Urinary metabotyping of bladder cancer using two-dimensional gas chromatography time-of-flight mass spectrometry

Metabolomics for searching validated biomarkers in cancer studies: a decade in review

INTRODUCTION

In the dynamic landscape of modern healthcare, the ability to anticipate and diagnose diseases, particularly in cases where early treatment significantly impacts outcomes, is paramount. Cancer, a complex and heterogeneous disease, underscores the critical importance of early diagnosis for patient survival. The integration of metabolomics information has emerged as a crucial tool, complementing the genotype-phenotype landscape and providing insights into active metabolic mechanisms and disease-induced dysregulated pathways.

AREAS COVERED

This review explores a decade of developments in the search for biomarkers validated within the realm of cancer studies. By critically assessing a diverse array of research articles, clinical trials, and studies, this review aims to present an overview of the methodologies employed and the progress achieved in identifying and validating biomarkers in metabolomics results for various cancer types.

EXPERT OPINION

Through an exploration of more than 800 studies, this review has allowed to establish a general idea about state-of-art in the search of biomarkers in metabolomics studies involving cancer which include certain level of results validation. The potential for metabolites as diagnostic markers to reach the clinic and make a real difference in patient health is substantial, but challenges remain to be explored.

Insight into Fructose-to-Sucrose Ratio as the Potential Target of Urinalysis in Bladder Cancer

Bladder cancer usually has been diagnosed in elderly patients as it stays asymptomatic until it presents. Current detection methods for bladder cancer cannot be considered as an adequate screening strategy due to their high invasiveness and low sensitivity. However, there remains uncertainty about targets with high sensitivity and specificity for non-invasive bladder cancer examination. Our study aims to investigate the actionable non-invasive screening biomarkers in bladder cancer. Here, we employed scRNA-seq to explore the crucial biological processes for bladder cancer development. We then utilized bidirectional Mendelian randomization (MR) analysis to explore the bidirectional causal relationship between ATP-associated metabolites in urine and bladder cancer. Lastly, we used a BBN-induced mouse model of bladder cancer to validate the crucial gene identified by scRNA-seq and MR analysis. We found that (1) the ATP metabolism process plays a critical role in bladder cancer development; (2) there is a bidirectional and negative causal relationship between fructose-to-sucrose ratio in urine and the risk of bladder cancer; and (3) the higher expression of TPI1, a critical gene in the fructose metabolism pathway, was validated in BBN-induced bladder tumors. Our results reveal that fructose-to-sucrose ratio can serve as a potential target of urinalysis in bladder cancer.

Identifying potential breath biomarkers for early diagnosis of papillary thyroid cancer based on solid-phase microextraction gas chromatography-high resolution mass spectrometry with metabolomics

INTRODUCTION

Thyroid cancer incidence rate has increased substantially worldwide in recent years. Fine needle aspiration biopsy (FNAB) is currently the golden standard of thyroid cancer diagnosis, which however, is invasive and costly. In contrast, breath analysis is a non-invasive, safe and simple sampling method combined with a promising metabolomics approach, which is suitable for early cancer diagnosis in high volume population.

OBJECTIVES

This study aims to achieve a more comprehensive and definitive exhaled breath metabolism profile in papillary thyroid cancer patients (PTCs).

METHODS

We studied both end-tidal and mixed expiratory breath, solid-phase microextraction gas chromatography coupled with high resolution mass spectrometry (SPME-GC-HRMS) was used to analyze the breath samples. Multivariate combined univariate analysis was applied to identify potential breath biomarkers.

RESULTS

The biomarkers identified in end-tidal and mixed expiratory breath mainly included alkanes, olefins, enols, enones, esters, aromatic compounds, and fluorine and chlorine containing organic compounds. The area under the curve (AUC) values of combined biomarkers were 0.974 (sensitivity: 96.1%, specificity: 90.2%) and 0.909 (sensitivity: 98.0%, specificity: 74.5%), respectively, for the end-tidal and mixed expiratory breath, indicating of reliability of the sampling and analysis method CONCLUSION: This work not only successfully established a standard metabolomic approach for early diagnosis of PTC, but also revealed the necessity of using both the two breath types for comprehensive analysis of the biomarkers.

Metabolomics for the diagnosis of bladder cancer: A systematic review

Objective

Metabolomics has been extensively utilized in bladder cancer (BCa) research, employing mass spectrometry and nuclear magnetic resonance spectroscopy to compare various variables (tissues, serum, blood, and urine). This study aimed to identify potential biomarkers for early BCa diagnosis.

Methods

A search strategy was designed to identify clinical trials, descriptive and analytical observational studies from databases such as Medline, Embase, Cochrane Central Register of Controlled Trials, and Latin American and Caribbean Literature in Health Sciences. Inclusion criteria comprised studies involving BCa tissue, serum, blood, or urine profiling using widely adopted metabolomics techniques like mass spectrometry and nuclear magnetic resonance. Primary outcomes included description of metabolites and metabolomics profiling in BCa patients and the association of metabolites and metabolomics profiling with BCa diagnosis compared to control patients. The risk of bias was assessed using the Quality Assessment of Studies of Diagnostic Accuracy.

Results

The search strategy yielded 2832 studies, of which 30 case-control studies were included. Urine was predominantly used as the primary sample for metabolite identification. Risk of bias was often unclear inpatient selection, blinding of the index test, and reference standard assessment, but no applicability concerns were observed. Metabolites and metabolomics profiles associated with BCa diagnosis were identified in glucose, amino acids, nucleotides, lipids, and aldehydes metabolism.

Conclusion

The identified metabolites in urine included citric acid, valine, tryptophan, taurine, aspartic acid, uridine, ribose, phosphocholine, and carnitine. Tissue samples exhibited elevated levels of lactic acid, amino acids, and lipids. Consistent findings across tissue, urine, and serum samples revealed downregulation of citric acid and upregulation of lactic acid, valine, tryptophan, taurine, glutamine, aspartic acid, uridine, ribose, and phosphocholine.

Untargeted metabolomics of bladder tissue using liquid chromatography and quadrupole time-of-flight mass spectrometry for cancer biomarker detection

Bladder cancer (BC) ranks among the most common cancers globally, with an increasing occurrence, particularly in developed nations. Utilizing tissue metabolomics presents a promising strategy for identifying potential biomarkers for cancer detection. In this study, we utilized ultra-high-performance liquid chromatography coupled with ultra-high-resolution mass spectrometry (UHPLC-UHRMS), incorporating both C18-silica and HILIC columns, to comprehensively analyze both polar and non-polar metabolite profiles in tissue samples from 99 patients with bladder cancer. By utilizing an untargeted approach with external validation, we identified twenty-five tissue metabolites that hold promise as potential indicators of BC. Furthermore, twenty-five characteristic tissue metabolites that exhibit discriminatory potential across bladder cancer tumor grades, as well as thirty-nine metabolites that display correlations with tumor stages were presented. Receiver operating characteristics analysis demonstrated high predictive power for all types of metabolomics data, with area under the curve (AUC) values exceeding 0.966. Notably, this study represents the first report in which human bladder normal tissues adjacent to cancerous tissues were analyzed using UHPLC-UHRMS. These findings suggest that the metabolite markers identified in this investigation could serve as valuable tools for the detection and monitoring of bladder cancer stages and grades.

Liquid biopsy in urothelial carcinoma: Detection techniques and clinical applications

The types of urothelial carcinoma (UC) include urothelial bladder cancer and upper tract urothelial carcinoma. Current diagnostic techniques cannot meet the needs of patients. Liquid biopsy is an accurate method of determining the molecular profile of UC and is a cutting-edge and popular technique that is expected to complement existing detection techniques and benefit patients with UC. Circulating tumor cells, cell-free DNA, cell-free RNA, extracellular vesicles, proteins, and metabolites can be found in the blood, urine, or other bodily fluids and are examined during liquid biopsies. This article focuses on the components of liquid biopsies and their clinical applications in UC. Liquid biopsies have tremendous potential in multiple aspects of precision oncology, from early diagnosis and treatment monitoring to predicting prognoses. They may therefore play an important role in the management of UC and precision medicine.

Oncological effectiveness of bladder-preserving trimodal therapy versus radical cystectomy for the treatment of muscle-invasive bladder cancer: a system review and meta-analysis

OBJECTIVE

Radical cystectomy (RC) is the gold standard treatment for muscle-invasive bladder cancer (MIBC). As a bladder-preservation option recommended in guidelines, trimodal therapy (TMT) has become increasingly popular in recent years. However, it is still uncertain whether TMT can provide comparable oncologic outcomes to RC. Therefore, it is imperative to evaluate whether TMT yields comparable outcomes to RC.

METHODS

We conducted a systematic search of Web of Science, MEDLINE, the Cochrane Library, and EMBASE databases up to June 2023 to identify studies that met our inclusion criteria. The primary outcome measures evaluated in this study were overall survival (OS) and cancer-specific survival (CSS). The study quality was evaluated independently by two authors, and data were extracted accordingly.

RESULTS

After excluding duplicates and ineligible articles, our meta-analysis included seven studies involving 3,489 and 13,877 patients in the TMT and RC groups, respectively. Short-term overall survival rates were comparable between the groups, but beyond 5 and > 10-years, the RC group had significantly higher overall survival rates compared to the TMT group. In terms of cancer-specific survival, there was no significant difference between the groups at 1-year follow-up, but from the second year onwards, including the 5-year and > 10-year nodes, the RC group had significantly better outcomes compared to the TMT group.

CONCLUSION

The treatment effect of RC is better than that of TMT. Unless the patient can't tolerate RC or has a strong desire to preserve the bladder, RC should be chosen over TMT in treatment, and patients undergoing TMT should be closely followed up.

States of quinolinic acid excess in urine: A systematic review of human studies

Introduction

Quinolinic acid is an intermediate compound derived from the metabolism of dietary tryptophan. Its accumulation has been reported in patients suffering a broad spectrum of diseases and conditions. In this manuscript, we present the results of a systematic review of research studies assessing urinary quinolinic acid in health and disease.

Methods

We performed a literature review using PubMed, Cochrane, and Scopus databases of all studies reporting data on urinary quinolinic acid in human subjects from December 1949 to January 2022.

Results

Fifty-seven articles met the inclusion criteria. In most of the reported studies, compared to the control group, quinolinic acid was shown to be at increased concentration in urine of patients suffering from different diseases and conditions. This metabolite was also demonstrated to correlate with the severity of certain diseases including juvenile idiopathic inflammatory myopathies, graft vs. host disease, autism spectrum disorder, and prostate cancer. In critically ill patients, elevated quinolinic acid in urine predicted a spectrum of adverse outcomes including hospital mortality.

Conclusion

Quinolinic acid has been implicated in the pathophysiology of multiple conditions. Its urinary accumulation appears to be a feature of acute physiological stress and several chronic diseases. The exact significance of these findings is still under investigation, and further studies are needed to reveal the subsequent implications of this accumulation.

LC-MS metabolomics of urine reveals distinct profiles for non-muscle-invasive and muscle-invasive bladder cancer

PURPOSE

Bladder cancer (BC) is among the most frequent malignancies worldwide. Novel non-invasive markers are needed to diagnose and stage BC with more accuracy than invasive procedures like cystoscopy. To date, no study has identified urine metabolites characteristic of all BC stages. To discover novel urine metabolomic profiles to diagnose and stage non-muscle-invasive (NMIBC) and muscle-invasive (MIBC) patients using mass spectrometry-based metabolomics.

METHODS

We prospectively recruited 198 BC patients and 98 age- and sex-matched healthy volunteers without evidence of renal or bladder condition confirmed by ultrasound, from whom we collected a first morning urine sample (before surgery in patients). In a discovery stage, an untargeted metabolomic analysis was conducted in urine samples of a selection of 64 BC patients (19 TaG1, 11 TaG3, 20 T1G3, 12 T2G3, 1 T2G2, 1 T3G3) and 20 controls to identify dysregulated metabolites. Next, after exhaustive multivariate analysis, confirmed dysregulated metabolites were validated in an independent cohort of 134 BC patients (19 TaG1, 62 TaG2, 9 TaG3, 15 T1G2, 16 T1G3, 4 T2G2, 9 T2G3) and 78 controls.

RESULTS

We validated p-cresol glucuronide as potential diagnostic biomarker for BC patients compared to controls (AUC = 0.79). For NMIBC, p-cresol glucuronide was valuable as staging biomarker (AUC = 0.803). And among NMIBCs, p-coumaric acid may be a potential specific staging biomarker for the TaG1 NMIBC; however, future validation experiments should be conducted once the precise version of the standard is commercially available. Remarkably, for MIBC we validated spermine as potential specific staging biomarker (AUC = 0.882).

CONCLUSION

Ours is the first metabolomics study conducted in urine of a thoroughly characterized cohort comprising all stages of NMIBC, MIBC and healthy controls in which we identified non-invasive diagnostic and staging biomarkers. These may improve BC management, thus reducing the use of current harmful diagnostic techniques.

Integrative analysis of plasma metabolomics and proteomics reveals the metabolic landscape of breast cancer

BACKGROUND

Breast cancer (BC) is the most commonly diagnosed cancer. Currently, mammography and breast ultrasonography are the main clinical screening methods for BC. Our study aimed to reveal the specific metabolic profiles of BC patients and explore the specific metabolic signatures in human plasma for BC diagnosis.

METHODS

This study enrolled 216 participants, including BC patients, benign patients, and healthy controls (HC) and formed two cohorts, one training cohort and one testing cohort. Plasma samples were collected from each participant and subjected to perform nontargeted metabolomics and proteomics. The metabolic signatures for BC diagnosis were identified through machine learning.

RESULTS

Metabolomics analysis revealed that BC patients showed a significant change of metabolic profiles compared to HC individuals. The alanine, aspartate and glutamate pathways, glutamine and glutamate metabolic pathways, and arginine biosynthesis pathways were the critical biological metabolic pathways in BC. Proteomics identified 29 upregulated and 2 downregulated proteins in BC. Our integrative analysis found that aspartate aminotransferase (GOT1), L-lactate dehydrogenase B chain (LDHB), glutathione synthetase (GSS), and glutathione peroxidase 3 (GPX3) were closely involved in these metabolic pathways. Support vector machine (SVM) demonstrated a predictive model with 47 metabolites, and this model achieved a high accuracy in BC prediction (AUC = 1). Besides, this panel of metabolites also showed a fairly high predictive power in the testing cohort between BC vs HC (AUC = 0.794), and benign vs HC (AUC = 0.879).

CONCLUSIONS

This study uncovered specific changes in the metabolic and proteomic profiling of breast cancer patients and identified a panel of 47 plasma metabolites, including sphingomyelins, glutamate, and cysteine could be potential diagnostic biomarkers for breast cancer.

Analysis of VOCs in Urine Samples Directed towards of Bladder Cancer Detection

Bladder cancer is one of most common types of cancer diagnosed in the genitourinary tract. Typical tests are costly and characterized by low sensitivity, which contributes to a growing interest in volatile biomarkers. Head space solid phase microextraction (SPME) was applied for the extraction of volatile organic compounds from urine samples, and gas chromatography time of flight mass spectrometry (GC×GC TOF MS) was used for the separation and detection of urinary volatiles. A cohort of 40 adult patients with bladder cancer and 57 healthy persons was recruited. Different VOC profiles were obtained for urine samples taken from each group. Twelvecompounds were found only in the samples from theBC group.The proposed candidate biomarkers are butyrolactone; 2-methoxyphenol; 3-methoxy-5-methylphenol; 1-(2,6,6-trimethylcyclohexa-1,3-dien-1-yl)-2-buten-1-one; nootkatone and 1-(2,6,6-trimethyl-1-cyclohexenyl)-2-buten-1-one.Since most of the studies published in the field are proving the potential of VOCs detected in urine samples for the screening and discrimination of patients with bladder cancer from healthy, but rarely presenting the identity of proposed biomarkers, our study represents a novel approach.

Whither Gas Chromatography? New Tools ~ New Solutions

We might well ask “Where is gas chromatography (GC) heading?” For many analysts, the answer may be just “more of the same,” reflecting that GC is mature and that most analysis tasks and sample types have been tried and tested. In this scenario, any changes to the basic method may be marginal—sample introduction, and maybe a new detector? But beneath this status quo is an undercurrent of passion, excitement, and power.

Profiling of Urine Carbonyl Metabolic Fingerprints in Bladder Cancer Based on Ambient Ionization Mass Spectrometry

The diagnosis of bladder cancer (BC) is currently based on cystoscopy, which is invasive and expensive. Here, we describe a noninvasive profiling method for carbonyl metabolic fingerprints in BC, which is based on a desorption, separation, and ionization mass spectrometry (DSI-MS) platform with N,N-dimethylethylenediamine (DMED) as a differential labeling reagent. The DSI-MS platform avoids the interferences from intra- and/or intersamples. Additionally, the DMED derivatization increases detection sensitivity and distinguishes carboxyl, aldehyde, and ketone groups in untreated urine samples. Carbonyl metabolic fingerprints of urine from 41 BC patients and 41 controls were portrayed and 9 potential biomarkers were identified. The mechanisms of the regulations of these biomarkers have been tentatively discussed. A logistic regression (LR) machine learning algorithm was applied to discriminate BC from controls, and an accuracy of 85% was achieved. We believe that the method proposed here may pave the way toward the point-of-care diagnosis of BC in a patient-friendly manner.

Artificially Intelligent Olfaction for Fast and Noninvasive Diagnosis of Bladder Cancer from Urine

Globally, bladder cancer (BLC) is one of the most common cancers and has a high recurrence and mortality rate. Current clinical diagnostic approaches are either invasive or inaccurate. Here, we report on a cost-efficient, artificially intelligent chemiresistive sensor array made of polyaniline (PANI) derivatives that can noninvasively diagnose BLC at an early stage and maintain postoperative surveillance through ″smelling″ clinical urine samples at room temperature. In clinical trials, 18 healthy controls and 76 BLC patients (60 and 16 at early and advanced stages, respectively) are assessed by the artificial olfactory system. With the assistance of a support vector machine (SVM), very high sensitivity and accuracy from healthy controls are achieved, exceeding those obtained by the current techniques in practice. In addition, the recurrences of both early and advanced stages are diagnosed well, with the effect of confounding factors on the performance of the artificial olfactory system found to have a negligible influence on the diagnostic performance. Overall, this study contributes a novel, noninvasive, easy-to-use, inexpensive, real-time, accurate method for urine disease diagnosis, which can be useful for personalized care/diagnosis and postoperative surveillance, resulting in saving more lives.

Guide to Metabolomics Analysis: A Bioinformatics Workflow

Metabolomics is an emerging field that quantifies numerous metabolites systematically. The key purpose of metabolomics is to identify the metabolites corresponding to each biological phenotype, and then provide an analysis of the mechanisms involved. Although metabolomics is important to understand the involved biological phenomena, the approach's ability to obtain an exhaustive description of the processes is limited. Thus, an analysis-integrated metabolomics, transcriptomics, proteomics, and other omics approach is recommended. Such integration of different omics data requires specialized statistical and bioinformatics software. This review focuses on the steps involved in metabolomics research and summarizes several main tools for metabolomics analyses. We also outline the most abnormal metabolic pathways in several cancers and diseases, and discuss the importance of multi-omics integration algorithms. Overall, our goal is to summarize the current metabolomics analysis workflow and its main analysis software to provide useful insights for researchers to establish a preferable pipeline of metabolomics or multi-omics analysis.

Untargeted metabolomics analysis of esophageal squamous cell cancer progression

90% of esophageal cancer are esophageal squamous cell carcinoma (ESCC) and ESCC has a very poor prognosis and high mortality. Nevertheless, the key metabolic pathways associated with ESCC progression haven’t been revealed yet. Metabolomics has become a new platform for biomarker discovery over recent years. We aim to elucidate dominantly metabolic pathway in all ESCC tumor/node/metastasis (TNM) stages and adjacent cancerous tissues. We collected 60 postoperative esophageal tissues and 15 normal tissues adjacent to the tumor, then performed Liquid Chromatography with tandem mass spectrometry (LC–MS/MS) analyses. The metabolites data was analyzed with metabolites differential and correlational expression heatmap according to stage I vs. con., stage I vs. stage II, stage II vs. stage III, and stage III vs. stage IV respectively. Metabolic pathways were acquired by Kyoto Encyclopedia of Genes and Genomes. (KEGG) pathway database. The metabolic pathway related genes were obtained via Gene Set Enrichment Analysis (GSEA). mRNA expression of ESCC metabolic pathway genes was detected by two public datasets: gene expression data series (GSE)23400 and The Cancer Genome Atlas (TCGA). Receiver operating characteristic curve (ROC) analysis is applied to metabolic pathway genes. 712 metabolites were identified in total. Glycerophospholipid metabolism was significantly distinct in ESCC progression. 16 genes of 77 genes of glycerophospholipid metabolism mRNA expression has differential significance between ESCC and normal controls. Phosphatidylserine synthase 1 (PTDSS1) and Lysophosphatidylcholine Acyltransferase1 (LPCAT1) had a good diagnostic value with Area under the ROC Curve (AUC) > 0.9 using ROC analysis. In this study, we identified glycerophospholipid metabolism was associated with the ESCC tumorigenesis and progression. Glycerophospholipid metabolism could be a potential therapeutic target of ESCC progression.

Diagnostic Value of 1H NMR-Based Metabolomics in Acute Lymphoblastic Leukemia, Acute Myeloid Leukemia, and Breast Cancer

Cancer refers to a massive number of diseases distinguished by the development of abnormal cells that divide uncontrollably and have the capability of infiltration and destroying the normal body tissue. It is critical to detect biomarkers that are early detectable and noninvasive to save millions of lives. The aim of the present work is to use NMR as a noninvasive diagnostic tool for cancer diseases. This study included 30 plasma and 21 urine samples of patients diagnosed with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML), 25 plasma and 17 urine samples of patients diagnosed with breast cancer (BC), and 9 plasma and urine samples obtained from healthy individuals as controls. They were prepared for NMR measurements; then, the metabolites were identified and the data were analyzed using multivariate statistical procedures. The OPLS-DA score plots clearly discriminated ALL, AML, and BC from healthy controls. Plots of the PLS-DA loadings and S-line plots showed that all metabolites in plasma were greater in BC than in the healthy controls, whereas lactate, O-acetylcarnitine, pyruvate, trimethylamine-N-oxide (TMAO), and glucose were higher in healthy controls than in ALL and AML. On the other hand, urine samples showed lower amounts of lactate, melatonin, pyruvate, and succinate in all of the studied types of cancer when compared to those of healthy controls. 1H NMR can be a successful and noninvasive tool for the diagnosis of different types of cancer.

Comprehensive two-dimensional gas chromatography with mass spectrometry: an advanced bioanalytical technique for clinical metabolomics studies

This review highlights the current state of knowledge in the development of GC × GC-MS for the analysis of clinical metabolites. Selected applications are described as well as our perspectives on current challenges and potential future directions.

Rapid screening for genitourinary cancers: mass spectrometry-based metabolic fingerprinting of urine

Genitourinary (GU) cancers are among the most common malignant diseases in men. Rapid screening is the key to GU cancers management for early diagnosis and treatment. Urine is a highly...

Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics

Rapid advancements in high-throughput omics technologies and experimental protocols have led to the generation of vast amounts of scale-specific biomolecular data on cancer that now populates several online databases and resources. Cancer systems biology models built using this data have the potential to provide specific insights into complex multifactorial aberrations underpinning tumor initiation, development, and metastasis. Furthermore, the annotation of these single- and multi-scale models with patient data can additionally assist in designing personalized therapeutic interventions as well as aid in clinical decision-making. Here, we have systematically reviewed the emergence and evolution of (i) repositories with scale-specific and multi-scale biomolecular cancer data, (ii) systems biology models developed using this data, (iii) associated simulation software for the development of personalized cancer therapeutics, and (iv) translational attempts to pipeline multi-scale panomics data for data-driven in silico clinical oncology. The review concludes that the absence of a generic, zero-code, panomics-based multi-scale modeling pipeline and associated software framework, impedes the development and seamless deployment of personalized in silico multi-scale models in clinical settings.

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.

A Unique Urinary Metabolic Feature for the Determination of Bladder Cancer, Prostate Cancer, and Renal Cell Carcinoma

Prostate cancer (PCa), bladder cancer (BCa), and renal cell carcinoma (RCC) are the most prevalent cancer among urological cancers. However, there are no cancer-specific symptoms that can differentiate them as well as early clinical signs of urological malignancy. Furthermore, many metabolic studies have been conducted to discover their biomarkers, but the metabolic profiling study to discriminate between these cancers have not yet been described. Therefore, in this study, we aimed to investigate the urinary metabolic differences in male patients with PCa (n = 24), BCa (n = 29), and RCC (n = 12) to find the prominent combination of metabolites between cancers. Based on ¹H NMR analysis, orthogonal partial least-squares discriminant analysis was applied to find distinct metabolites among cancers. Moreover, the ranked analysis of covariance by adjusting a potential confounding as age revealed that 4-hydroxybenzoate, N-methylhydantoin, creatinine, glutamine, and acetate had significantly different metabolite levels among groups. The receiver operating characteristic analysis created by prominent five metabolites showed the great discriminatory accuracy with area under the curve (AUC) > 0.7 for BCa vs. RCC, PCa vs. BCa, and RCC vs. PCa. This preliminary study compares the metabolic profiles of BCa, PCa, and RCC, and reinforces the exploratory role of metabolomics in the investigation of human urine.

Ultrasensitive and High Reproducible Detection of Urinary Metabolites Using the Tip-Contact Extraction Method Coupled with Negative LDI-MS

More and more evidence has proved that urinary metabolites can instantly reflect disease state. Therefore, ultra-sensitive and reproducible detection of urinary metabolites in a high-throughput way is urgently desirable for clinical diagnosis. Matrix-free laser desorption/ionization mass spectrometry (LDI-MS) is a high-throughput platform for metabolites detection, but it is encountered by severe interference from numerous salts in urine samples, because the crystallized urine salt on dried samples could result in poor reproducibility in LDI-MS detection. The present work proposed a tip-contact extraction (TCE) technique to eliminate interference from the urine salt. Vertical silicon nanowire arrays decorated with the fluorinated ethylene propylene film (FEP@VSiNWs) could effectively extract metabolites from the urine sample dropping on its surface. High salt tolerance was observed in the subsequent LDI-MS detection of the metabolites extracted on the tip of FEP@VSiNWs even in the presence of 1 M urea. Stable and reproducible mass spectra for non-target metabolic analysis were obtained in real urine samples with different dilution folds. Urinary metabolites collected from bladder cancer (BC) patients were reliably profiled by the TCE method coupled with negative LDI-MS. Based on this platform, potential metabolic biomarkers that can distinguish BC patients and normal controls were uncovered.

Developing non-invasive bladder cancer screening methodology through potentiometric multisensor urine analysis

We report on the feasibility study exploring the potential of a simple electrochemical multisensor system as a tool for distinguishing between urine samples from patients with confirmed bladder cancer (36 samples) and healthy volunteers (51 samples). The potentiometric sensor responses obtained in urine samples were employed as the input data for various machine learning classification algorithms (logistic regression, random forest, extreme gradient boosting classifier, support vector machine, and voting classifier). The performance metrics of the classifiers were evaluated via Monte-Carlo cross-validation. The best model combining all the acquired data from the people aged 19-88 with different tumor grades and malignancies, including patients with recurrent bladder cancer, yielded 72% accuracy, 71% sensitivity, and 58% specificity. It was found that these metrics can be improved to 76% accuracy, 80% sensitivity, and 75% specificity when only a limited age group (50-88 years of age) is considered. Taking into account the simplicity of the proposed screening method, this technique appears to be a promising tool for further research.

Metabolomics in cancer research and emerging applications in clinical oncology

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

Putative markers for the detection of early-stage bladder cancer selected by urine metabolomics

BACKGROUND

Early detection of bladder cancer remains challenging because patients with early-stage bladder cancer usually have no incentive to take cytology or cystoscopy tests if they are asymptomatic. Our goal is to find non-invasive marker candidates that may help us gain insight into the metabolism of early-stage bladder cancer and be examined in routine health checks.

RESULTS

We acquired urine samples from 124 patients diagnosed with early-stage bladder cancer or hernia (63 cancer patients and 61 controls). In which 100 samples were included in our marker discovery cohort, and the remaining 24 samples were included in our independent test cohort. We obtained metabolic profiles of 922 compounds of the samples by gas chromatography-mass spectrometry. Based on the metabolic profiles of the marker discovery cohort, we selected marker candidates using Wilcoxon rank-sum test with Bonferroni correction and leave-one-out cross-validation; we further excluded compounds detected in less than 60% of the bladder cancer samples. We finally selected eight putative markers. The abundance of all the eight markers in bladder cancer samples was high but extremely low in hernia samples. Moreover, the up-regulation of these markers might be in association with sugars and polyols metabolism.

CONCLUSIONS

In the present study, comparative urine metabolomics selected putative metabolite markers for the detection of early-stage bladder cancer. The suggested relations between early-stage bladder cancer and sugars and polyols metabolism may create opportunities for improving the detection of bladder cancer.

Epigenomic and Metabolomic Integration Reveals Dynamic Metabolic Regulation in Bladder Cancer

Bladder cancer (BC) represents a clinical, social, and economic challenge due to tumor-intrinsic characteristics, limitations of diagnostic techniques and a lack of personalized treatments. In the last decade, the use of liquid biopsy has grown as a non-invasive approach to characterize tumors. Moreover, the emergence of omics has increased our knowledge of cancer biology and identified critical BC biomarkers. The rewiring between epigenetics and metabolism has been closely linked to tumor phenotype. Chromatin remodelers interact with each other to control gene silencing in BC, but also with stress-inducible factors or oncogenic signaling cascades to regulate metabolic reprogramming towards glycolysis, the pentose phosphate pathway, and lipogenesis. Concurrently, one-carbon metabolism supplies methyl groups to histone and DNA methyltransferases, leading to the hypermethylation and silencing of suppressor genes in BC. Conversely, α-KG and acetyl-CoA enhance the activity of histone demethylases and acetyl transferases, increasing gene expression, while succinate and fumarate have an inhibitory role. This review is the first to analyze the interplay between epigenome, metabolome and cell signaling pathways in BC, and shows how their regulation contributes to tumor development and progression. Moreover, it summarizes non-invasive biomarkers that could be applied in clinical practice to improve diagnosis, monitoring, prognosis and the therapeutic options in BC.

Unique ion filter-A data reduction tool for chemometric analysis of raw comprehensive two-dimensional gas chromatography-mass spectrometry data

Comprehensive gas chromatography with time of flight mass spectrometry is a powerful tool in the analysis of complex samples. Chemometric analysis of raw chromatographic data is more useful in one- and two-dimensional separations relative to peak tables. The data volume from such experiments generally necessitates the use of data reduction tools. Such tools often sacrifice some of the multivariate information in the mass to charge ratio dimension. The unique ion filter reduces the over-redundancy in two-dimensional gas chromatography-mass spectrometry data by limiting the data to a few unique/pseudo-unique ions, sub-peaks/slices in the first dimension, and spectra in the second dimension. We explore the performance of this algorithm through careful inspection of two-dimensional gas chromatography-mass spectrometry data before and after application of the filter. A reduction (99%) in the number of variables in a two-dimensional gas chromatography-mass spectrometry chromatogram passed on to subsequent analysis was observed. Feature selection times for model optimization reduced from 229 (±13) to 6.8 (±0.5) min when the filter was applied. An estimate of two unique/pseudo-unique ions, one sub-peak in the first dimension and five spectra in the second dimension were considered to provide a true representation of each chromatogram and provided enough information to achieve 100% model prediction accuracy.

Not Only Immune Escape-The Confusing Role of the TRP Metabolic Pathway in Carcinogenesis

BACKGROUND

The recently discovered phenomenon that cancer cells can avoid immune response has gained scientists' interest. One of the pathways involved in this process is tryptophan (TRP) metabolism through the kynurenine pathway (KP). Individual components involved in TRP conversion seem to contribute to cancerogenesis both through a direct impact on cancer cells and the modulation of immune cell functionality. Due to this fact, this pathway may serve as a target for immunotherapy and attempts are being made to create novel compounds effective in cancer treatment. However, the results obtained from clinical trials are not satisfactory, which raises questions about the exact role of KP elements in tumorigenesis. An increasing number of experiments reveal that TRP metabolites may either be tumor promoters and suppressors and this is why further research in this field is highly needed. The aim of this study is to present KP as a modulator of cancer development through multiple mechanisms and to point to its ambiguity, which may be a reason for failures in treatment based on the inhibition of tryptophan metabolism.

Research Progress of Urine Biomarkers in the Diagnosis, Treatment, and Prognosis of Bladder Cancer

Bladder cancer (BC) is one of the most common tumor with high incidence. Relative to other cancers, BC has a high rate of recurrence, which results in increased mortality. As a result, early diagnosis and life-long monitoring are clinically significant for improving the long-term survival rate of BC patients. At present, the main methods of BC detection are cystoscopy and biopsy; however, these procedures can be invasive and expensive. This can lead to patient refusal and reluctance for monitoring. There are several BC biomarkers that have been approved by the FDA, but their sensitivity, specificity, and diagnostic accuracy are not ideal. More research is needed to identify suitable biomarkers that can be used for early detection, evaluation, and observation. There has been heavy research in the proteomics and genomics of BC and many potential biomarkers have been found. Although the advent of metabonomics came late, with the recent development of advanced analytical technology and bioinformatics, metabonomics has become a widely used diagnostic tool in clinical and biomedical research. It should be emphasized that despite progress in new biomarkers for BC diagnosis, there remains challenges and limitations in metabonomics research that affects its translation into clinical practice. In this chapter, the latest literature on BC biomarkers was reviewed.

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.

Urinary Volatilomics Unveils a Candidate Biomarker Panel for Noninvasive Detection of Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer usually associated with asymptomatic development and risk of systemic progression. Hence, reliable molecular biomarkers of ccRCC are needed to provide early and minimally invasive detection. In this study, urinary volatilome profiling of patients diagnosed with ccRCC (n = 75), and cancer-free controls (n = 75), was performed to investigate the presence of a volatile signature characteristic of ccRCC. Volatile organic compounds (VOCs) in general, and more specifically volatile carbonyl compounds (VCCs), present in urine were extracted by headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME-GC-MS). Supervised multivariate models showed a good discriminatory power of ccRCC patients from controls in urine. Overall, 22 volatile metabolites were found significantly altered between the two groups, including aldehydes, ketones, aromatic hydrocarbons, and terpenoids. A candidate six-biomarker panel, comprising octanal, 3-methylbutanal, benzaldehyde, 2-furaldehyde, 4-heptanone, and p-cresol, depicted the best performance for ccRCC detection with 83% sensitivity, 79% specificity, and 81% accuracy. Moreover, the ccRCC urinary volatilome signature suggested dysregulation of energy metabolism and overexpression of enzymes associated with carcinogenesis. These findings provide the molecular basis for the fine-tuning of gas-sensing materials for application in the development of a bioelectronic sensor.

Urinary Metabolic Biomarkers in Cancer Patients: An Overview

The pathogenesis of cancer involves multiple molecular alterations at the level of genome, epigenome, and stromal environment, resulting in several deregulated signal transduction pathways. Metabolites are not only end products of gene and protein expression but also a consequence of the mutual relationship between the genome and the internal environment. Considering that metabolites serve as a comprehensive chemical fingerprint of cell metabolism, metabolomics is emerging as the method able to discover metabolite biomarkers that can be developed for early cancer detection, prognosis, and response to treatment. Urine represents a noninvasive source, available and rich in metabolites, useful for cancer diagnosis, prognosis, and treatment monitoring. In this chapter, we reported the main published evidences on urinary metabolic biomarkers in the studied cancers related to hepatopancreatic and urinary tract with the aim at discussing their promising role in clinical practice.

Metabolomic and lipidomic approaches to identify biomarkers for bladder cancer and interstitial cystitis (Review)

The discovery, introduction and clinical use of prognostic and diagnostic biomarkers has significantly improved outcomes for patients with various illnesses, including bladder cancer (BC) and other bladder-related diseases, such as benign bladder dysfunction and interstitial cystitis (IC). Several sensitive and noninvasive clinically relevant biomarkers for BC and IC have been identified. Metabolomic- and lipidomic-based biomarkers have notable clinical potential in improving treatment outcomes for patients with cancer; however, there are also some noted limitations. This review article provides a short and concise summary of the literature on metabolomic and lipidomic biomarkers for BC and IC, focusing on the possible clinical utility of profiling metabolic alterations in BC and IC.

Metabolomics of Prostate Cancer Gleason Score in Tumor Tissue and Serum

Gleason score, a measure of prostate tumor differentiation, is the strongest predictor of lethal prostate cancer at the time of diagnosis. Metabolomic profiling of tumor and of patient serum could identify biomarkers of aggressive disease and lead to the development of a less-invasive assay to perform active surveillance monitoring. Metabolomic profiling of prostate tissue and serum samples was performed. Metabolite levels and metabolite sets were compared across Gleason scores. Machine learning algorithms were trained and tuned to predict transformation or differentiation status from metabolite data. A total of 135 metabolites were significantly different (P _adjusted < 0.05) in tumor versus normal tissue, and pathway analysis identified one sugar metabolism pathway (P _adjusted = 0.03). Machine learning identified profiles that predicted tumor versus normal tissue (AUC of 0.82 ± 0.08). In tumor tissue, 25 metabolites were associated with Gleason score (unadjusted P < 0.05), 4 increased in high grade while the remainder were enriched in low grade. While pyroglutamine and 1,5-anhydroglucitol were correlated (0.73 and 0.72, respectively) between tissue and serum from the same patient, no metabolites were consistently associated with Gleason score in serum. Previously reported as well as novel metabolites with differing abundance were identified across tumor tissue. However, a "metabolite signature" for Gleason score was not obtained. This may be due to study design and analytic challenges that future studies should consider. IMPLICATIONS: Metabolic profiling can distinguish benign and neoplastic tissues. A novel unsupervised machine learning method can be utilized to achieve this distinction.

Uterine Metabolomics Reveals Protection of Taohong Siwu Decoction Against Abnormal Uterine Bleeding

Incomplete abortion, a procedure for terminating pregnancy, will lead to abnormal uterine bleeding (AUB), infections, and even death. Taohong Siwu decoction (TSD) is a traditional Chinese medicine (TCM) formula, which has been developed to treat AUB for hundreds of years. However, the mechanism of the protective effect of TSD against AUB is not clear. We performed mass spectrometry (MS) of uterine samples to observe metabolic profile resulting from the treatment with TSD. An integrated gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry based untargeted metabolomics approach combined with multivariate statistical analyses were used to investigate the metabolic profile of TSD against AUB. There was clear separation between pregnant and incomplete aborting rats as well as incomplete aborting and TSD administered rats. Based on random forest algorithm and receiver operator characteristic analysis, 12 biomarkers were optimized related to TSD administered. The effect of TSD on AUB are related to several pathways, such as AA metabolism, glyoxylate and dicarboxylate metabolism, alanine, aspartate, and glutamate metabolism. To our knowledge, this is the first uterine metabolomics study focusing on TSD on AUB and provide a new perspective for explaining the mechanism of TSD on AUB.

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.

Comprehensive Targeted Metabolomic Assay for Urine Analysis

Among all the human biological fluids used for disease biomarker discovery or clinical chemistry, urine stands out. It can be collected easily and noninvasively, it is readily available in large volumes, it is typically free from protein contamination, and it is chemically complex-reflecting a wide range of physiological states and functions. However, the comprehensive metabolomic analysis of urine has been somewhat less studied compared to blood. Indeed, most published metabolomic assays are specifically optimized for serum or plasma. In an effort to improve this situation, we have developed a comprehensive, quantitative MS-based assay for urine analysis. The assay robustly detects and quantifies 142 urinary metabolites including 28 amino acids and derivatives, 17 organic acids, 22 biogenic amines and derivatives, 40 acylcarnitines, 34 lipids, and glucose/hexose, among which 67 metabolites are absolutely quantified and 75 metabolites are semiquantified. All the analysis methods in this assay are based on liquid chromatography-tandem mass spectrometry (LC-MS/MS) using both positive and negative-mode multiple reaction monitoring (MRM). The recovery rates of spiked urine samples at three different concentration levels, that is, low, medium and high, are in the range of 80% to 120% with satisfactory precision values of less than 20%. This targeted metabolomic assay has been successfully applied to the analysis of large numbers of human urine samples, with results closely matching those reported in the literature as well as those obtained from orthogonal analysis via NMR spectroscopy. Moreover, the assay was specifically developed in a 96-well plate format, which enables automated, high-throughput sample analysis. The assay has already been used to analyze more than 1800 urine samples in our laboratory since early 2019.

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.

Metabolomics analysis identifies lysine and taurine as candidate prognostic biomarkers for AML-M2 patients

There is an ongoing search for potential biomarkers for acute myeloid leukemia (AML) patients using metabolic analysis. However, only few studies to date have focused on bone marrow samples or a specific subtype of AML. In the present study, we used gas chromatography time-of-flight mass spectrometry of plasma and bone marrow supernatants to compare the metabolic characteristics of patients with AML with maturation (AML-M2). This approach identified significantly altered metabolites. We next performed pathway analysis and determined relative mRNA expression by qRT-PCR. Our results show that lysine, methionine and serine were significantly decreased in AML-M2 patients compared with healthy control. Moreover, plasma abundance of lysine was negatively associated with patients' risk stratification. Taurine had higher plasma abundance in AML-M2 patients and plasma level of taurine was positively related with AML-M2 risk status, while the expression level of taurine transporter showed a negative correlation. Receiver operating characteristic curve analysis showed these four metabolites had high diagnostic value with lysine showing the highest sensitivity and specificity. These results suggest that plasma abundances of lysine and taurine may serve as potential metabolic biomarkers for the prognosis of patients with AML-M2.

Fluorometric optical sensor arrays for the detection of urinary bladder cancer specific volatile organic compounds in the urine of patients with frank hematuria: a prospective case-control study

This study outlines a simple fluorometric optical sensor system for the sensitive, real time measurement of volatile organic compounds (VOCs) as biomarkers of urinary bladder cancer in patients presenting with frank hematuria and confirmed to have the disease on histopathology. Arrays of 24 sensor points based on fluorescence VOC sensitive materials were made. Urine samples of 38 consecutive patients with pathologically confirmed bladder tumours and 41 age and gender matched healthy controls were recruited and analysed using this sensor array. This system correctly classified 68 out of 79 urine samples with 84.21% sensitivity and 87.80% specificity; the system also achieved 66.67% sensitivity and 75.00% specificity for classification of high-grade and low-grade bladder cancer patients. This study showed promising results in the detection of urinary bladder cancer as well as to classify high grade versus low grade bladder cancers.

Cancer metabolomic markers in urine: evidence, techniques and recommendations

Urinary tests have been used as noninvasive, cost-effective tools for screening, diagnosis and monitoring of diseases since ancient times. As we progress through the 21st century, modern analytical platforms have enabled effective measurement of metabolites, with promising results for both a deeper understanding of cancer pathophysiology and, ultimately, clinical translation. The first study to measure metabolomic urinary cancer biomarkers using NMR and mass spectrometry (MS) was published in 2006 and, since then, these techniques have been used to detect cancers of the urological system (kidney, prostate and bladder) and nonurological tumours including those of the breast, ovary, lung, liver, gastrointestinal tract, pancreas, bone and blood. This growing field warrants an assessment of the current status of research developments and recommendations to help systematize future research.

Advances in urinary biomarker discovery in urological research

A disease-specific biomarker (or biomarkers) is a characteristic reflecting a pathological condition in human body, which can be used as a diagnostic or prognostic tool for the clinical management. A urine-based biomarker(s) may provide a clinical value as attractive tools for clinicians to utilize in the clinical setting in particular to bladder diseases including bladder cancer and other bladder benign dysfunctions. Urine can be easily obtained by patients with no preparation or painful procedures required from patients' side. Currently advanced omics technologies and computational power identified potential omics-based novel biomarkers. An unbiased profiling based on transcriptomics, proteomics, epigenetics, metabolomics approaches et al. found that expression at RNA, protein, and metabolite levels are linked with specific bladder diseases and outcomes. In this review, we will discuss about the urine-based biomarkers reported by many investigators including us and how these biomarkers can be applied as a diagnostic and prognostic tool in clinical trials and patient care to promote bladder health. Furthermore, we will discuss how these promising biomarkers can be developed into a smart medical device and what we should be cautious about toward being used in real clinical setting.

Effects of Bailing capsule on diabetic nephropathy based on UPLC-MS urine metabolomics

Diabetic nephropathy (DN) is one of the most common microvascular diabetes complications and has become a threat to human health. Bailing capsules (BLCs), containing fermentation products of Cordyceps sinensis, have been commonly used for treatment of renal dysfunction, such as DN. However, mechanisms underlying the protective effects of BLC remain largely obscure and await more investigation. In this study, UPLC-MS-based comprehensive metabolomics along with pattern recognition was applied to explore the urine metabolic alteration of DN as well as therapeutic mechanisms of BLC. Nineteen differentially expressed endogenous metabolites were identified related to DN, which were involved in the perturbations of tyrosine metabolism, tryptophan metabolism, glycine metabolism, purine metabolism, glutamine metabolism, phenylalanine metabolism, histidine metabolism and TCA cycle metabolism pathways. After drug intervention, most of the biomarkers exhibited a certain extent towards normal levels (P < 0.05), which indicated that BLC was an effective drug for treating DN and might play its therapeutic role by retrieving abnormal metabolism pathways. The data obtained in this research may pave the way for further exploration of DN and provide key clues to understand the protective effect of BLC.

UPLC-MS-based metabolomics along with pattern recognition was applied to explore the metabolic alteration of diabetic nephropathy and therapeutic mechanisms of Bailing capsule.

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.

Metabolomic profiling identifies novel biomarkers and mechanisms in human bladder cancer treated with submucosal injection of gemcitabine

Bladder cancer (BCa) is a common urinary tract malignancy with frequent recurrences after initial resection. Submucosal injection of gemcitabine prior to transurethral resection of bladder tumor (TURBT) may prevent recurrence of urothelial cancer. However, the underlying mechanism remains unknown. In the present study, ultra-performance liquid chromatography Q-Exactive mass spectrometry was used to profile tissue metabolites from 12 BCa patients. The 48 samples included pre- and post-gemcitabine treatment BCa tissues, as well as adjacent normal tissues. Principal component analysis (PCA) revealed that the metabolic profiles of pre-gemcitabine BCa tissues differed significantly from those of pre-gemcitabine normal tissues. A total of 34 significantly altered metabolites were further analyzed. Pathway analysis using MetaboAnalyst identified three metabolic pathways closely associated with BCa, including glutathione, purine and thiamine metabolism, while gluta-thione metabolism was also identified by the enrichment analysis using MetaboAnalyst. In search of the possible targets of gemcitabine, metabolite profiles were compared between the pre-gemcitabine normal and post-gemcitabine BCa tissues. Among the 34 metabolites associated with BCa, the levels of bilirubin and retinal recovered in BCa tissues treated with gemcitabine. When comparing normal bladder tissues with and without gemcitabine treatment, among the 34 metabolites associated with BCa, it was observed that histamine change may be associated with the prevention of relapse, whereas thiamine change may be involved in possible side effects. Therefore, by employing a hypothesis-free tissue-based metabolomics study, the present study investigated the metabolic signatures of BCa and found that bilirubin and retinal may be involved in the mechanism underlying the biomolecular action of submucosal injection of gemcitabine in urothelial BCa.

Recognition of early and late stages of bladder cancer using metabolites and machine learning

INTRODUCTION

Bladder cancer (BCa) is one of the most common and aggressive cancers. It is the sixth most frequently occurring cancer in men and its rate of occurrence increases with age. The current method of BCa diagnosis includes a cystoscopy and biopsy. This process is expensive, unpleasant, and may have severe side effects. Recent growth in the power and accessibility of machine-learning software has allowed for the development of new, non-invasive diagnostic methods whose accuracy and sensitivity are uncompromising to function.

OBJECTIVES

The goal of this research was to elucidate the biomarkers including metabolites and corresponding genes for different stages of BCa, show their distinguishing and common features, and create a machine-learning model for classification of stages of BCa.

METHODS

Sets of metabolites for early and late stages, as well as common for both stages were analyzed using MetaboAnalyst and Ingenuity^® Pathway Analysis (IPA^®) software. Machine-learning methods were utilized in the development of a binary classifier for early- and late-stage metabolites of BCa. Metabolites were quantitatively characterized using EDragon 1.0 software. The two modeling methods used are Multilayer Perceptron (MLP) and Stochastic Gradient Descent (SGD) with a logistic regression loss function.

RESULTS

We explored metabolic pathways related to early-stage BCa (Galactose metabolism and Starch and sucrose metabolism) and to late-stage BCa (Glycine, serine, and threonine metabolism, Arginine and proline metabolism, Glycerophospholipid metabolism, and Galactose metabolism) as well as those common to both stages pathways. The central metabolite impacting the most cancerogenic genes (AKT, EGFR, MAPK3) in early stage is D-glucose, while late-stage BCa is characterized by significant fold changes in several metabolites: glycerol, choline, 13(S)-hydroxyoctadecadienoic acid, 2'-fucosyllactose. Insulin was also seen to play an important role in late stages of BCa. The best performing model was able to predict metabolite class with an accuracy of 82.54% and the area under precision-recall curve (PRC) of 0.84 on the training set. The same model was applied to three separate sets of metabolites obtained from public sources, one set of the late-stage metabolites and two sets of the early-stage metabolites. The model was better at predicting early-stage metabolites with accuracies of 72% (18/25) and 95% (19/20) on the early sets, and an accuracy of 65.45% (36/55) on the late-stage metabolite set.

CONCLUSION

By examining the biomarkers present in the urine samples of BCa patients as compared with normal patients, the biomarkers associated with this cancer can be pinpointed and lead to the elucidation of affected metabolic pathways that are specific to different stages of cancer. Development of machine-learning model including metabolites and their chemical descriptors made it possible to achieve considerable accuracy of prediction of stages of BCa.

Global Plasma Profiling for Colorectal Cancer-Associated Volatile Organic Compounds: a Proof-of-Principle Study

Volatile organic compounds (VOCs) could reflect changes resulting from ongoing pathophysiological processes and altered body metabolisms, and thus have been studied for various types of cancers. We aimed to test an advanced global metabolomic technique to characterize circulating VOCs in patients diagnosed with colorectal cancer (CRC). We employed solid-phase microextraction (SPME) and comprehensive two-dimensional gas chromatography mass-spectrometry (GC × GC-MS). We analyzed 30 random plasma samples from incident cases of CRC. The 30 samples were from population controls enrolled in a large population-based case-control study. The number of metabolite peaks detected in the cases was significantly lower than that detected in the controls (median 1530 vs. 1694, P = 0.02). Partial least squares-discriminant analysis showed clear VOC profile differences between the CRC and the controls. After adjustment for multiple comparisons at the 5% false discovery rate level, five VOCs were differentially expressed between the cases and the controls. Among these five VOCs, 2,3,4-trimethyl-hexane (decreased) and 2,4-dimethylhept-1-ene (increased) were both lipid peroxidation products but not previously reported for CRC. In summary, this study pointed to an intriguing observation that the richness of volatile metabolites may be reduced in CRC cases and demonstrated the utility of SPME GC × GC-MS in discovery of candidate markers for further validation.