer

Bladder cancer (BCa) is the most common malignancy of the urinary system with increasing incidence, mortality, and limited treatment options. Therefore, it is imperative to validate preclinical models that faithfully represent BCa cellular, molecular, and metabolic heterogeneity to develop new therapeutics. We performed metabolomic profiling of premalignant and non-muscle invasive bladder cancer (NMIBC) that ensued in the chemical carcinogenesis N-butyl-N-(4-hydroxybutyl)-nitrosamine (BBN) mouse model. We identified the enriched metabolic signatures that associate with premalignant and NMIBC. We found that enrichment of lipid metabolism is the forerunner of carcinogen-induced premalignant and NMIBC lesions. Cross-species analysis revealed the prognostic value of the enzymes associated with carcinogen-induced enriched metabolic in human disease. To date, this is the first study describing the global metabolomic profiles associated with early premalignant and NMIBC and provide evidence that these metabolomic signatures can be used for prognostication of human disease.

Urinary Metabolic Markers of Bladder Cancer: A Reflection of the Tumor or the Response of the Body?

This work will review the metabolic information that various studies have obtained in recent years on bladder cancer, with particular attention to discovering biomarkers in urine for the diagnosis and prognosis of this disease. In principle, they would be capable of complementing cystoscopy, an invasive but nowadays irreplaceable technique or, in the best case, of replacing it. We will evaluate the degree of reproducibility that the different experiments have shown in the indication of biomarkers, and a synthesis will be attempted to obtain a consensus list that is more likely to become a guideline for clinical practice. In further analysis, we will inquire into the origin of these dysregulated metabolites in patients with bladder cancer. For this purpose, it will be helpful to compare the imbalances measured in urine with those known inside tumor cells or tissues. Although the urine analysis is sometimes considered a liquid biopsy because of its direct contact with the tumor in the bladder wall, it contains metabolites from all organs and tissues of the body, and the tumor is separated from urine by the most impermeable barrier found in mammals. The distinction between the specific and systemic responses can help understand the disease and its consequences in more depth.

Metabolomics: A Scoping Review of Its Role as a Tool for Disease Biomarker Discovery in Selected Non-Communicable Diseases

Metabolomics is a branch of ‘omics’ sciences that utilises a couple of analytical tools for the identification of small molecules (metabolites) in a given sample. The overarching goal of metabolomics is to assess these metabolites quantitatively and qualitatively for their diagnostic, therapeutic, and prognostic potentials. Its use in various aspects of life has been documented. We have also published, howbeit in animal models, a few papers where metabolomic approaches were used in the study of metabolic disorders, such as metabolic syndrome, diabetes, and obesity. As the goal of every research is to benefit humankind, the purpose of this review is to provide insights into the applicability of metabolomics in medicine vis-à-vis its role in biomarker discovery for disease diagnosis and management. Here, important biomarkers with proven diagnostic and therapeutic relevance in the management of disease conditions, such as Alzheimer’s disease, dementia, Parkinson’s disease, inborn errors of metabolism (IEM), diabetic retinopathy, and cardiovascular disease, are noted. The paper also discusses a few reasons why most metabolomics-based laboratory discoveries are not readily translated to the clinic and how these could be addressed going forward.

Metabolic Evaluation of Urine from Patients Diagnosed with High Grade (HG) Bladder Cancer by SPME-LC-MS Method

Bladder cancer (BC) is a common malignancy of the urinary system and a leading cause of death worldwide. In this work, untargeted metabolomic profiling of biological fluids is presented as a non-invasive tool for bladder cancer biomarker discovery as a first step towards developing superior methods for detection, treatment, and prevention well as to further our current understanding of this disease. In this study, urine samples from 24 healthy volunteers and 24 BC patients were subjected to metabolomic profiling using high throughput solid-phase microextraction (SPME) in thin-film format and reversed-phase high-performance liquid chromatography coupled with a Q Exactive Focus Orbitrap mass spectrometer. The chemometric analysis enabled the selection of metabolites contributing to the observed separation of BC patients from the control group. Relevant differences were demonstrated for phenylalanine metabolism compounds, i.e., benzoic acid, hippuric acid, and 4-hydroxycinnamic acid. Furthermore, compounds involved in the metabolism of histidine, beta-alanine, and glycerophospholipids were also identified. Thin-film SPME can be efficiently used as an alternative approach to other traditional urine sample preparation methods, demonstrating the SPME technique as a simple and efficient tool for urinary metabolomics research. Moreover, this study's results may support a better understanding of bladder cancer development and progression mechanisms.

Metabolomic approaches for the determination of metabolites from pathogenic microorganisms: A review

Metabolomics is a high precision analytical approach to obtaining detailed information of varieties of metabolites produced in biological systems, including foods. This study reviews the use of metabolomic approaches such as liquid chromatography mass spectrometry (LCMS), gas chromatography mass spectrometry (GC-MS), matrix assisted laser desorption /ionization tandem time of flight mass spectrometry (MALDI-TOF-MS) and nuclear magnetic resonance (NMR) for investigating the presence of foodborne pathogens and their metabolites. Pathogenic fungi and their notable metabolites (mycotoxins) have been studied more extensively using metabolomics as compared to bacteria, necessitating further studies in this regard. Nevertheless, such identified fungal and bacteria metabolites could be used as biomarkers for a more rapid detection of these pathogens in food. Other important compounds detected through metabolomics could also be correlated to functionality of these pathogenic strains, determined by the composition of the foods in which they exist, thereby providing insights into their metabolism. Considering the prevalence of these food pathogens, metabolomics still has potentials in the determination of food-borne pathogenic microorganisms especially for the determination of pathogenic bacteria toxins and is expected to generate research interests for further studies and applications.

A Urine Metabonomics Study of Rat Bladder Cancer by Combining Gas Chromatography-Mass Spectrometry with Random Forest Algorithm

A urine metabolomics study based on gas chromatography-mass spectrometry (GC-MS) and multivariate statistical analysis was applied to distinguish rat bladder cancer. Urine samples with different stages were collected from animal models, i.e., the early stage, medium stage, and advanced stage of the bladder cancer model group and healthy group. After resolving urea with urease, the urine samples were extracted with methanol and, then, derived with N, O-Bis(trimethylsilyl) trifluoroacetamide and trimethylchlorosilane (BSTFA + TMCS, 99 : 1, v/v), before analyzed by GC-MS. Three classification models, i.e., healthy control vs. early- and middle-stage groups, healthy control vs. advanced-stage group, and early- and middle-stage groups vs. advanced-stage group, were established to analyze these experimental data by using Random Forests (RF) algorithm, respectively. The classification results showed that combining random forest algorithm with metabolites characters, the differences caused by the progress of disease could be effectively exhibited. Our results showed that glyceric acid, 2, 3-dihydroxybutanoic acid, N-(oxohexyl)-glycine, and D-turanose had higher contributions in classification of different groups. The pathway analysis results showed that these metabolites had relationships with starch and sucrose, glycine, serine, threonine, and galactose metabolism. Our study results suggested that urine metabolomics was an effective approach for disease diagnosis.

Systematic re-evaluation of the long-used standard protocol of urease-dependent metabolome sample preparation

In the urinary metabolomics for finding biomarkers in urine, owing to high concentrations of urea, for chromatography-based metabolomic analysis, urea needed to be degraded by urease. This urease pretreatment has been the key step of sample preparation for standard urinary metabolomics until today even for mass spectrometry-based analysis. The urease pretreatment involving incubation of urine with urease contradicts the concept of metabolome sampling, which should immediately arrest metabolic reactions to prevent alterations of a metabolite profile. Nonetheless, the impact of urease pretreatment has not been clearly elucidated yet. We found that activities of urease and endogenous urinary enzymes and metabolite contaminants from the urease preparations introduce artefacts into metabolite profiles, thus leading to misinterpretation.

Targeting citrate as a novel therapeutic strategy in cancer treatment

An important feature shared by many cancer cells is drastically altered metabolism that is critical for rapid growth and proliferation. The distinctly reprogrammed metabolism in cancer cells makes it possible to manipulate the levels of metabolites for cancer treatment. Citrate is a key metabolite that bridges many important metabolic pathways. Recent studies indicate that manipulating the level of citrate can impact the behaviors of both cancer and immune cells, resulting in induction of cancer cell apoptosis, boosting immune responses, and enhanced cancer immunotherapy. In this review, we discuss the recent developments in this emerging area of targeting citrate in cancer treatment. Specifically, we summarize the molecular basis of altered citrate metabolism in both tumors and immune cells, explore the seemingly conflicted growth promoting and growth inhibiting roles of citrate in various tumors, discuss the use of citrate in the clinic as a novel biomarker for cancer progression and outcomes, and highlight the new development of combining citrate with other therapeutic strategies in cancer therapy. An improved understanding of complex roles of citrate in the suppressive tumor microenvironment should open new avenues for cancer therapy.

Highly Sensitive Detection of Bladder Cancer-Related miRNA in Urine Using Time-Gated Luminescent Biochip

Detection of biomarkers in complex samples is a significant health plan strategy for medical diagnosis, therapy monitoring, and health management. However, high background noise resulting from impurities and other analytes in complex samples has hampered the improvement of detection sensitivity and accuracy. Herein, an ultralow background biochip based on time-gated luminescent probes supported by photonic crystals (PCs) was successfully developed for detection of bladder cancer (BC)-related miRNA biomarkers with high sensitivity and specificity in urine samples. Coupled with the time-gated luminescence of long-lifetime luminescence probes and the luminescence-enhanced capability of PCs, the short-lived autofluorescence can be efficiently removed; thus, the detection sensitivity will be significantly improved. Benefiting from these merits, a detection limit of 26.3 fM is achieved. Furthermore, the biochip exhibits excellent performance in urinary miRNA detection, and good recoveries are also obtained. The developed biochip possesses unique properties of ultralow background and luminescence enhancement, thus offering a suitable tool for the detection of BC-related miRNA in urine. With rational design of probe sequences, the biochip holds great potential for many other biomarkers in real patient samples, making it valuable in areas such as medical diagnosis and disease evaluation.

Metabolomics Analysis in Serum from Patients with Colorectal Polyp and Colorectal Cancer by 1H-NMR Spectrometry

Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide. Colorectal adenomatous polyps are at high risk for the development of CRC. In this report, we described the metabolic changes in the sera from patients with colorectal polyps and CRC by using the NMR-based metabolomics. 110 serum samples were collected from patients and healthy controls, including 40 CRC patients, 32 colorectal polyp patients, and 38 healthy controls. The metabolic profiles and differential metabolites of sera were analyzed by multivariate statistical analysis (MSA), including principal component analysis (PCA), partial least squares discriminant analysis (PLS-DA), and orthogonal partial least squares discriminant analysis (OPLS-DA) methods. A total of 23 differential metabolites were identified from MSA. According to the pathway analysis and multivariate ROC curve-based exploratory analysis by using the relative concentrations of differential metabolites, we found abnormal metabolic pathways and potential biomarkers involved with the colorectal polyp and CRC. The results showed that the pyruvate metabolism and glycerolipid metabolism were activated in colorectal polyps. And the glycolysis and glycine, serine, and threonine metabolism were activated in CRC. The changed metabolism may promote cellular proliferation. In addition, we found that the rates of acetate/glycerol and lactate/citrate could be the potential biomarkers in colorectal polyp and CRC, respectively. The application of ¹H-NMR metabolomics analysis in serum has interesting potential as a new detection and diagnostic tool for early diagnosis of CRC.

Recent advances in the metabolomic study of bladder cancer

INTRODUCTION

Metabolomics is a chemical process, involving the characterization of metabolites and cellular metabolism. Recent studies indicate that numerous metabolic pathways are altered in bladder cancer (BLCA), providing potential targets for improved detection and possible therapeutic intervention. We review recent advances in metabolomics related to BLCA and identify various metabolites that may serve as potential biomarkers for BLCA. Areas covered: In this review, we describe the latest advances in defining the BLCA metabolome and discuss the possible clinical utility of metabolic alterations in BLCA tissues, serum, and urine. In addition, we focus on the metabolic alterations associated with tobacco smoke and racial disparity in BLCA. Expert commentary: Metabolomics is a powerful tool which can shed new light on BLCA development and behavior. Key metabolites may serve as possible markers of BLCA. However, prospective validation will be needed to incorporate these markers into clinical care.

GC-MS Metabolomics Reveals Distinct Profiles of Low- and High-Grade Bladder Cancer Cultured Cells

Previous studies have shown that metabolomics can be a useful tool to better understand the mechanisms of carcinogenesis; however, alterations in biochemical pathways that lead to bladder cancer (BC) development have hitherto not been fully investigated. In this study, gas chromatography-mass spectrometry (GC-MS)-based metabolomics was applied to unveil the metabolic alterations between low-grade and high-grade BC cultured cell lines. Multivariable analysis revealed a panel of metabolites responsible for the separation between the two tumorigenic cell lines. Significantly lower levels of fatty acids, including myristic, palmitic, and palmitoleic acids, were found in high-grade versus low-grade BC cells. Furthermore, significantly altered levels of some amino acids were observed between low- and high-grade BC, namely glycine, leucine, methionine, valine, and aspartic acid. This study successfully demonstrated the potential of metabolomic analysis to discriminate BC cells according to tumor aggressiveness. Moreover, these findings suggest that bladder tumorigenic cell lines of different grades disclose distinct metabolic profiles, mainly affecting fatty acid biosynthesis and amino acid metabolism to compensate for higher energetic needs.

Large-scale profiling of serum metabolites in African American and European American patients with bladder cancer reveals metabolic pathways associated with patient survival

BACKGROUND

African Americans (AAs) experience a disproportionally high rate of bladder cancer (BLCA) deaths even though their incidence rates are lower than those of other patient groups. Using a metabolomics approach, this study investigated how AA BLCA may differ molecularly from European Americans (EAs) BLCA, and it examined serum samples from patients with BLCA with the aim of identifying druggable metabolic pathways in AA patients.

METHODS

Targeted metabolomics was applied to measure more than 300 metabolites in serum samples from 2 independent cohorts of EA and AA patients with BLCA and healthy EA and AA controls via liquid chromatography-mass spectrometry, and this was followed by the identification of altered metabolic pathways with a focus on AA BLCA. A subset of the differential metabolites was validated via absolute quantification with the Biocrates AbsoluteIDQ p180 kit. The clinical significance of the findings was further examined in The Cancer Genomic Atlas BLCA data set.

RESULTS

Fifty-three metabolites, mainly related to amino acid, lipid, and nucleotide metabolism, were identified that showed significant differences in abundance between AA and EA BLCA. For example, the levels of taurine, glutamine, glutamate, aspartate, and serine were elevated in serum samples from AA patients versus EA patients. By mapping these metabolites to genes, this study identified significant relations with regulators of metabolism such as malic enzyme 3, prolyl 3-hydroxylase 2, and lysine demethylase 2A that predicted patient survival exclusively in AA patients with BLCA.

CONCLUSIONS

This metabolic profile of serum samples might be used to assess risk progression in AA BLCA. These first-in-field findings describe metabolic alterations in AA BLCA and emphasize a potential biological basis for BLCA health disparities.

Nuclear magnetic resonance spectroscopy as a new approach for improvement of early diagnosis and risk stratification of prostate cancer

Prostate cancer (PCa) is the second most common male cancer worldwide and the fifth leading cause of death from cancer in men. Early detection and risk stratification is the most effective way to improve the survival of PCa patients. Current PCa biomarkers lack sufficient sensitivity and specificity to cancer. Metabolite biomarkers are evolving as a new diagnostic tool. This review is aimed to evaluate the potential of metabolite biomarkers for early detection, risk assessment, and monitoring of PCa. Of the 154 identified publications, 27 and 38 were original papers on urine and serum metabolomics, respectively. Nuclear magnetic resonance (NMR) is a promising method for measuring concentrations of metabolites in complex samples with good reproducibility, high sensitivity, and simple sample processing. Especially urine-based NMR metabolomics has the potential to be a cost-efficient method for the early detection of PCa, risk stratification, and monitoring treatment efficacy.

Volatile metabolomic signature of bladder cancer cell lines based on gas chromatography-mass spectrometry

INTRODUCTION

Recent studies provide a convincing support that the presence of cancer cells in the body leads to the alteration of volatile organic compounds (VOCs) emanating from biological samples, particularly of those closely related with tumoral tissues. Thus, a great interest emerged for the study of cancer volatilome and subsequent attempts to confirm VOCs as potential diagnostic biomarkers.

OBJECTIVES

The aim of this study was to determine the volatile metabolomic signature of bladder cancer (BC) cell lines and provide an in vitro proof-of-principle that VOCs emanated into the extracellular medium may discriminate BC cells from normal bladder epithelial cells.

METHODS

VOCs in the culture media of three BC cell lines (Scaber, J82, 5637) and one normal bladder cell line (SV-HUC-1) were extracted by headspace-solid phase microextraction and analysed by gas chromatography-mass spectrometry (HS-SPME/GC-MS). Two different pH (pH 2 and 7) were used for VOCs extraction to infer the best pH to be used in in vitro metabolomic studies.

RESULTS

Multivariate analysis revealed a panel of volatile metabolites that discriminated cancerous from normal bladder cells, at both pHs, although a higher number of discriminative VOCs was obtained at neutral pH. Most of the altered metabolites were ketones and alkanes, which were generally increased in BC compared to normal cells, and alcohols, which were significantly decreased in BC cells. Among them, three metabolites, namely 2-pentadecanone, dodecanal and γ-dodecalactone (the latter only tentatively identified), stood out as particularly important metabolites and promising volatile biomarkers for BC detection. Furthermore, our results also showed the potential of VOCs in discriminating BC cell lines according to tumour grade and histological subtype.

CONCLUSIONS

We demonstrate that a GC-MS metabolomics-based approach for analysis of VOCs is a valuable strategy for identifying new and specific biomarkers that may improve BC diagnosis. Future studies should entail the validation of volatile signature found for BC cell lines in biofluids from BC patients.

A characteristic biosignature for discrimination of gastric cancer from healthy population by high throughput GC-MS analysis

Early diagnosis of gastric cancer is crucial to improve patient′ outcome. A good biomarker will function in early diagnosis for gastric cancer. In order to find practical and cost-effective biomarkers, we used gas chromatography combined mass spectrometer (GC-MS) to profile urinary metabolites on 293 urine samples. Ninety-four samples are taken as training set, others for validating study. Orthogonal partial least squares discriminant analysis (OPLS-DA), significance analysis of microarray (SAM) and Mann-Whitney U test are used for data analysis. The diagnostic value of urinary metabolites was evaluated by ROC curve. As results, Seventeen metabolites are significantly different between patients and healthy controls in training set. Among them, 14 metabolites show diagnostic value better than classic blood biomarkers by quantitative assay on validation set. Ten of them are amino acids and four are organic metabolites. Importantly, proline, p-cresol and 4-hydroxybenzoic acid disclose outcome-prediction value by means of survival analysis. Therefore, the examination of urinary metabolites is a promising noninvasive strategy for gastric cancer screening.

Unmasking molecular profiles of bladder cancer

Precision medicine is designed to tailor treatments for individual patients by factoring in each person's specific biology and mechanism of disease. This paradigm shifted from a "one size fits all" approach to "personalized and precision care" requires multiple layers of molecular profiling of biomarkers for accurate diagnosis and prediction of treatment responses. Intensive studies are also being performed to understand the complex and dynamic molecular profiles of bladder cancer. These efforts involve looking bladder cancer mechanism at the multiple levels of the genome, epigenome, transcriptome, proteome, lipidome, metabolome etc. The aim of this short review is to outline the current technologies being used to investigate molecular profiles and discuss biomarker candidates that have been investigated as possible diagnostic and prognostic indicators of bladder cancer.

TRAF6 expression is associated with poorer prognosis and high recurrence in urothelial bladder cancer

The aim of the present study was to investigate the clinical significance of TNF receptor-associated factor 6 (TRAF6) expression in urothelial bladder cancer. TRAF6 expression was detected by immunohistochemistry in 126 samples of patients with urothelial bladder cancer. The association between clinicopathological factors and TRAF6 expression was analyzed by χ² test. The association between TRAF6 expression, overall survival rate and the recurrence-free survival rate was evaluated in univariate analysis with Kaplan-Meier test and in multivariate analysis with Cox-regression model. In the cohort tested, the rate of high TRAF6 expression was 61.9% (78/126). TRAF6 expression was demonstrated to be significantly associated with positive metastasis (P=0.001) with χ² test. Furthermore, TRAF6 expression was demonstrated to be associated with overall survival rate (P=0.016) and recurrence-free survival rate (P=0.016). With Cox-regression model, it was indicate that TRAF6 high expression was an independent predictive factor of poor prognosis (P=0.037) and high recurrence (P=0.011). High TRAF6 expression may predict unfavorable prognosis and high recurrence in urothelial bladder cancer, indicating that TRAF6 may be a potential and promising therapeutic target in urothelial bladder cancer.

Renal cell carcinoma: a critical analysis of metabolomic biomarkers emerging from current model systems

Metabolomics, an emerging field of "omics" sciences, has caught wide scientific attention in the area of biomarker research for cancers in which early diagnostic biomarkers have the potential to greatly improve patient outcome, such as renal cell carcinoma (RCC). Metabolomic approaches have been successfully applied to various human RCC model systems, mostly ex vivo neoplastic renal tissues and biofluids (urine and serum) from patients with RCC. Importantly, in contrast to other cancers, only a few studies have addressed the RCC metabolome using cancer cell culture-based in vitro models. Herein, we first carried out a comprehensive review of current metabolomic data in RCC, with emphasis on metabolite disturbances and dysregulated metabolic pathways identified in each of these experimental models. We then critically analyzed the consistency of evidence in this field and whether metabolites found altered in tumor cell and tissue microenvironment are reflected in biofluids, which constitute the rationale underlying the translation of discovered metabolic biomarkers into noninvasive diagnostic tools. Finally, dominant metabolic pathways and promising metabolites as biomarkers for diagnosis and prognosis of RCC are outlined.

Metabolomic Biomarkers in Urine of Cushing's Syndrome Patients

Cushing’s syndrome (CS) is a disease which results from excessive levels of cortisol in the human body. The disorder is associated with various signs and symptoms which are also common for the general population not suffering from compound hypersecretion. Thus, more sensitive and selective methods are required for the diagnosis of CS. This follow-up study was conducted to determine which steroid metabolites could serve as potential indicators of CS and possible subclinical hypercortisolism in patients diagnosed with so called non-functioning adrenal incidentalomas (AIs). Urine samples from negative controls (n = 37), patients with CS characterized by hypercortisolism and excluding iatrogenic CS (n = 16), and patients with non-functioning AIs with possible subclinical Cushing’s syndrome (n = 25) were analyzed using gas chromatography-mass spectrometry (GC/MS) and gas chromatograph equipped with flame ionization detector (GC/FID). Statistical and multivariate methods were applied to investigate the profile differences between examined individuals. The analyses revealed hormonal differences between patients with CS and the rest of examined individuals. The concentrations of selected metabolites of cortisol, androgens, and pregnenetriol were elevated whereas the levels of tetrahydrocortisone were decreased for CS when opposed to the rest of the study population. Moreover, after analysis of potential confounding factors, it was also possible to distinguish six steroid hormones which discriminated CS patients from other study subjects. The obtained discriminant functions enabled classification of CS patients and AI group characterized by mild hypersecretion of cortisol metabolites. It can be concluded that steroid hormones selected by applying urinary profiling may serve the role of potential biomarkers of CS and can aid in its early diagnosis.

The development of plasma pseudotargeted GC-MS metabolic profiling and its application in bladder cancer

Bladder cancer (BC) is a fatal malignancy with considerable mortality. BC urinary metabolomics has been extensively investigated for biomarker discovery, but few BC blood metabolomic studies have been performed. Hence, a plasma pseudotargeted metabolomic method based on gas chromatography-mass spectrometry with selected ion monitoring (GC-MS-SIM) was developed to study metabolic alterations in BC. The analytical performance of the developed method was compared with that of a nontargeted method. The relative standard deviation (RSD) values of 89 and 70.7 % of the peaks obtained using the pseudotargeted and nontargeted methods, respectively, were less than 20 %. The Pearson correlations of 90.7 and 78.3 % of the peaks obtained using the pseudotargeted and nontargeted methods, respectively, exceeded 0.90 in the linearity evaluation. Compared with the nontargeted method, the signal-to-noise ratios (S/N) of 97.9 and 69.3 % of the peaks increased two- and fivefold, respectively. The developed method was fully validated, with good precision, recovery, and stability of the trimethylsilyl (TMS) derivatives. The method was applied to investigate BC. Significant increases in the contents of metabolites involved in, for example, the pentose phosphate pathway (PPP) and nucleotide and fatty acid synthesis were found in the high-grade (HG) BC group compared to the healthy control (HC) group. These differences imply that the activated PPP may regulate BC cell proliferation by promoting lipid and nucleotide biosynthesis and the detoxification of reactive oxygen species (ROS). These results illustrate that the plasma pseudotargeted method is a powerful tool for metabolic profiling. Graphical abstract The plasma pseudotargeted metabolic profiling suggested the metabolic alterations in bladder cancer (BC) and the significantly differential metabolites for BC discrimination.

Tumor growth affects the metabonomic phenotypes of multiple mouse non-involved organs in an A549 lung cancer xenograft model

The effects of tumorigenesis and tumor growth on the non-involved organs remain poorly understood although many research efforts have already been made for understanding the metabolic phenotypes of various tumors. To better the situation, we systematically analyzed the metabolic phenotypes of multiple non-involved mouse organ tissues (heart, liver, spleen, lung and kidney) in an A549 lung cancer xenograft model at two different tumor-growth stages using the NMR-based metabonomics approaches. We found that tumor growth caused significant metabonomic changes in multiple non-involved organ tissues involving numerous metabolic pathways, including glycolysis, TCA cycle and metabolisms of amino acids, fatty acids, choline and nucleic acids. Amongst these, the common effects are enhanced glycolysis and nucleoside/nucleotide metabolisms. These findings provided essential biochemistry information about the effects of tumor growth on the non-involved organs.

Discovery of urine biomarkers for bladder cancer via global metabolomics

Bladder cancer (BC) is latent in its early stage and lethal in its late stage. Therefore, early diagnosis and intervention are essential for successful BC treatment. Considering the limitations of current diagnostic tools, noninvasive biomarkers that are both highly sensitive and specific are needed to improve the overall survival and quality of life of patients. With the advent of systems biology, "-omics" technologies have been developed over the past few decades. As a promising member, global metabolomics has increasingly been found to have clear potential for biomarker discovery. However, urinary metabolomics studies related to BC have lagged behind those of other urinary cancers, and major findings have not been systematically reported. The objective of this review is to comprehensively list the currently identified potential urinary metabolite biomarkers for BC.

Advantages and Pitfalls of Mass Spectrometry Based Metabolome Profiling in Systems Biology

Mass spectrometry-based metabolome profiling became the method of choice in systems biology approaches and aims to enhance biological understanding of complex biological systems. Genomics, transcriptomics, and proteomics are well established technologies and are commonly used by many scientists. In comparison, metabolomics is an emerging field and has not reached such high-throughput, routine and coverage than other omics technologies. Nevertheless, substantial improvements were achieved during the last years. Integrated data derived from multi-omics approaches will provide a deeper understanding of entire biological systems. Metabolome profiling is mainly hampered by its diversity, variation of metabolite concentration by several orders of magnitude and biological data interpretation. Thus, multiple approaches are required to cover most of the metabolites. No software tool is capable of comprehensively translating all the data into a biologically meaningful context yet. In this review, we discuss the advantages of metabolome profiling and main obstacles limiting progress in systems biology.

Distinct Metabolic Signature of Human Bladder Cancer Cells Carrying an Impaired Fanconi Anemia Tumor-Suppressor Signaling Pathway

Metabolic profiling has great potential to help the diagnosis and prognosis of cancer patients. Fanconi Anemia (FA) tumor-suppressor signaling has been instrumental in understanding human tumorigenesis. However, this instrumental understanding has never been demonstrated at the metabolic level. Here, we show that impaired FA signaling can lead cells to exhibit metabolic signatures of tumorigenesis. This is consistent with our original studies of the roles of FA signaling in suppressing non-FA tumorigenesis at functional and genetic levels. Using ultraperformance liquid chromatography-mass spectroscopy and gas chromatography-mass spectrometry, we characterized metabolic alterations in bladder cancer cells carrying an intact or impaired FA pathway. The latter was obtained by ectopically expressing FAVL (FAVL-high), which we previously found to be capable of inactivating FA signaling. A total of 18 metabolites, end products of cell proliferation or apoptosis, were significantly different between FAVL-high and -low cells. Methionine, phenylalanine, and threonine, resulting from a tumorigenic process, were substantially increased in FAVL-high cells. With this study, we achieved genomic, functional, and metabolomic characterization of the roles of FA signaling in the development of human cancer. Furthermore, this study provides novel insights into how to translate FA basic research into strategies for producing effective biomarkers in human cancer diagnosis and prognosis.

Biomarkers in bladder cancer: A metabolomic approach using in vitro and ex vivo model systems

Metabolomics has recently proved to be useful in the area of biomarker discovery for cancers in which early diagnostic and prognostic biomarkers are urgently needed, as is the case of bladder cancer (BC). This article presents a comprehensive review of the literature on the metabolomic studies on BC, highlighting metabolic pathways perturbed in this disease and the altered metabolites as potential biomarkers for BC detection. Current disease model systems used in the study of BC metabolome include in vitro-cultured cancer cells, ex vivo neoplastic bladder tissues and biological fluids, mainly urine but also blood serum/plasma, from BC patients. The major advantages and drawbacks of each model system are discussed. Based on available data, it seems that BC metabolic signature is mainly characterized by alterations in metabolites related to energy metabolic pathways, particularly glycolysis, amino acid and fatty acid metabolism, known to be crucial for cell proliferation, as well as glutathione metabolism, known to be determinant in maintaining cellular redox balance. In addition, purine and pyrimidine metabolism as well as carnitine species were found to be altered in BC. Finally, it is emphasized that, despite the progress made in respect to novel biomarkers for BC diagnosis, there are still some challenges and limitations that should be addressed in future metabolomic studies to ensure their translatability to clinical practice.

Tissue Metabonomic Phenotyping for Diagnosis and Prognosis of Human Colorectal Cancer

Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide and prognosis based on the conventional histological grading method for CRC remains poor. To better the situation, we analyzed the metabonomic signatures of 50 human CRC tissues and their adjacent non-involved tissues (ANIT) using high-resolution magic-angle spinning (HRMAS) (1)H NMR spectroscopy together with the fatty acid compositions of these tissues using GC-FID/MS. We showed that tissue metabolic phenotypes not only discriminated CRC tissues from ANIT, but also distinguished low-grade tumor tissues (stages I-II) from the high-grade ones (stages III-IV) with high sensitivity and specificity in both cases. Metabonomic phenotypes of CRC tissues differed significantly from that of ANIT in energy metabolism, membrane biosynthesis and degradations, osmotic regulations together with the metabolism of proteins and nucleotides. Amongst all CRC tissues, the stage I tumors exhibited largest differentiations from ANIT. The combination of the differentiating metabolites showed outstanding collective power for differentiating cancer from ANIT and for distinguishing CRC tissues at different stages. These findings revealed details in the typical metabonomic phenotypes associated with CRC tissues nondestructively and demonstrated tissue metabonomic phenotyping as an important molecular pathology tool for diagnosis and prognosis of cancerous solid tumors.

A Simultaneous Metabolic Profiling and Quantitative Multimetabolite Metabolomic Method for Human Plasma Using Gas-Chromatography Tandem Mass Spectrometry

For the first time it is possible to simultaneously collect targeted and nontargeted metabolomics data from plasma based on GC with high scan speed tandem mass spectrometry (GC-MS/MS). To address the challenge of getting broad metabolome coverage while quantifying known biomarker compounds in high-throughput GC-MS metabolomics, we developed a novel GC-MS/MS metabolomics method using a high scan speed (20 000 Da/second) GC-MS/MS that enables simultaneous data acquisition of both nontargeted full scan and targeted quantitative tandem mass spectrometry data. The combination of these two approaches has hitherto not been demonstrated in metabolomics. This method allows reproducible quantification of at least 37 metabolites using multiple reaction monitoring (MRM) and full mass spectral scan-based detection of 601 reproducible metabolic features from human plasma. The method showed good linearity over normal concentrations in plasma (0.06-343 to 0.86-4800 μM depending on the metabolite) and good intra- and interbatch precision (0.9-16.6 and 2.6-29.6% relative standard deviation). Based on the parameters determined for this method, targeted quantification using MRM can be expanded to cover at least 508 metabolites while still collecting full scan data. The new simultaneous targeted and nontargeted metabolomics method enables more sensitive and accurate detection of predetermined metabolites and biomarkers of interest, while still allowing detection and identification of unknown metabolites. This is the first validated GC-MS/MS metabolomics method with simultaneous full scan and MRM data collection, and clearly demonstrates the utility of GC-MS/MS with high scanning rates for complex analyses.

NMR metabolomics of renal cancer: an overview

This paper reviews the use of NMR metabolomics for the metabolic characterization of renal cancer. The existing challenges in the clinical management of this disease are first presented, followed by a brief introduction to the metabolomics approach, in the context of cancer research. A subsequent review of the literature on NMR metabolic studies of renal cancer reveals that the subject has been clearly underdeveloped, compared with other types of cancer, particularly regarding cultured cells and tissue analysis. NMR analysis of biofluids has focused on blood (plasma or serum) metabolomics, comprising no account of studies on human urine, in spite of its noninvasiveness and physiological proximity to the affected organs. Finally, some areas of potential future development are identified.