Integrative Pathway Analysis of Metabolic Signature in Bladder Cancer: A Linkage to The Cancer Genome Atlas Project and Prediction of Survival

Serum metabolites as early detection markers of non-muscle invasive bladder cancer in Chinese patients

Background

Biomarkers of different stages and grades of bladder cancer (BC) are important in clinical work. The objective of our study was to investigate new biomarkers of early-stage BC with liquid chromatography-high resolution mass spectrometry (LC-HRMS) using serum samples.

Methods

A total of 215 cases were included in our study, including 109 healthy adults as the control group and 106 non-muscle invasive bladder cancer (NMIBC) patients as the NMIBC group. Serum samples were collected from BC patients in the early stage, called NMIBC, and healthy people before surgery. We used LC-HRMS to distinguish the NMIBC group from the control group and the low-grade NMIBC group from the control group.

Results

An apparent difference between the NMIBC group and the control group was visualized by unsupervised principal component analysis (PCA). Metabolite panels for 16-hydroxy-10-oxohexadecanoic acid, PGF2a ethanolamide, sulfoglycolithocholate, and threoninyl-alanine were used to distinguish the two groups. The area under the curve (AUC) of the panels was 0.985, and the sensitivity and specificity were 98.63% and 98.59%, respectively. To distinguish the low-grade NMIBC group from the control group, serum metabolic profiling differences between the low-grade NMIBC group and control group samples were also analyzed. Metabolite panels of L-octanoylcarnitine, PGF2a ethanolamide, and threoninyl-alanine showed good discrimination performance. The AUC of the panels was 0.999, and the sensitivity and specificity were 97.8% and 100%, respectively.

Conclusion

Metabolomics analysis of serum samples can distinguish the NMIBC group from the control group, particularly the early-stage low-grade NMIBC group.

Integrative metabolomics and transcriptomics analysis reveals novel therapeutic vulnerabilities in lung cancer

BACKGROUND

Non-small cell lung cancer (NSCLC) comprises the majority (~85%) of all lung tumors, with lung adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC) being the most frequently diagnosed histological subtypes. Multi-modal omics profiling has been carried out in NSCLC, but no studies have yet reported a unique metabolite-related gene signature and altered metabolic pathways associated with LUAD and LUSC.

METHODS

We integrated transcriptomics and metabolomics to analyze 30 human lung tumors and adjacent noncancerous tissues. Differential co-expression was used to identify modules of metabolites that were altered between normal and tumor.

RESULTS

We identified unique metabolite-related gene signatures specific for LUAD and LUSC and key pathways aberrantly regulated at both transcriptional and metabolic levels. Differential co-expression analysis revealed that loss of coherence between metabolites in tumors is a major characteristic in both LUAD and LUSC. We identified one metabolic onco-module gained in LUAD, characterized by nine metabolites and 57 metabolic genes. Multi-omics integrative analysis revealed a 28 metabolic gene signature associated with poor survival in LUAD, with six metabolite-related genes as individual prognostic markers.

CONCLUSIONS

We demonstrated the clinical utility of this integrated metabolic gene signature in LUAD by using it to guide repurposing of AZD-6482, a PI3Kβ inhibitor which significantly inhibited three genes from the 28-gene signature. Overall, we have integrated metabolomics and transcriptomics analyses to show that LUAD and LUSC have distinct profiles, inferred gene signatures with prognostic value for patient survival, and identified therapeutic targets and repurposed drugs for potential use in NSCLC treatment.

Aminoacyl-tRNA synthetases of the multi-tRNA synthetase complex and their role in tumorigenesis

In mammalian cells, 20 aminoacyl-tRNA synthetases (AARS) catalyze the ligation of amino acids to their cognate tRNAs to generate aminoacylated-tRNAs. In higher eukaryotes, 9 of the 20 AARSs, along with 3 auxiliary proteins, join to form the cytoplasmic multi-tRNA synthetase complex (MSC). The complex is absent in prokaryotes, but evolutionary expansion of MSC constituents, primarily by addition of novel interacting domains, facilitates formation of subcomplexes that join to establish the holo-MSC. In some cases, environmental cues direct the release of constituents from the MSC which enables the execution of non-canonical, i.e., "moonlighting", functions distinct from their essential activities in protein translation. These activities are generally beneficial, but can also be deleterious to the cell. Elucidation of the non-canonical activities of several AARSs residing in the MSC suggest they are potential therapeutic targets for cancer, as well as metabolic and neurologic diseases. Here, we describe the role of MSC-resident AARSs in cancer progression, and the factors that regulate their release from the MSC. Also, we highlight recent developments in therapeutic modalities that target MSC AARSs for cancer prevention and treatment.

Integrated Analysis of Energy Metabolism Signature-Identified Distinct Subtypes of Bladder Urothelial Carcinoma

Background: Bladder urothelial carcinoma (BLCA) is the most common type of bladder cancer. In this study, the correlation between the metabolic status and the outcome of patients with BLCA was evaluated using data from the Cancer Genome Atlas and Gene Expression Omnibus datasets.

Methods: The clinical and transcriptomic data of patients with BLCA were downloaded from the Cancer Genome Atlas and cBioPortal datasets, and energy metabolism-related gene sets were obtained from the Molecular Signature Database. A consensus clustering algorithm was then conducted to classify the patients into two clusters. Tumor prognosis, clinicopathological features, mutations, functional analysis, ferroptosis status analysis, immune infiltration, immune checkpoint-related gene expression level, chemotherapy resistance, and tumor stem cells were analyzed between clusters. An energy metabolism-related signature was further developed and verified using data from cBioPortal datasets.

Results: Two clusters (C1 and C2) were identified using a consensus clustering algorithm based on an energy metabolism-related signature. The patients with subtype C1 had more metabolism-related pathways, different ferroptosis status, higher cancer stem cell scores, higher chemotherapy resistance, and better prognosis. Subtype C2 was characterized by an increased number of advanced BLCA cases and immune-related pathways. Higher immune and stromal scores were also observed for the C2 subtype. A signature containing 16 energy metabolism-related genes was then identified, which can accurately predict the prognosis of patients with BLCA.

Conclusion: We found that the energy metabolism-associated subtypes of BLCA are closely related to the immune microenvironment, immune checkpoint-related gene expression, ferroptosis status, CSCs, chemotherapy resistance, prognosis, and progression of BLCA patients. The established energy metabolism-related gene signature was able to predict survival in patients with BLCA.

Identification of a novel metabolism-related gene signature associated with the survival of bladder cancer

Background

Bladder cancer (BC) is one of the most common malignancies and has a relatively poor outcome worldwide. In this study, we attempted to construct a novel metabolism-related gene (MRG) signature for predicting the survival probability of BC patients.

Methods

First, differentially expressed MRGs between BC and normal samples were identified and used to construct a protein-protein interaction (PPI) network and perform mutation analysis. Next, univariate Cox regression analysis was utilized to select prognostic genes, and multivariate Cox regression analysis was applied to establish an MRG signature for predicting the survival probability of BC patients. Moreover, Kaplan-Meier (KM) survival analysis and receiver operating characteristic (ROC) analysis were performed to evaluate the predictive capability of the MRG signature. Finally, a nomogram based on the MRG signature was established to better predict the survival of BC.

Results

In the present study, 27 differentially expressed MRGs were identified, most of which presented mutations in BC patients, and LRP1 showed the highest mutation rate. Next, an MRG signature, including MAOB, FASN and LRP1, was established by using univariate and multivariate Cox regression analysis. Furthermore, survival analysis indicated that BC patients in the high-risk group had a dramatically lower survival probability than those in the low-risk group. Finally, Cox regression analysis showed that the risk score was an independent prognostic factor, and a nomogram integrating age, pathological tumor stage and risk score was established and presented good predictive ability.

Conclusion

We successfully constructed a novel MRG signature to predict the prognosis of BC patients, which might contribute to the clinical treatment of BC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-09006-w.

Identification of an 11-Autophagy-Related-Gene Signature as Promising Prognostic Biomarker for Bladder Cancer Patients

Simple Summary

Human bladder cancer, one of the most common cancers worldwide, is a molecularly heterogenous and complex disease. Identifying novel prognostic biomarkers and establishing new predictive signatures are important for personalized medicine and effective treatment of bladder cancer patients. Autophagy, a cell self-maintenance process that removes damaged organelles and misfolded proteins, displays both tumor promotion and suppression activities. The aim of our study is to investigate the function of autophagy-related genes in bladder cancer with the main focus on their contribution to prognostic outcome. By analyzing data obtained from The Cancer Genome Atlas (TCGA), we identified 32 autophagy-related genes that were highly associated with overall survival of bladder cancer patients. Further statistical assessment established an 11-autophagy-related-gene signature as an effective prognostic biomarker to predict the survival outcomes of bladder cancer patients.

Abstract

Background: Survival rates for highly invasive bladder cancer (BC) patients have been very low, with a 5-year survival rate of 6%. Accurate prediction of tumor progression and survival is important for diagnosis and therapeutic decisions for BC patients. Our study aims to develop an autophagy-related-gene (ARG) signature that helps to predict the survival of BC patients. Methods: RNA-seq data of 403 BC patients were retrieved from The Cancer Genome Atlas Urothelial Bladder Carcinoma (TCGA-BLCA) database. Univariate Cox regression analysis was performed to identify overall survival (OS)-related ARGs. The Lasso Cox regression model was applied to establish an ARG signature in the TCGA training cohort (N = 203). The performance of the 11-gene ARG signature was further evaluated in a training cohort and an independent validation cohort (N = 200) using Kaplan-Meier OS curve analysis, receiver operating characteristic (ROC) analysis, as well as univariate and multivariate Cox regression analysis. Results: Our study identified an 11-gene ARG signature that is significantly associated with OS, including APOL1, ATG4B, BAG1, CASP3, DRAM1, ITGA3, KLHL24, P4HB, PRKCD, ULK2, and WDR45. The ARGs-derived high-risk bladder cancer patients exhibited significantly poor OS in both training and validation cohorts. The prognostic model showed good predictive efficacy, with the area under the ROC curve (AUCs) for 1-year, 3-year, and 5-year overall survival of 0.702 (0.695), 0.744 (0.640), and 0.794 (0.658) in the training and validation cohorts, respectively. A prognostic nomogram, which included the ARGs-derived risk factor, age and stage for eventual clinical translation, was established. Conclusion: We identified a novel ARG signature for risk-stratification and robust prediction of overall survival for BC patients.

Glioma induced alterations in fecal short-chain fatty acids and neurotransmitters

Aim: To explore fecal short-chain fatty acids and neurotransmitter alterations in a mouse-glioma model and glioma patients. Methods: Liquid chromatography-mass spectrometry and 16S rRNA-sequencing from fecal samples were performed to measure metabolite levels and taxa abundance in mice/humans. Mice underwent GL261 implantation with/without temozolomide. Glioma patients were compared with healthy controls. Results: Glioma altered several short-chain fatty acids and neurotransmitter levels. Reduced 5-hydroxyindoleaceic acid and norepinephrine levels were seen in mice and humans. Interestingly, temozolomide treatment abrogates the effects of glioma on fecal metabolites. Conclusion: Our findings demonstrate the interplay between glioma and the gut-brain axis. Further work is required to identify pathways within the gut-brain axis by which glioma influences and promotes the modulation of fecal metabolites and microbiome.

The metabolic landscape of urological cancers: New therapeutic perspectives

Deregulation of cell metabolism is an established cancer hallmark that contributes to tumor initiation and progression, as well as tumor heterogeneity. In solid tumors, alterations in different metabolic pathways, including glycolysis, pentose phosphate pathway, glutaminolysis and fatty acid metabolism, support the high proliferative rates and macromolecule biosynthesis of cancer cells. Despite advances in therapy, urothelial tumors still exhibit high recurrence and mortality rates, especially in advanced stages of disease. These tumors harbor gene mutations and expression patterns which play an important role in metabolic reprogramming. Taking into account the unique metabolic features underlying carcinogenesis in these cancers, new and promising therapeutic targets based on metabolic alterations must be considered. Furthermore, the combination of metabolic inhibitors with conventional targeted therapies may improve effectiveness of treatments. This review will summarize the metabolic alterations present in urological tumors and the results with metabolic inhibitors currently available.

Metabolomic biomarkers are associated with mortality in patients with cirrhosis caused by primary biliary cholangitis or primary sclerosing cholangitis

Aim:

To assess the ability of signature metabolites alone, or in combination with the model for end-stage liver disease-Na (MELD-Na) score to predict mortality in patients with cirrhosis caused by primary biliary cholangitis or primary sclerosing cholangitis.

Materials & methods:

Plasma metabolites were detected using ultrahigh-performance liquid chromatography/tandem mass spectrometry in 39 patients with cirrhosis caused by primary biliary cholangitis or primary sclerosing cholangitis. Mortality was predicted using Cox proportional hazards regression and time-dependent receiver operating characteristic curve analyses.

Results:

The top five metabolites with significantly greater accuracy than the MELD-Na score (area under the receiver operating characteristic curve [AUROC] = 0.7591) to predict 1-year mortality were myo-inositol (AUROC = 0.9537), N-acetylputrescine (AUROC = 0.9018), trans-aconitate (AUROC = 0.8880), erythronate (AUROC = 0.8345) and N6-carbamoylthreonyladenosine (AUROC = 0.8055). Several combined MELD-Na-metabolite models increased the accuracy of predicted 1-year mortality substantially (AUROC increased from 0.7591 up to 0.9392).

Conclusion:

Plasma metabolites have the potential to enhance the accuracy of mortality predictions, minimize underestimates of mortality in patients with cirrhosis and low MELD-Na scores, and promote equitable allocation of donor livers.

To receive a liver transplant, patients with cirrhosis need to be listed on the US liver transplant waiting list based on a score called the model for end-stage liver disease-Na (MELD-Na) score that is expected to accurately rank the patients based on urgency for a liver transplant. However, MELD-Na score is not sufficiently accurate to identify many patients with cirrhosis with the highest urgency, and this results in longer waiting times on the liver transplant list, and therefore higher death rates. We identified several metabolomic biomarkers that can increase the accuracy of the MELD-Na score, and optimize the allocation of donor livers for transplantation of patients with cirrhosis.

Liquid biopsy in the clinical management of bladder cancer: current status and future developments

Introduction: The use of liquid biopsy on the blood from solid malignancies provides a convenient way of detecting actionable mutations, monitoring treatment response, detecting early recurrence and prognosticating outcomes. The aim of this review is to discuss the current status and future direction of serum biomarkers in the clinical management of urinary bladder cancer.Areas covered: This review provides an overview of blood liquid biopsy and bladder cancer using methods of circulating tumors cells, circulating RNA, serum metabolites and cell-free DNA. Recent clinical studies and advances in methodology are emphasized. We performed a literature search using PMC/PubMed with keywords including 'liquid biopsy', 'circulating tumor DNA', 'cell-free DNA', 'biomarkers', 'bladder cancer' 'precision medicine'. Additional articles were obtained from the cited references of key articles. An emphasis was placed on recent studies published since 2018.Expert opinion: Liquid biopsies represent a potential biomarker using cell-free DNA, metabolomic profiles of altered cellular metabolism, circulating cancer cells and RNA. Despite displaying tremendous clinical promise, the current status of the blood liquid biopsies has not reached fruition. However, future investigations should lead the evolution of liquid biomarker into clinical utility for the management of bladder cancer.

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.

Integrative Analysis of Genomic and Clinical Data Reveals Intrinsic Characteristics of Bladder Urothelial Carcinoma Progression

The progression of bladder cancer is generally a complex and dynamic process, involving a variety of biological factors. Here, we aimed to identify a set of survival-related genes that play an important role in the progression of bladder cancer and uncover their synergistic patterns. Based on the large-scale genomic profiling data and clinical information of 404 bladder cancer patients derived from The Cancer Genome Atlas (TCGA) database, we first discovered 1078 survival-related genes related to their survival states using univariate and multivariate Cox proportional hazardous regression. We then investigated the dynamic changes of the cooperative behaviors of these 1078 genes by analyzing their respective genomic features, including copy number variations, DNA methylations, somatic mutations, and microRNA regulatory networks. Our analyses showed that during the progression of bladder cancer, the biological disorder involving the identified survival-related genes can be reflected by multiple levels of abnormal gene regulation, ranging from genomic alteration to post-transcriptional dysregulation. In particular, the stage-specific co-expression networks of these genes undergo a series of structural variations. Our findings provide useful hints on understanding the underlying complex molecular mechanisms related to the evolution of bladder cancer and offer a new perspective on clinical diagnosis and treatment of bladder cancer.

Metabolomic profiling for the identification of novel diagnostic markers and therapeutic targets in prostate cancer: an update

INTRODUCTION

An altered metabolic regulation is involved in the development and progression of different cancer types. As well as this, many genes associated with tumors are shown to have an important role in control of the metabolism. The incidence of prostate cancer (PCa) is increased in men with metabolic disorders. In particular, obesity is an established risk factor for PCa. An increased body mass index correlates with aggressive disease, and a higher risk of biochemical recurrence and prostate cancer-specific mortality. Increased lipogenesis is also one of the most significant events in PCa metabolism reprogramming. Areas covered: In this article, we provide an updated review of the current understanding of the PCa metabolome and evaluate the possibility of unveiling novel therapeutic targets. Expert opinion: Obesity is an established risk factor for PCa, and an increased BMI correlates with aggressive disease, and a higher risk of biochemical recurrence and prostate cancer-specific mortality. PCa metabolome is characterized by the accumulation of metabolic intermediates and an increased expression of genes in the tricarboxylic acid cycle, the induction of de novo lipogenesis and cholesterogenesis. PCa cells can induce different alterations in their microenvironment by modulating the crosstalk between cancer and stromal cells.

SIRT1 promotes GLUT1 expression and bladder cancer progression via regulation of glucose uptake

Bladder cancer (BC) is one of the most common tumors. Metabolic reprogramming is a feature of neoplasia and tumor growth. Understanding the metabolic alterations in bladder cancer may provide new directions for bladder cancer treatment. Sirtuin 1 (SIRT1) is a lysine deacetylase of multiple targets including metabolic regulators. In pancreatic cancer, the loss of SIRT1 is accompanied by a decreased expression of proteins in the glycolysis pathway, such as GLUT1, and cancer cell proliferation. Thus, we hypothesize that SIRT1 may interact with GLUT1 to modulate the proliferation and glycolysis phenotype in bladder cancer. In the present study, the expression of SIRT1 and GLUT1 was upregulated in BC tissues and cell lines and positively correlated in tissue samples. SIRT1 overexpression or GLUT1 overexpression alone was sufficient to promote cell proliferation and glucose uptake in BC cells. EX527, a specific inhibitor of SIRT1, exerted an opposing effect on bladder cancer proliferation and glucose uptake. The effect of EX527 could be partially reversed by GLUT1 overexpression. More importantly, SIRT1 overexpression significantly promoted the transcriptional activity and expression of GLUT1, indicating that SIRT1 increases the transcription activity and expression of GLUT1, therefore, promoting the cell proliferation and glycolysis in BC cells. Our study first reported that SIRT1/GLUT1 axis promotes bladder cancer progression via regulation of glucose uptake.

Multi-omics Integration Analysis Robustly Predicts High-Grade Patient Survival and Identifies CPT1B Effect on Fatty Acid Metabolism in Bladder Cancer

PURPOSE

The perturbation of metabolic pathways in high-grade bladder cancer has not been investigated. We aimed to identify a metabolic signature in high-grade bladder cancer by integrating unbiased metabolomics, lipidomics, and transcriptomics to predict patient survival and to discover novel therapeutic targets.

EXPERIMENTAL DESIGN

We performed high-resolution liquid chromatography mass spectrometry (LC-MS) and bioinformatic analysis to determine the global metabolome and lipidome in high-grade bladder cancer. We further investigated the effects of impaired metabolic pathways using in vitro and in vivo models.

RESULTS

We identified 519 differential metabolites and 19 lipids that were differentially expressed between low-grade and high-grade bladder cancer using the NIST MS metabolomics compendium and lipidblast MS/MS libraries, respectively. Pathway analysis revealed a unique set of biochemical pathways that are highly deregulated in high-grade bladder cancer. Integromics analysis identified a molecular gene signature associated with poor patient survival in bladder cancer. Low expression of CPT1B in high-grade tumors was associated with low FAO and low acyl carnitine levels in high-grade bladder cancer, which were confirmed using tissue microarrays. Ectopic expression of the CPT1B in high-grade bladder cancer cells led to reduced EMT in in vitro, and reduced cell proliferation, EMT, and metastasis in vivo.

CONCLUSIONS

Our study demonstrates a novel approach for the integration of metabolomics, lipidomics, and transcriptomics data, and identifies a common gene signature associated with poor survival in patients with bladder cancer. Our data also suggest that impairment of FAO due to downregulation of CPT1B plays an important role in the progression toward high-grade bladder cancer and provide potential targets for therapeutic intervention.

Recent advances in the metabolomic study of bladder cancer

INTRODUCTION

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

Serum Metabolic Profiling Identified a Distinct Metabolic Signature in Bladder Cancer Smokers: A Key Metabolic Enzyme Associated with Patient Survival

BACKGROUND

The current system to predict the outcome of smokers with bladder cancer is insufficient due to complex genomic and transcriptomic heterogeneities. This study aims to identify serum metabolite-associated genes related to survival in this population.

METHODS

We performed LC/MS-based targeted metabolomic analysis for >300 metabolites in serum obtained from two independent cohorts of bladder cancer never smokers, smokers, healthy smokers, and healthy never smokers. A subset of differential metabolites was validated using Biocrates absoluteIDQ p180 Kit. Genes associated with differential metabolites were integrated with a publicly available cohort of The Cancer Genome Atlas (TCGA) to obtain an intersecting signature specific for bladder cancer smokers.

RESULTS

Forty metabolites (FDR < 0.25) were identified to be differential between bladder cancer never smokers and smokers. Increased abundance of amino acids (tyrosine, phenylalanine, proline, serine, valine, isoleucine, glycine, and asparagine) and taurine were observed in bladder cancer smokers. Integration of differential metabolomic gene signature and transcriptomics data from TCGA cohort revealed an intersection of 17 genes that showed significant correlation with patient survival in bladder cancer smokers. Importantly, catechol-O-methyltransferase, iodotyrosine deiodinase, and tubulin tyrosine ligase showed a significant association with patient survival in publicly available bladder cancer smoker datasets and did not have any clinical association in never smokers.

CONCLUSIONS

Serum metabolic profiling of bladder cancer smokers revealed dysregulated amino acid metabolism. It provides a distinct gene signature that shows a prognostic value in predicting bladder cancer smoker survival.

IMPACT

Serum metabolic signature-derived genes act as a predictive tool for studying the bladder cancer progression in smokers.

Measurement of methylated metabolites using Liquid Chromatography-Mass Spectrometry and its biological application

Methylation aberrations play an important role in many metabolic disorders including cancer. Methylated metabolites are direct indicators of metabolic aberrations, and currently, there is no Liquid chromatography - Mass spectrometry (LC-MS) based method available to cover all classes of methylated metabolites at low detection limits. In this study, we have developed a method for the detection of methylated metabolites, and it's biological application. In this approach, we used a HILIC based HPLC with MS to measure methylated organic acids, amino acids, and nucleotides. These metabolites were separated from each other by their hydrophobic interactions and analyzed by targeted metabolomics of single reaction monitoring by positive and negative mode of electrospray ionization. These metabolites were quantified, and the interday reproducibility was <10% relative standard deviation. Furthermore, by applying this method, we identified high levels of methylated metabolites in bladder cancer cell lines compared to benign cells. In vitro treatment of cancer cells with methylation inhibitor, 5- aza-2'-deoxycytidine showed a decrease in these methylated metabolites. This data indicates that HPLC analysis using this HILIC based method could be a powerful tool for measuring methylated metabolites in biological specimens. This method is rapid, sensitive, selective, and precise to measure methylated metabolites.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Innovative methods for biomarker discovery in the evaluation and development of cancer precision therapies

The discovery of biomarkers able to detect cancer at an early stage, to evaluate its aggressiveness, and to predict the response to therapy remains a major challenge in clinical oncology and precision medicine. In this review, we summarize recent achievements in the discovery and development of cancer biomarkers. We also highlight emerging innovative methods in biomarker discovery and provide insights into the challenges faced in their evaluation and validation.

Unmasking molecular profiles of bladder cancer

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

Systematic analysis of human telomeric dysfunction using inducible telosome/shelterin CRISPR/Cas9 knockout cells

CRISPR/Cas9 technology enables efficient loss-of-function analysis of human genes using somatic cells. Studies of essential genes, however, require conditional knockout (KO) cells. Here, we describe the generation of inducible CRISPR KO human cell lines for the subunits of the telosome/shelterin complex, TRF1, TRF2, RAP1, TIN2, TPP1 and POT1, which directly interact with telomeres or can bind to telomeres through association with other subunits. Homozygous inactivation of several subunits is lethal in mice, and most loss-of-function studies of human telomere regulators have relied on RNA interference-mediated gene knockdown, which suffers its own limitations. Our inducible CRISPR approach has allowed us to more expediently obtain large numbers of KO cells in which essential telomere regulators have been inactivated for biochemical and molecular studies. Our systematic analysis revealed functional differences between human and mouse telomeric proteins in DNA damage responses, telomere length and metabolic control, providing new insights into how human telomeres are maintained.

Knockdown of SIRT1 Suppresses Bladder Cancer Cell Proliferation and Migration and Induces Cell Cycle Arrest and Antioxidant Response through FOXO3a-Mediated Pathways

Bladder cancer (BCa) is one of the most common tumors, but its underlying mechanism has not been fully clarified. Our transcriptome analysis suggested a close link of Sirtuins, Peroxisome Proliferator-Activated Receptor (PPAR), cell cycle regulation, reactive oxygen species (ROS) metabolism, and Forkhead Box Class O (FOXO) signaling pathway in BCa. SIRT1 is a key member of Sirtuins, playing important roles in aging and energy metabolism, which has been reported to be involved in various metabolic diseases and tumors. We observed that SIRT1 was upregulated in BCa tissues at both mRNA and protein levels. By establishing a SIRT1-knockdown BCa cell model, our results suggested that proliferation and viability were suppressed. Moreover, migration rate was inhibited as well, possibly via reduction of epithelial-mesenchymal transition (EMT). In addition, cell cycle arrest was significantly induced, consisting with strongly decreased proteins involved (CDK2/4/6). Furthermore, ROS production was slightly reduced, accompanied by increasing of antioxidant enzymes and total/acetylated FOXO3a. Consistently with our Path-net analysis, we observed no significant alteration of apoptosis in the SIRT1-knockdown BCa cells. Taken together, our results suggested that SIRT1 deficiency in BCa cells could suppress cell viability by activating antioxidant response and inducing cell cycle arrest possibly via FOXO3a-related pathways.

Pharmacological inhibition of CaMKK2 with the selective antagonist STO-609 regresses NAFLD

Binding of calcium to its intracellular receptor calmodulin (CaM) activates a family of Ca²⁺/CaM-dependent protein kinases. CaMKK2 (Ca²⁺/CaM-dependent protein kinase kinase 2) is a central member of this kinase family as it controls the actions of a CaMK cascade involving CaMKI, CaMKIV or AMPK. CaMKK2 controls insulin signaling, metabolic homeostasis, inflammation and cancer cell growth highlighting its potential as a therapeutic target for a variety of diseases. STO-609 is a selective, small molecule inhibitor of CaMKK2. Although STO-609 has been used extensively in vitro and in cells to characterize and define new mechanistic functions of CaMKK2, only a few studies have reported the in vivo use of STO-609. We synthesized functional STO-609 and assessed its pharmacological properties through in vitro (kinase assay), ex vivo (human liver microsomes) and in vivo (mouse) model systems. We describe the metabolic processing of STO-609, its toxicity, pharmacokinetics and bioavailability in a variety of mouse tissues. Utilizing these data, we show STO-609 treatment to inhibit CaMKK2 function confers protection against non-alcoholic fatty liver disease. These data provide a valuable resource by establishing criteria for use of STO-609 to inhibit the in vivo functions of CaMKK2 and demonstrate its utility for treating metabolically-related hepatic disease.

Metabolomics analysis reveals distinct profiles of nonmuscle-invasive and muscle-invasive bladder cancer

Urothelial carcinoma is the most common form of bladder cancer, but pathway changes that occur with stage-wise progression have not been well defined. We used a metabolomics approach to identify potential metabolic pathways uniquely altered in normal urothelium, nonmuscle-invasive bladder cancer (NMIBC), and muscle-invasive bladder cancer (MIBC). We performed global metabolomic profiling using GC-mass spectrometry (MS) and LC-MS platforms to identify metabolite signatures between normal urothelium and high-grade urothelial carcinoma of different stages. Pathways globally dysregulated in cancer relative to normal urothelium included glucose, tricarboxylic acid (TCA) cycle, lipid, amino acid, and nucleotide pathways. Urothelial carcinoma showed elevated glucose utilization for glycolysis and increased sorbitol pathway intermediates, consistent with Warburg effect. Anaplerosis to sustain energy production suggested by increased late TCA cycle intermediates, amino acids, and dipeptides occurs in bladder cancer. Urothelial carcinoma also shows altered membrane lipid membrane metabolism and differential derivation of nucleic acid components pyrimidine and purine. In stage comparison, MIBC appears to preferentially enhance cyclooxygenase (COX) and lipoxygenase (LOX) signaling, increase heme catabolism, and alter nicotinamide adenine dinucleotide (NAD+) synthesis with a possible influence from associated inflammatory cells. We identify numerous metabolomic alterations in NMIBC and MIBC that likely reflect underlying pathway changes. Differential pathway activity may have value in designing stage-specific novel therapeutics in urothelial carcinoma.

Proteomics analysis to reveal biological pathways and predictive proteins in the survival of high-grade serous ovarian cancer

High-grade serous ovarian cancer (HGSC) is an aggressive cancer with a worse clinical outcome. Therefore, studies about the prognosis of HGSC may provide therapeutic avenues to improve patient outcomes. Since genome alteration are manifested at the protein level, we integrated protein and mRNA data of ovarian cancer from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) and utilized the sparse overlapping group lasso (SOGL) method, a new mechanism-driven variable selection method, to select dysregulated pathways and crucial proteins related to the survival of HGSC. We found that biosynthesis of amino acids was the main biological pathway with the best predictive performance (AUC = 0.900). A panel of three proteins, namely EIF2B1, PRPS1L1 and MAPK13 were selected as potential predictive proteins and the risk score consisting of these three proteins has predictive performance for overall survival (OS) and progression free survival (PFS), with AUC of 0.976 and 0.932, respectively. Our study provides additional information for further mechanism and therapeutic avenues to improve patient outcomes in clinical practice.

Metabolic phenotype of bladder cancer

Metabolism of bladder cancer represents a key issue for cancer research. Several metabolic altered pathways are involved in bladder tumorigenesis, representing therefore interesting targets for therapy. Tumor cells, including urothelial cancer cells, rely on a peculiar shift to aerobic glycolysis-dependent metabolism (the Warburg-effect) as the main energy source to sustain their uncontrolled growth and proliferation. Therefore, the high glycolytic flux depends on the overexpression of glycolysis-related genes (SRC-3, glucose transporter type 1 [GLUT1], GLUT3, lactic dehydrogenase A [LDHA], LDHB, hexokinase 1 [HK1], HK2, pyruvate kinase type M [PKM], and hypoxia-inducible factor 1-alpha [HIF-1α]), resulting in an overproduction of pyruvate, alanine and lactate. Concurrently, bladder cancer metabolism displays an increased expression of genes favoring the pentose phosphate pathway (glucose-6-phosphate dehydrogenase [G6PD]) and the fatty-acid synthesis (fatty acid synthase [FASN]), along with a decrease of AMP-activated protein kinase (AMPK) and Krebs cycle activities. Moreover, the PTEN/PI3K/AKT/mTOR pathway, hyper-activated in bladder cancer, acts as central regulator of aerobic glycolysis, hence contributing to cancer metabolic switch and tumor cell proliferation. Besides glycolysis, glycogen metabolism pathway plays a robust role in bladder cancer development. In particular, the overexpression of GLUT-1, the loss of the tumor suppressor glycogen debranching enzyme amylo-α-1,6-glucosidase, 4-α-glucanotransferase (AGL), and the increased activity of the tumor promoter enzyme glycogen phosphorylase impair glycogen metabolism. An increase in glucose uptake, decrease in normal cellular glycogen storage, and overproduction of lactate are consequences of decreased oxidative phosphorylation and inability to reuse glucose into the pentose phosphate and de novo fatty acid synthesis pathways. Moreover, AGL loss determines augmented levels of the serine-to-glycine enzyme serine hydroxymethyltransferase-2 (SHMT2), resulting in an increased glycine and purine ring of nucleotides synthesis, thus supporting cells proliferation. A deep understanding of the metabolic phenotype of bladder cancer will provide novel opportunities for targeted therapeutic strategies.