Recent advances in the metabolomic study of bladder cancer

A panel of four plasma amino acids is a promising biomarker for newly diagnosed bladder cancer

BACKGROUND

Metastasis and recurrence are the main causes of death in post-operative bladder cancer (BC), emphasizing the importance of exploring early-stage diagnostic markers. Serum biomarkers constitute a promising diagnostic approach for asymptomatic stage cancer as they are non-invasive, have high accuracy and low cost.

AIMS

To correlate concentrations of plasma amino acids with BC progression to assess their utility as an early-stage diagnostic.

METHODS

Newly diagnosed BC patients (n = 95) and normal controls (n = 96) were recruited during the period from 1 December 2018 to 30 December 2020. General and food frequency questionnaires established their basic information and dietary intake data. Venous blood samples were collected from fasting subjects and used to detect levels of plasma amino acids by liquid chromatography-mass spectrometry. Verification was performed on the GSE13507 transcriptome gene expression matrix of BC from Gene Expression Omnibus (GEO) database.

RESULTS

Eleven amino acids have been identified as altered in the plasma of newly diagnosed BC patients compared to controls (P < 0.05). Adjusted by gender, education, smoking and other factors, plasma ornithine level (OR = 0.256, 95% CI: 0.104-0.630) is a protective factor for BC, plasma levels of methionine (OR = 3.460, 95% CI: 1.384-8.651), arginine (OR = 3.851, 95% CI: 1.542-9.616), and glutamate (OR = 3.813, 95% CI: 1.543-9.419) are all risk factors for BC. ROC analysis demonstrated that the combination of plasma ornithine, methionine, arginine and glutamate could accurately diagnose BC (AUC = 0.84, 95% CI: 0.747-0.833). In addition, the mRNA level of arginase 1 was decreased (P < 0.05), while the inducible nitric oxide synthase was increased significantly, which may be linked with the disturbance of arginine metabolism in BC patients. Further analysis of GEO database confirmed the role of arginine metabolism.

CONCLUSION

A biomarker panel containing four amino acids may provide a feasible strategy for the early diagnosis of BC. However, further validation is required through prospective studies.

Inferring Bladder Cancer Evolution from Mucosal field Effects by Whole-Organ Spatial Mutational, Proteomic, and Metabolomic Mapping

Multi-platform mutational, proteomic, and metabolomic spatial mapping was used on the whole-organ scale to identify the molecular evolution of bladder cancer from mucosal field effects. We identified complex proteomic and metabolomic dysregulations in microscopically normal areas of bladder mucosa adjacent to dysplasia and carcinoma in situ. The mutational landscape developed in a background of complex defects of protein homeostasis which included dysregulated nucleocytoplasmic transport, splicesome, ribosome biogenesis, and peroxisome. These changes were combined with altered urothelial differentiation which involved lipid metabolism and protein degradations controlled by PPAR. The complex alterations of proteome were accompanied by dysregulation of gluco-lipid energy-related metabolism. The analysis of mutational landscape identified three types of mutations based on their geographic distribution and variant allele frequencies. The most common were low frequency α mutations restricted to individual mucosal samples. The two other groups of mutations were associated with clonal expansion. The first of this group referred to as β mutations occurred at low frequencies across the mucosa. The second of this group called γ mutations increased in frequency with disease progression. Modeling of the mutations revealed that carcinogenesis may span nearly 30 years and can be divided into dormant and progressive phases. The α mutations developed gradually in the dormant phase. The progressive phase lasted approximately five years and was signified by the advent of β mutations, but it was driven by γ mutations which developed during the last 2-3 years of disease progression to invasive cancer. Our study indicates that the understanding of complex alterations involving mucosal microenvironment initiating bladder carcinogenesis can be inferred from the multi-platform whole-organ mapping.

Exploiting volatile fingerprints for bladder cancer diagnosis: A scoping review of metabolomics and sensor-based approaches

Bladder cancer (BC) represents a significant global health concern, for which early detection is essential to improve patient outcomes. This review evaluates the potential of the urinary volatile organic compounds (VOCs) as biomarkers for detecting and staging BC. The methods used include gas chromatography-mass spectrometry (GC-MS)-based metabolomics and electronic-nose (e-nose) sensors. The GC-MS studies that have been published reveal diverse results in terms of diagnostic performance. The sensitivities range from 27 % to an impressive 97 %, while specificities vary between 43 % and 94 %. Furthermore, the accuracies reported in these studies range from 80 to 89 %. In the urine of BC patients, a total of 80 VOCs were discovered to be significantly altered when compared to controls. These VOCs encompassed a variety of chemical classes such as alcohols, aldehydes, alkanes, aromatic compounds, fatty acids, ketones, and terpenoids, among others. Conversely, e-nose-based studies displayed sensitivities from 60 to 100 %, specificities from 53 to 96 %, and accuracies from 65 to 97 %. Interestingly, conductive polymer-based sensors performed better, followed by metal oxide semiconductor and optical sensors. GC-MS studies have shown improved performance in detecting early stages and low-grade tumors, providing valuable insights into staging. Based on these findings, VOC-based diagnostic tools hold great promise for early BC detection and staging. Further studies are needed to validate biomarkers and their classification performance. In the future, advancements in VOC profiling technologies may significantly contribute to improving the overall survival and quality of life for BC patients.

Metabolic changes preceding bladder cancer occurrence among Korean men: a nested case-control study from the KCPS-II cohort

BACKGROUND

Bladder cancer (BLCA) research in Koreans is still lacking, especially in focusing on the prediction of BLCA. The current study aimed to discover metabolic signatures related to BLCA onset and confirm its potential as a biomarker.

METHODS

We designed two nested case-control studies using Korean Cancer Prevention Study (KCPS)-II. Only males aged 35-69 were randomly selected and divided into two sets by recruitment organizations [set 1, BLCA (n = 35) vs. control (n = 35); set 2, BLCA (n = 31) vs. control (n = 31)]. Baseline serum samples were analyzed by non-targeted metabolomics profiling, and OPLS-DA and network analysis were performed. Calculated genetic risk score (GRS) for BLCA from all KCPS participants was utilized for interpreting metabolomics data.

RESULTS

Critical metabolic signatures shown in the BLCA group were dysregulation of lysine metabolism and tryptophan-indole metabolism. Furthermore, the prediction model consisting of metabolites (lysine, tryptophan, indole, indoleacrylic acid, and indoleacetaldehyde) reflecting these metabolic signatures showed mighty BLCA predictive power (AUC: 0.959 [0.929-0.989]). The results of metabolic differences between GRS-high and GRS-low groups in BLCA indicated that the pathogenesis of BLCA is associated with a genetic predisposition. Besides, the predictive ability for BLCA on the model using GRS and five significant metabolites was powerful (AUC: 0.990 [0.980-1.000]).

CONCLUSION

Metabolic signatures shown in the present research may be closely associated with BLCA pathogenesis. Metabolites involved in these could be predictive biomarkers for BLCA. It could be utilized for early diagnosis, prognostic diagnosis, and therapeutic targets for BLCA.

Clinicopathological and prognostic significance of Ephrin A3 in bladder urothelial carcinoma

Ephrin A3 (EFNA3) is a member of the Eph/ephrin tyrosine kinase family, which is associated with multiple signaling pathways involved in cell growth and tumor cell metastasis. Aberrant regulation of EFNA3 is associated with the occurrence and development of various types of cancer. However, despite the high incidence of EFNA3 upregulation in cancer, studies concerning EFNA3 in urothelial carcinoma have not, to the best of our knowledge, been conducted. In the present study, bioinformatics analyses using data from multiple online databases were performed to confirm the upregulation of EFNA3 in bladder cancer. The co-expression gene set of EFNA3 and enriched signaling pathways were also analyzed. In addition, immunohistochemistry was conducted to detect EFNA3 expression in 491 clinically confirmed bladder urothelial carcinoma samples and 80 non-cancerous bladder tissues. Kaplan-Meier survival analysis, binary logistic regression analysis, and Cox regression analysis were conducted to confirm the validity of EFNA3 in predicting patient prognosis and its significance in clinical pathology. Statistical analysis demonstrated a significant association between EFNA3 expression levels with tumor size, lymph node metastasis, distant metastasis, and pathological grade. In conclusion, high EFNA3 expression may be a potential biomarker that indicates bladder tumor occurrence and patient prognosis.

Urinary microbiota and metabolic signatures associated with inorganic arsenic-induced early bladder lesions

Inorganic arsenic (iAs) contamination in drinking water is a global public health problem, and exposure to iAs is a known risk factor for bladder cancer. Perturbation of urinary microbiome and metabolome induced by iAs exposure may have a more direct effect on the development of bladder cancer. The aim of this study was to determine the impact of iAs exposure on urinary microbiome and metabolome, and to identify microbiota and metabolic signatures that are associated with iAs-induced bladder lesions. We evaluated and quantified the pathological changes of bladder, and performed 16S rDNA sequencing and mass spectrometry-based metabolomics profiling on urine samples from rats exposed to low (30 mg/L NaAsO₂) or high (100 mg/L NaAsO₂) iAs from early life (in utero and childhood) to puberty. Our results showed that iAs induced pathological bladder lesions, and more severe effects were noticed in the high-iAs group and male rats. Furthermore, six and seven featured urinary bacteria genera were identified in female and male offspring rats, respectively. Several characteristic urinary metabolites, including Menadione, Pilocarpine, N-Acetylornithine, Prostaglandin B1, Deoxyinosine, Biopterin, and 1-Methyluric acid, were identified significantly higher in the high-iAs groups. In addition, the correlation analysis demonstrated that the differential bacteria genera were highly correlated with the featured urinary metabolites. Collectively, these results suggest that exposure to iAs in early life not only causes bladder lesions, but also perturbs urinary microbiome composition and associated metabolic profiles, which shows a strong correlation. Those differential urinary genera and metabolites may contribute to bladder lesions, suggesting a potential for development of urinary biomarkers for iAs-induced bladder cancer.

Clinical value of anoikis-related genes and molecular subtypes identification in bladder urothelial carcinoma and in vitro validation

Background

Anoikis is a programmed cell death process that was proven to be associated with cancer. Uroepithelial carcinoma of the bladder (BLCA) is a malignant disease of the urinary tract and has a strong metastatic potential. To determine whether anoikis-associated genes can predict the prognosis of BLCA accurately, we evaluated the prognostic value of anoikis-associated genes in BLCA and constructed the best model to predict prognosis.

Method

The BLCA transcriptome data were downloaded from TCGA and GEO databases, and genes with differential expression were selected and then clustered using non-negative matrix factorization (NMF). The genes with the most correlation with anoikis were screened and identified using univariate Cox regression, lasso regression, and multivariate Cox regression. The GEO dataset was used for external validation. Nomograms were created based on risk characteristics in combination with clinical variants and the performance of the model was validated with receiver operating characteristic (ROC) curves. The immunotherapeutic significance of this risk score was assessed using the immune phenomenon score (IPS). IC50 values of predictive chemotherapeutic agents were calculated. Finally, we used RT-qPCR to determine the mRNA expression of four genes, CALR, FASN, CASP6, and RAD9A.

Result

We screened 406 tumor samples and 19 normal tissue samples from the TCGA database. Based on anoikis-associated genes, we classified patients into two subtypes (C1 and C2) using NMF method. Subsequently, nine core genes were screened by multiple methods after analysis, which were used to construct risk profiles. The design of nomograms based on risk profiles and clinical variables, ROC, and calibration curves confirmed that the model could well have the ability to predict the survival of BLCA patients at 1, 3, and 5 years. By predicting the IC50 values of chemotherapeutic drugs, it was learned that the high-risk group (HRG) was more susceptible to paclitaxel, gemcitabine, and cisplatin, and the low-risk group (LRG) was more susceptible to veriparib and afatinib.

Conclusion

In summary, the risk score of anoikis-associated genes can be applied as a predictor to predict the prognosis of BLCA in clinical practice.

Tumour microenvironment as a predictive factor for immunotherapy in non-muscle-invasive bladder cancer

Bladder cancer (BC) can be divided into two subgroups depending on invasion of the muscular layer: non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC). Its aggressiveness is associated, inter alia, with genetic aberrations like losses of 1p, 6q, 9p, 9q and 13q; gain of 5p; or alterations in the p53 and p16 pathways. Moreover, there are reported metabolic disturbances connected with poor diagnosis-for example, enhanced aerobic glycolysis, gluconeogenesis or haem catabolism.Currently, the primary way of treatment method is transurethral resection of the bladder tumour (TURBT) with adjuvant Bacillus Calmette-Guérin (BCG) therapy for NMIBC or radical cystectomy for MIBC combined with chemotherapy or immunotherapy. However, intravesical BCG immunotherapy and immune checkpoint inhibitors are not efficient in every case, so appropriate biomarkers are needed in order to select the proper treatment options. It seems that the success of immunotherapy depends mainly on the tumour microenvironment (TME), which reflects the molecular disturbances in the tumour. TME consists of specific conditions like hypoxia or local acidosis and different populations of immune cells including tumour-infiltrating lymphocytes, natural killer cells, neutrophils and B lymphocytes, which are responsible for shaping the response against tumour neoantigens and crucial pathways like the PD-L1/PD-1 axis.In this review, we summarise holistically the impact of the immune system, genetic alterations and metabolic changes that are key factors in immunotherapy success. These findings should enable better understanding of the TME complexity in case of NMIBC and causes of failures of current therapies.

Data Processing and Analysis in Liquid Chromatography-Mass Spectrometry-Based Targeted Metabolomics

Mass spectrometry (MS)-based metabolomics provides high-dimensional datasets; that is, the data include various metabolite features. Data analysis begins by converting the raw data obtained from the MS to produce a data matrix (metabolite × concentrations). This is followed by several steps, such as peak integration, alignment of multiple data, metabolite identification, and calculation of metabolite concentrations. Each step yields the analytical results and the accompanying information used for the quality assessment of the anterior steps. Thus, the measurement quality can be analyzed through data processing. Here, we introduce a typical data processing procedure and describe a method to utilize the intermediate data as quality control. Subsequently, commonly used data analysis methods for metabolomics data, such as statistical analyses, are also introduced.

A Comprehensive Metabolomics Analysis of Fecal Samples from Advanced Adenoma and Colorectal Cancer Patients

Accurate diagnosis of colorectal cancer (CRC) still relies on invasive colonoscopy. Noninvasive methods are less sensitive in detecting the disease, particularly in the early stage. In the current work, a metabolomics analysis of fecal samples was carried out by ultra-high-performance liquid chromatography–tandem mass spectroscopy (UPLC-MS/MS). A total of 1380 metabolites were analyzed in a cohort of 120 fecal samples from patients with normal colonoscopy, advanced adenoma (AA) and CRC. Multivariate analysis revealed that metabolic profiles of CRC and AA patients were similar and could be clearly separated from control individuals. Among the 25 significant metabolites, sphingomyelins (SM), lactosylceramides (LacCer), secondary bile acids, polypeptides, formiminoglutamate, heme and cytidine-containing pyrimidines were found to be dysregulated in CRC patients. Supervised random forest (RF) and logistic regression algorithms were employed to build a CRC accurate predicted model consisting of the combination of hemoglobin (Hgb) and bilirubin E,E, lactosyl-N-palmitoyl-sphingosine, glycocholenate sulfate and STLVT with an accuracy, sensitivity and specificity of 91.67% (95% Confidence Interval (CI) 0.7753–0.9825), 0.7 and 1, respectively.

Molecular Oncology of Bladder Cancer from Inception to Modern Perspective

Within the last forty years, seminal contributions have been made in the areas of bladder cancer (BC) biology, driver genes, molecular profiling, biomarkers, and therapeutic targets for improving personalized patient care. This overview includes seminal discoveries and advances in the molecular oncology of BC. Starting with the concept of divergent molecular pathways for the development of low- and high-grade bladder tumors, field cancerization versus clonality of bladder tumors, cancer driver genes/mutations, genetic polymorphisms, and bacillus Calmette-Guérin (BCG) as an early form of immunotherapy are some of the conceptual contributions towards improving patient care. Although beginning with a promise of predicting prognosis and individualizing treatments, "-omic" approaches and molecular subtypes have revealed the importance of BC stem cells, lineage plasticity, and intra-tumor heterogeneity as the next frontiers for realizing individualized patient care. Along with urine as the optimal non-invasive liquid biopsy, BC is at the forefront of the biomarker field. If the goal is to reduce the number of cystoscopies but not to replace them for monitoring recurrence and asymptomatic microscopic hematuria, a BC marker may reach clinical acceptance. As advances in the molecular oncology of BC continue, the next twenty-five years should significantly advance personalized care for BC patients.

Surface-Enhanced Raman Spectroscopy of Pretreated Plasma Samples Predicts Disease Recurrence in Muscle-Invasive Bladder Cancer Patients Undergoing Neoadjuvant Chemotherapy and Radical Cystectomy

Objective

To explore the value of surface-enhanced Raman spectroscopy analysis of pretreated plasma samples in prediction of bladder cancer (BCa) recurrence after neoadjuvant chemotherapy (NAC) and radical cystectomy (RC).

Patients and Methods

SERS was used to analyze plasma samples collected before biopsy and treatment in BCa patients undergoing NAC and RC. The value of clinicopathological parameters and distinctive SERS peaks in the prediction of disease recurrence were analyzed in Cox regression proportional hazard analysis and Log rank test. Principal component analysis and linear discriminant analysis (PCA-LDA) were employed to process spectral data and construct diagnostic algorithms.

Results

A total of 88 patients with 440 plasma SERS spectra were collected. The SRES spectra from recurrent patients were compared with patients who remained recurrence free. The SERS demonstrated higher levels of circulating free nucleic acid components in recurrent population, which is represented by significantly higher intensities at SERS peaks of 725 cm⁻¹, 1328 cm⁻¹ and 1455 cm⁻¹. The SERS also detected significantly lower levels of tryptophan shown as lower significantly intensities at the 1558 cm⁻¹, which is proved to be an independent predictor of BCa recurrence. The addition of SERS peaks of 1558 cm⁻¹ to classic clinicopathological predictors including pathological tumor stage, lymph node metastasis and pathological downstaging can significantly enhance the power of the predictive model from 0.66 to 0.76 in the area under curve (AUC) of receiver operating characteristic (ROC) curves. Meanwhile, the PCA-LDA diagnostic model based on SERS spectra reveals a high accuracy of 85.2% in prediction of disease recurrence and the AUC of 0.92 in the ROC curve. When validated in the leave-one-out cross-validation method, the accuracy of the model remained 84.1%.

Conclusion

We show that SERS analysis of plasma before NAC treatment can accurately classify patients with different risks of disease recurrence after surgery and improve the power of clinicopathological predictive models, thus refining clinical decision-making.

Pre- and Post-Resection Urine Metabolic Profiles of Bladder Cancer Patients: Results of Preliminary Studies on Time Series Metabolomics Analysis

Simple Summary

Bladder cancer is one of the most frequently diagnosed cancers worldwide and due to non-specific symptoms, it is often detected at a late stage. For this reason, possible diagnostic alternatives that could be used for non-invasive screening are still being sought. In recent years, metabolomics approach has been frequently used for this type of research, using urine or blood collected from two groups: patients with a given disease and healthy volunteers. Usually, to minimize the impact of between-subject differences, participants of the study are matched in terms of age, gender, or BMI. Another way to rule out the impact of this variability is to analyze samples taken at intervals from the same patient. Therefore, the aim of our study was to validate results obtained using the traditional approach on a small group of patients, from whom samples were taken before and after resection of the bladder tumor, in a given time frame.

Abstract

The incidence of bladder cancer (BCa) has remained high for many years. Nevertheless, its pathomechanism has not yet been fully understood and is still being studied. Therefore, multiplatform untargeted urinary metabolomics analysis has been performed in order to study differences in the metabolic profiles of urine samples collected at three time points: before transurethral resection of bladder tumor (TURBT), the day after the procedure and two weeks after TURBT. Collected samples were analyzed with the use of high-performance liquid chromatography hyphenated with time-of-flight mass spectrometry detection (HPLC-TOF/MS) and gas chromatography coupled with triple quadrupole mass spectrometry detection (GC-QqQ/MS, in a scan mode). Levels of metabolites selected in our previous study were assessed in order to confirm their potential to differentiate the healthy and diseased samples, regardless of the risk factors and individual characteristics. Hippuric acid, pentanedioic acid and uridine confirmed their potential for sample differentiation. Based on the results of statistical analysis for the paired samples (comparison of metabolic profiles of samples collected before TURBT and two weeks after), a set of metabolites belonging to nucleotide metabolism and methylation processes was also selected. Longitudinal studies proved to be useful for the evaluation of metabolic changes in bladder cancer.

Identifying the grade of bladder cancer cells using microfluidic chips based on impedance

Bladder cancer diagnosis is made by microfluidic chip based-on impedance analysis.

A Novel Prognostic Signature Based on Ferroptosis-Related Genes Predicts the Prognosis of Patients With Advanced Bladder Urothelial Carcinoma

Bladder Urothelial Carcinoma (BLCA) is the major subtype of bladder cancer, and the prognosis prediction of BLCA is difficult. Ferroptosis is a newly discovered iron-dependent cell death pathway. However, the clinical value of ferroptosis-related genes (FRGs) on the prediction of BLCA prognosis is still uncertain. In this study, we aimed to construct a novel prognostic signature to improve the prognosis prediction of advanced BLCA based on FRGs. In the TCGA cohort, we identified 23 differentially expressed genes (DEGs) associated with overall survival (OS) via univariate Cox analysis (all P < 0.05). 8 optimal DEGs were finally screened to generate the prognostic risk signature through LASSO regression analysis. Patients were divided into two risk groups based on the median risk score. Survival analyses revealed that the OS rate in the high-risk group was significantly lower than that in the low-risk group. Moreover, the risk score was determined as an independent predictor of OS by the multivariate Cox regression analysis (Hazard ratio > 1, 95% CI = 1.724-2.943, P < 0.05). Many potential ferroptosis-related pathways were identified in the enrichment analysis in BLCA. With the aid of an external FAHWMU cohort (n = 180), the clinical predication value of the signature was further verified. In conclusion, the prognosis of advanced BLCA could be accurately predicted by this novel FRG-signature.

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.

Current Development and Application of Anaerobic Glycolytic Enzymes in Urothelial Cancer

Urothelial cancer is a malignant tumor with metastatic ability and high mortality. Malignant tumors of the urinary system include upper tract urothelial cancer and bladder cancer. In addition to typical genetic alterations and epigenetic modifications, metabolism-related events also occur in urothelial cancer. This metabolic reprogramming includes aberrant expression levels of genes, metabolites, and associated networks and pathways. In this review, we summarize the dysfunctions of glycolytic enzymes in urothelial cancer and discuss the relevant phenotype and signal transduction. Moreover, we describe potential prognostic factors and risks to the survival of clinical cancer patients. More importantly, based on several available databases, we explore relationships between glycolytic enzymes and genetic changes or drug responses in urothelial cancer cells. Current advances in glycolysis-based inhibitors and their combinations are also discussed. Combining all of the evidence, we indicate their potential value for further research in basic science and clinical applications.

Multiple Chromatographic Analysis of Urine in the Detection of Bladder Cancer

Bladder cancer (BC) is the most common type of carcinoma of the urological system. Recently, there has been an increasing interest in non-invasive diagnostic tumor markers due to the invasive attribute of cystoscopy, which is still considered the gold standard diagnostic method. However, markers published in the literature so far do not meet expectations for replacing cystoscopy due to their low specificity and excessively high false-positive results, which can be mainly caused by frequently occurring hematuria also in benign cases. No reliable non-invasive method has yet been identified that can distinguish patients with bladder cancer and non-malignant hematuria patients. Our work examined the possibilities of non-targeted biomarkers of urine to distinguish patients with malignant and non-malignant diseases of the bladder using 3D HPLC in combination with computer processing of multiple datasets. Urine samples from 47 patients, 23 patients with bladder cancer (BC) and 24 patients with non-malignant hematuria (NMHU), were enrolled in clinical trials. For the separation and subsequent analysis of a large number of urine components, 3D HPLC (high-performance liquid chromatography) with an absorption and fluorescence detector was used. The obtained dataset was further subjected to various uni- and multi-dimensional statistical analyses and mathematical modeling. We found 334 chromatographic peaks, of which 18 peaks were identified as significantly different for BC and NMHU patients. Using receiver operating characteristic (ROC) analysis, we assessed the informative ability of significant chromatographic peaks (90% sensitivity and 74% specificity). By logistic regression, we identified the optimal and simplified set of seven chromatographic peaks (5 absorptions plus 2 fluorescence) with strong classification power (100% sensitivity and 100% specificity) for distinguishing patients with bladder cancer and those with non-malignant hematuria. Partial least square discriminant analysis (PLS-DA) model and orthogonal projection to latent structure discriminant analysis (OPLS-DA) with 100% sensitivity and 96% specificity were used to distinguish BC and NMHU patients. Multivariate statistical analysis of urinary metabolomic profiles of patients revealed that BC patients can be discriminated from NMHU patients and the results can likely contribute to an early and non-invasive diagnosis of BC.

Detecting urine metabolites of bladder cancer by surface-enhanced Raman spectroscopy

AIM

Metabolites present in urine reflect the current phenotype of the cancer state. Surface-enhanced Raman spectroscopy (SERS) can be used in urine supernatant or sediment to largely reflect the metabolic status of the body.

MATERIALS & METHODS

SERS was performed to detect bladder cancer (BCa) and predict tumour grade from urine supernatant, which contains various system metabolites, as well as from urine sediment, which contains exfoliated tumour cells.

RESULTS & DISCUSSION

Upon combining the urinary supernatant and sediment results, the total diagnostic sensitivity and specificity of SERS were 100% and 98.85%, respectively, for high-grade tumours and 97.53% and 90.80%, respectively, for low-grade tumours.

CONCLUSION

The present results suggest high potential for SERS to detect BCa from urine, especially when combining both urinary supernatant and sediment results.

Salivary metabolomics for cancer detection

Introduction: Saliva is an ideal biofluid that can be collected in a noninvasive manner, enabling safe and frequent screening of various diseases. Recent studies have revealed that salivary metabolomics analysis has the potential to detect both oral and systemic cancers. Area covered: We reviewed the technical aspects, as well as applications, of salivary metabolomics for cancer detection. The topics include the effects of preconditioning and the method of sample collection, sample storage, processing, measurement, data analysis, and validation of the results. We also examined the rational relationship between salivary biomarkers and tumors distant from the oral cavity. A strategy to establish standard operating protocols for obtaining reproducible quantification data is also discussed Expert opinion: Salivary metabolomics reflects oral and systematic health status, which potently enables cancer detection. The sensitivity and specificity of each marker and their combinations have been well evaluated, but a validation study is required. Further, the standard operating protocol for each procedure should be established to obtain reproducible data before clinical usage.

Label-free detection of multiple genitourinary cancers from urine by surface-enhanced Raman spectroscopy

Detecting cancers through testing biological fluids, namely, "liquid biopsy", is noninvasive and shows great promise in cancer diagnosis, surveillance and screening. Many metabolites that may reflect cancer specificity are concentrated in and excreted through urine. In this study, urine samples were collected from healthy subjects and patients with bladder or prostate cancer. By using surface-enhanced Raman spectroscopy (SERS) with silver nanoparticles, urine sample spectra from 500-1800 cm^-1 were obtained. The spectra were classified by principal component analysis and linear discriminant analysis (PCA-LDA). The results showed that the classification accuracy of the model for healthy individuals, bladder cancer patients and prostate cancer patients was 91.9%, and the classification accuracy of the test set was 89%, which indicated that SERS combined with the PCA-LDA diagnostic algorithm could be used as a classification and diagnostic tool to detect and distinguish bladder cancer and prostate cancer through testing urine.

Circulating proteomic signature for detection of biomarkers in bladder cancer patients

The identification of clinically-relevant early diagnostic and prognostic protein biomarkers is essential to maximize therapeutic efficacy and prevent cancer progression. The aim of the current study is to determine whether aberrant plasma protein profile can be applied as a surrogate tool for early diagnosis of bladder carcinoma. Plasma samples from patients with low grade non-muscle invasive bladder cancer and healthy controls were analyzed using combined 2D-DIGE and mass-spectrometry to identify differentially expressed proteins. Validation was performed using western blotting analysis in an independent cohort of cancer patients and controls. Fifteen differentially-expressed proteins were identified of which 12 were significantly up-regulated and three were significantly down-regulated in tumors compared to controls. The Ingenuity Pathways Analysis revealed functional connection between the differentially-expressed proteins and immunological disease, inflammatory disease and cancer mediated through chemokine and cytokine signaling pathway and NF-kB transcription factor. Among the three validated proteins, haptoglobin was able to distinguish between patients with low grade bladder cancer and the controls with high sensitivity and specificity (AUC > 0.87). In conclusion, several biomarker proteins were identified in bladder cancer. Haptoglobin is a potential candidate that merit further investigation to validate its usefulness and functional significance as potential biomarkers for early detection of bladder cancer.

UPLC-MS based urine untargeted metabolomic analyses to differentiate bladder cancer from renal cell carcinoma

Background

To discover biomarker panels that could distinguish cancers (BC and RCC) from healthy controls (HCs) and bladder cancers (BC) from renal cell carcinoma (RCC), regardless of whether the patients have haematuria. In addition, we also explored the altered metabolomic pathways of BC and RCC.

Methods

In total, 403 participants were enrolled in our study, which included 146 BC patients (77 without haematuria and 69 with haematuria), 115 RCC patients (94 without haematuria and 21 with haematuria) and 142 sex- and age-matched HCs. Their midstream urine samples were collected and analysed by performing UPLC-MS. The statistical methods and pathway analyses were applied to discover potential biomarker panels and altered metabolic pathways.

Results

The panel of α-CEHC, β-cortolone, deoxyinosine, flunisolide, 11b,17a,21-trihydroxypreg-nenolone and glycerol tripropanoate could distinguish the patients with cancer from the HCs (the AUC was 0.950) and the external validation also displayed a good predictive ability (the AUC was 0.867). The panel of 4-ethoxymethylphenol, prostaglandin F2b, thromboxane B3, hydroxybutyrylcarnitine, 3-hydroxyphloretin and N′-formylkynurenine could differentiate BC from RCC without haematuria. The AUC was 0.829 in the discovering group and 0.76 in the external validation. The metabolite panel comprising 1-hydroxy-2-oxopropyl tetrahydropterin, 1-acetoxy-2-hydroxy-16-heptadecyn-4-one, 1,2-dehydrosalsolinol and L-tyrosine could significantly discriminate BC from RCC with haematuria (AUC was 0.913). Pathway analyses revealed altered lipid and purine metabolisms between cancer patients and HCs, together with disordered amino acid and purine metabolisms between BC and RCC with haematuria.

Conclusions

UPLC-MS urine metabolomic analyses could not only differentiate cancers from HCs but also discriminate BC from RCC. In addition, pathway analyses demonstrated a deeper metabolic mechanism of BC and RCC.

Improving diagnosis of genitourinary cancers: Biomarker discovery strategies through mass spectrometry-based metabolomics

The genitourinary oncology field needs integration of results from basic science, epidemiological studies, clinical and translational research to improve the current methods for diagnosis. MS-based metabolomics can be transformative for disease diagnosis and contribute to global health parity. Metabolite panels are promising to translate metabolomic findings into the clinics, changing the current diagnosis paradigm based on single biomarker analysis. This review article describes capabilities of the MS-based oncometabolomics field for improving kidney, prostate, and bladder cancer detection, early diagnosis, risk stratification, and outcome. Published works are critically discussed based on the study design; type and number of samples analyzed; data quality assessment through quality assurance and quality control practices; data analysis workflows; confidence levels reported for identified metabolites; validation attempts; the overlap of discriminant metabolites for the different genitourinary cancers; and the translation capability of findings into clinical settings. Ongoing challenges are discussed, and future directions are delineated.

Tobacco carcinogens and the methionine metabolism in human bladder cancer

Cigarette smoking is a strong risk factor for bladder cancer. It has been shown that the duration of smoking is associated with a poor prognosis and a higher risk of recurrence. This is due to tobacco carcinogens forming adducts with DNA and proteins that participate in the DNA repair mechanisms. Additionally, polymorphisms of genes responsible for methyl group transfer in the methionine cycle and dosages of vitamins (from diet and supplements) can cause an increased risk of bladder cancer. Upregulated DNA methyltransferase 1 expression and activity results in a high level of methylated products of metabolism, as well as hypermethylation of tumor suppressor genes. The development of a market that provides new inhibitors of DNA methyltransferase or alternatives for current smokers is essential not only for patients but also for people who are under the danger of secondhand smoking and can experience its long-term exposure consequences.