Integrative Metabolomic and Transcriptomic Analysis for the Study of Bladder Cancer

Joint metabolomics and transcriptomics analysis systematically reveal the impact of MYCN in neuroblastoma

The limited understanding of the molecular mechanism underlying MYCN-amplified (MNA) neuroblastoma (NB) has hindered the identification of effective therapeutic targets for MNA NB, contributing to its higher mortality rate compared to MYCN non-amplified (non-MNA) NB. Therefore, a comprehensive analysis integrating metabolomics and transcriptomics was conducted to systematically investigate the MNA NB. Metabolomics analysis utilized plasma samples from 28 MNA NB patients and 68 non-MNA NB patients, while transcriptomics analysis employed tissue samples from 15 MNA NB patients and 37 non-MNA NB patients. Notably, joint metabolomics and transcriptomics analysis was performed. A total of 46 metabolites exhibited alterations, with 21 displaying elevated levels and 25 demonstrating reduced levels in MNA NB. In addition, 884 mRNAs in MNA NB showed significant changes, among which 766 mRNAs were higher and 118 mRNAs were lower. Joint-pathway analysis revealed three aberrant pathways involving glycerolipid metabolism, purine metabolism, and lysine degradation. This study highlights the substantial differences in metabolomics and transcriptomics between MNA NB and non-MNA NB, identifying three abnormal metabolic pathways that may serve as potential targets for understanding the molecular mechanisms underlying MNA NB.

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.

Metabolomic profiling of human bladder tissue extracts

INTRODUCTION

Bladder cancer is a common malignancy affecting the urinary tract and effective biomarkers and for which monitoring therapeutic interventions have yet to be identified.

OBJECTIVES

Major aim of this work was to perform metabolomic profiling of human bladder cancer and adjacent normal tissue and to evaluate cancer biomarkers.

METHODS

This study utilized nuclear magnetic resonance (NMR) and high-resolution nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS) methods to investigate polar metabolite profiles in tissue samples from 99 bladder cancer patients.

RESULTS

Through NMR spectroscopy, six tissue metabolites were identified and quantified as potential indicators of bladder cancer, while LDI-MS allowed detection of 34 compounds which distinguished cancer tissue samples from adjacent normal tissue. Thirteen characteristic tissue metabolites were also found to differentiate bladder cancer tumor grades and thirteen metabolites were correlated with tumor stages. Receiver-operating characteristics analysis showed high predictive power for all three types of metabolomics data, with area under the curve (AUC) values greater than 0.853.

CONCLUSION

To date, this is the first study in which bladder human normal tissues adjacent to cancerous tissues are analyzed using both NMR and MS method. These findings suggest that the metabolite markers identified in this study may be useful for the detection and monitoring of bladder cancer stages and grades.

Targeted quantitative metabolomics with a linear mixed-effect model for analysis of urinary nucleosides and deoxynucleosides from bladder cancer patients before and after tumor resection

In the present study, we developed and validated a fast, simple, and sensitive quantitative method for the simultaneous determination of eleven nucleosides and deoxynucleosides from urine samples. The analyses were performed with the use of liquid chromatography coupled with triple quadrupole mass spectrometry. The sample pretreatment procedure was limited to centrifugation, vortex mixing of urine samples with a methanol/water solution (1:1, v/v), evaporation and dissolution steps. The analysis lasted 20 min and was performed in dynamic multiple reaction monitoring mode (dMRM) in positive polarity. Process validation was conducted to determine the linearity, precision, accuracy, limit of quantification, stability, recovery and matrix effect. All validation procedures were carried out in accordance with current FDA and EMA regulations. The validated method was applied for the analysis of 133 urine samples derived from bladder cancer patients before tumor resection and 24 h, 2 weeks, and 3, 6, 9, and 12 months after the surgery. The obtained data sets were analyzed using a linear mixed-effect model. The analysis revealed that concentration level of 2-methylthioadenosine was decreased, while for inosine, it was increased 24 h after tumor resection in comparison to the preoperative state. The presented quantitative longitudinal study of urine nucleosides and deoxynucleosides before and up to 12 months after bladder tumor resection brings additional prospective insight into the metabolite excretion pattern in bladder cancer disease. Moreover, incurred sample reanalysis was performed proving the robustness and repeatability of the developed targeted method.

Untargeted urinary metabolomics for bladder cancer biomarker screening with ultrahigh-resolution mass spectrometry

Bladder cancer (BC) is a common urological malignancy with a high probability of death and recurrence. Cystoscopy is used as a routine examination for diagnosis and following patient monitoring for recurrence. Repeated costly and intrusive treatments may discourage patients from having frequent follow-up screenings. Hence, exploring novel non-invasive ways to help identify recurrent and/or primary BC is critical. In this work, 200 human urine samples were profiled using ultra-high-performance liquid chromatography and ultra-high-resolution mass spectrometry (UHPLC-UHRMS) to uncover molecular markers differentiating BC from non-cancer controls (NCs). Univariate and multivariate statistical analyses with external validation identified metabolites that distinguish BC patients from NCs disease. More detailed divisions for the stage, grade, age, and gender are also discussed. Findings indicate that monitoring urine metabolites may provide a non-invasive and more straightforward diagnostic method for identifying BC and treating recurrent diseases.

Targeted and untargeted urinary metabolic profiling of bladder cancer

Bladder cancer (BC) is frequent cancer affecting the urinary tract and is one of the most prevalent malignancies worldwide. No biomarkers that can be used for effective monitoring of therapeutic interventions for this cancer have been identified to date. This study investigated polar metabolite profiles in urine samples from 100 BC patients and 100 normal controls (NCs) using nuclear magnetic resonance (NMR) and two methods of high-resolution nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS). Five urine metabolites were identified and quantified using NMR spectroscopy to be potential indicators of bladder cancer. Twenty-five LDI-MS-detected compounds, predominantly peptides and lipids, distinguished urine samples from BC and NCs individuals. Level changes of three characteristic urine metabolites enabled BC tumor grades to be distinguished, and ten metabolites were reported to correlate with tumor stages. Receiver-Operating Characteristics analysis showed high predictive power for all three types of metabolomics data, with the area under the curve (AUC) values greater than 0.87. These findings suggest that metabolite markers identified in this study may be useful for the non-invasive detection and monitoring of bladder cancer stages and grades.

High-resolution magic angle spinning NMR for intact biological specimen analysis: Initial discovery, recent developments, and future directions

High-resolution magic angle spinning (HRMAS) NMR, an approach for intact biological material analysis discovered more than 25 years ago, has been advanced by many technical developments and applied to many biomedical uses. This article provides a history of its discovery, first by explaining the key scientific advances that paved the way for HRMAS NMR's invention, and then by turning to recent developments that have profited from applying and advancing the technique during the last 5 years. Developments aimed at directly impacting healthcare include HRMAS NMR metabolomics applications within studies of human disease states such as cancers, brain diseases, metabolic diseases, transplantation medicine, and adiposity. Here, the discussion describes recent HRMAS NMR metabolomics studies of breast cancer and prostate cancer, as well as of matching tissues with biofluids, multimodality studies, and mechanistic investigations, all conducted to better understand disease metabolic characteristics for diagnosis, opportune windows for treatment, and prognostication. In addition, HRMAS NMR metabolomics studies of plants, foods, and cell structures, along with longitudinal cell studies, are reviewed and discussed. Finally, inspired by the technique's history of discoveries and recent successes, future biomedical arenas that stand to benefit from HRMAS NMR-initiated scientific investigations are presented.

Monoisotopic silver nanoparticles-based mass spectrometry imaging of human bladder cancer tissue: Biomarker discovery

PURPOSE

Bladder cancer (BC) is the 10th most common form of cancer worldwide and the 2nd most common cancer of the urinary tract after prostate cancer, taking into account both incidence and prevalence.

MATERIALS/METHODS

Tissues from patients with BC and also tissue extracts were analyzed by laser desorption/ionization mass spectrometry imaging (LDI-MSI) with monoisotopic silver-109 nanoparticles-enhanced target (¹⁰⁹AgNPET).

RESULTS

Univariate and multivariate statistical analyses revealed 10 metabolites that differentiated between tumor and normal tissues from six patients with diagnosed BC. Selected metabolites are discussed in detail in relation to their mass spectrometry (MS) imaging results. The pathway analysis enabled us to link these compounds with 17 metabolic pathways.

CONCLUSIONS

According to receiver operating characteristic (ROC) analysis of biomarkers, 10 known metabolites were identified as the new potential biomarkers with areas under the curve (AUC) higher than >0.99. In both univariate and multivariate analysis, it was predicted that these compounds could serve as useful discriminators of cancerous versus normal tissue in patients diagnosed with BC.

Metabolomic and elemental profiling of blood serum in bladder cancer

Bladder cancer (BC) is one of the most frequently diagnosed types of urinary cancer. Despite advances in treatment methods, no specific biomarkers are currently in use. Targeted and untargeted profiling of metabolites and elements of human blood serum from 100 BC patients and the same number of normal controls (NCs), with external validation, was attempted using three analytical methods, i.e., nuclear magnetic resonance, gold and silver-109 nanoparticle-based laser desorption/ionization mass spectrometry (LDI-MS), and inductively coupled plasma optical emission spectrometry (ICP-OES). All results were subjected to multivariate statistical analysis. Four potential serum biomarkers of BC, namely, isobutyrate, pyroglutamate, choline, and acetate, were quantified with proton nuclear magnetic resonance, which had excellent predictive ability as judged by the area under the curve (AUC) value of 0.999. Two elements, Li and Fe, were also found to distinguish between cancer and control samples, as judged from ICP-OES data and AUC of 0.807 (in validation set). Twenty-five putatively identified compounds, mostly related to glycans and lipids, differentiated BC from NCs, as detected using LDI-MS. Five serum metabolites were found to discriminate between tumor grades and nine metabolites between tumor stages. The results from three different analytical platforms demonstrate that the identified distinct serum metabolites and metal elements have potential to be used for noninvasive detection, staging, and grading of BC.

Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research

Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.

New bladder cancer non-invasive surveillance method based on voltammetric electronic tongue measurement of urine

Bladder cancer (BC) is the sixth leading cause of death by cancer. Depending on the invasiveness of tumors, patients with BC will undergo surgery and surveillance lifelong, owing the high rate of recurrence and progression. In this context, the development of strategies to support non-invasive BC diagnosis is focusing attention. Voltammetric electronic tongue (VET) has been demonstrated to be of use in the analysis of biofluids. Here, we present the implementation of a VET to study 207 urines to discriminate BC and non-BC for diagnosis and surveillance to detect recurrences. Special attention has been paid to the experimental setup to improve reproducibility in the measurements. PLSDA analysis together with variable selection provided a model with high sensitivity, specificity, and area under the ROC curve AUC (0.844, 0.882, and 0.917, respectively). These results pave the way for the development of non-invasive low-cost and easy-to-use strategies to support BC diagnosis and follow-up.

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Bladder cancer (BC) and control urines were studied by voltammetric electronic tongue

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A PLSDA model was obtained with high sensitivity, specificity, and accuracy (84/88/86)

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103/122 BC urines and 7⅝5 control urines were predicted correctly

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The electronic tongue has the potential for non-invasive BC diagnostics and follow-up

An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer

Simple Summary

Bladder cancer is evidently a challenge as far as its prognosis and treatment are concerned. The investigation of potential biomarkers and therapeutic targets is indispensable and still in progress. Most studies attempt to identify differential signatures between distinct molecular tumor subtypes. Therefore, keeping in mind the heterogeneity of urinary bladder tumors, we attempted to identify a consensus gene-related signature between the common expression profile of bladder cancer and control samples. In the quest for substantive features, we were able to identify key hub genes, whose signatures could hold diagnostic, prognostic, or therapeutic significance, but, primarily, could contribute to a better understanding of urinary bladder cancer biology.

Abstract

Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high morbidity and mortality. This study intended to elucidate potential key biomarkers related to the occurrence, development, and prognosis of BCa through an integrated bioinformatics analysis. In this context, a systematic meta-analysis, integrating 18 microarray gene expression datasets from the GEO repository into a merged meta-dataset, identified 815 robust differentially expressed genes (DEGs). The key hub genes resulted from DEG-based protein–protein interaction and weighted gene co-expression network analyses were screened for their differential expression in urine and blood plasma samples of BCa patients. Subsequently, they were tested for their prognostic value, and a three-gene signature model, including COL3A1, FOXM1, and PLK4, was built. In addition, they were tested for their predictive value regarding muscle-invasive BCa patients’ response to neoadjuvant chemotherapy. A six-gene signature model, including ANXA5, CD44, NCAM1, SPP1, CDCA8, and KIF14, was developed. In conclusion, this study identified nine key biomarker genes, namely ANXA5, CDT1, COL3A1, SPP1, VEGFA, CDCA8, HJURP, TOP2A, and COL6A1, which were differentially expressed in urine or blood of BCa patients, held a prognostic or predictive value, and were immunohistochemically validated. These biomarkers may be of significance as prognostic and therapeutic targets for BCa.

Gene Expression Monotonicity across Bladder Cancer Stages Informs on the Molecular Pathogenesis and Identifies a Prognostic Eight-Gene Signature

Despite advancements in molecular classification, tumor stage and grade still remain the most relevant prognosticators used by clinicians to decide on patient management. Here, we leverage publicly available data to characterize bladder cancer (BLCA)’s stage biology based on increased sample sizes, identify potential therapeutic targets, and extract putative biomarkers. A total of 1135 primary BLCA transcriptomes from 12 microarray studies were compiled in a meta-cohort and analyzed for monotonal alterations in pathway activities, gene expression, and co-expression patterns with increasing stage (Ta–T1–T2–T3–T4), starting from the non-malignant tumor-adjacent urothelium. The TCGA-2017 and IMvigor-210 RNA-Seq data were used to validate our findings. Wnt, MTORC1 signaling, and MYC activity were monotonically increased with increasing stage, while an opposite trend was detected for the catabolism of fatty acids, circadian clock genes, and the metabolism of heme. Co-expression network analysis highlighted stage- and cell-type-specific genes of potentially synergistic therapeutic value. An eight-gene signature, consisting of the genes AKAP7, ANLN, CBX7, CDC14B, ENO1, GTPBP4, MED19, and ZFP2, had independent prognostic value in both the discovery and validation sets. This novel eight-gene signature may increase the granularity of current risk-to-progression estimators.

Identification of glycolysis-associated long non-coding RNA regulatory subtypes and construction of prognostic signatures by transcriptomics for bladder cancer

Glycolysis-targeted cancer therapy based on long non-coding RNAs (lncRNAs), owing to its high specificity and less toxicity, is at the preclinical stages. Our study aimed to examine the roles of the core glycolysis-associated lncRNAs in bladder cancer (BC). Glycolysis scores of BC were computed by single-sample gene set enrichment analysis (ssGSEA). Glycolysis-associated lncRNAs were screened by Pearson's correlation analysis. Unsupervised consensus clustering using ConsensusClusterPlus assessed the glycolysis-associated lncRNAs for the identification of molecular subtypes of BC. The Kaplan-Meier survival analysis, genomic mutations, and tumor microenvironment (TME) analysis were used to compare the characteristics of different subtypes. Key glycolysis-associated lncRNAs were screened by first-order partial correlation and univariate Cox proportional-hazards model analyses; finally, the lncRNA signature was constructed. Four glycolysis-associated lncRNA-regulated subtypes having differential overall survival (OS), clinical features, genomic mutation profiles, and TME profiles along with nuclear immunotherapeutic responses were identified. Nine lncRNAs localized in the nucleus were identified and transcription factors (TFs) significantly negatively associated with these were found to be enriched in multiple oncogenic signaling pathways. Among them, three lncRNAs (AC093673.5, AC034220.3, and RP11-250B2.3) exerted the most profound effects on glycolysis and constituted the lncRNA signature, which could substantially distinguish the risk levels among different BC patients. Four glycolysis-associated lncRNA-regulated subtypes were identified in this study, reflective of the biological characteristics and heterogeneity of BC. Three key glycolysis-associated lncRNA constituting a signature could predict the risk levels in BC, provide a reference for stratification, and be used as prognostic markers for BC diagnosis and treatment.

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

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

Proteomic-Based Machine Learning Analysis Reveals PYGB as a Novel Immunohistochemical Biomarker to Distinguish Inverted Urothelial Papilloma From Low-Grade Papillary Urothelial Carcinoma With Inverted Growth

Background

The molecular biology of inverted urothelial papilloma (IUP) as a precursor disease of urothelial carcinoma is poorly understood. Furthermore, the overlapping histology between IUP and papillary urothelial carcinoma (PUC) with inverted growth is a diagnostic pitfall leading to frequent misdiagnoses.

Methods

To identify the oncologic significance of IUP and discover a novel biomarker for its diagnosis, we employed mass spectrometry-based proteomic analysis of IUP, PUC, and normal urothelium (NU). Machine learning analysis shortlisted candidate proteins, while subsequent immunohistochemical validation was performed in an independent sample cohort.

Results

From the overall proteomic landscape, we found divergent ‘NU-like’ (low-risk) and ‘PUC-like’ (high-risk) signatures in IUP. The latter were characterized by altered metabolism, biosynthesis, and cell–cell interaction functions, indicating oncologic significance. Further machine learning-based analysis revealed SERPINH1, PKP2, and PYGB as potential diagnostic biomarkers discriminating IUP from PUC. The immunohistochemical validation confirmed PYGB as a specific biomarker to distinguish between IUP and PUC with inverted growth.

Conclusion

In conclusion, we suggest PYGB as a promising immunohistochemical marker for IUP diagnosis in routine practice.

The potential role of c-MYC and polyamine metabolism in multiple drug resistance in bladder cancer investigated by metabonomics

Bladder cancer has a high incidence worldwide accompanies by high recurrent rate after treatment. The emergence of primary or acquired chemotherapy resistance leads to poor efficacy in many cases. To explore the underlying mechanisms of drug resistance, we firstly established a drug-resistant cell model T24/THP by repeated exposure of T24 cells to pirarubicin (THP) whose concentration increases gradually. Non-targeted metabolomics was performed to identify metabolic changes and key metabolism pathways variance in T24/THP cells. Pathway enrichment analysis demonstrated that the arginine and proline metabolic pathway was the most significantly changed pathway, where two representative members of polyamine, putrescine and spermidine were remarkably down regulated in T24/THP. Subsequent experiments further confirmed that ornithine decarboxylase (ODC1) and spermidine synthase (SRM), the key enzymes involved in the synthesis of these compounds, also showed a stable low expression in T24/THP. However, knocking down of ODC1 and SRM sensitized cells to chemotherapy treatment while overexpression of these two enzymes enhances chemotherapy resistance. This leaded to the point that ODC1 and SRM themselves are more likely to promote the drug resistance, which appears to contradict their low expression in T24/THP. We hypothesize that their diminished levels were due to the declined activity of genes upstream. According to this line of thought, we found that c-MYC was also down-regulated in T24/THP and its content could be significantly affected by drug administration. In addition, c-MYC could not only regulate the expression levels of ODC1 and SRM but also influence drug resistance in T24/THP. In conclusion, alterations in gene expression of ODC1 and SRM in drug resistance cell line is probably mediated by some upstream regulators rather than antineoplastic agents alone. Exploration of upstream signals and research on detailed regulatory mechanism, thereby understanding the actual role of c-MYC and polyamine in response to chemotherapy, can become a potential field direction to overcome drug resistance in bladder cancer.

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.

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.

Nanotechnology in Bladder Cancer: Diagnosis and Treatment

Bladder cancer (BC) is the second most common cancer of the urinary tract in men and the fourth most common cancer in women, and its incidence rises with age. There are many conventional methods for diagnosis and treatment of BC. There are some current biomarkers and clinical tests for the diagnosis and treatment of BC. For example, radiotherapy combined with chemotherapy and surgical, but residual tumor cells mostly cause tumor recurrence. In addition, chemotherapy after transurethral resection causes high side effects, and lack of selectivity, and low sensitivity in sensing. Therefore, it is essential to improve new procedures for the diagnosis and treatment of BC. Nanotechnology has recently sparked an interest in a variety of areas, including medicine, chemistry, physics, and biology. Nanoparticles (NP) have been used in tumor therapies as appropriate tools for enhancing drug delivery efficacy and enabling therapeutic performance. It is noteworthy, nanomaterial could be reduced the limitation of conventional cancer diagnosis and treatments. Since, the major disadvantages of therapeutic drugs are their insolubility in an aqueous solvent, for instance, paclitaxel (PTX) is one of the important therapeutic agents utilized to treating BC, due to its ability to prevent cancer cell growth. However, its major problem is the poor solubility, which has confirmed to be a challenge when improving stable formulations for BC treatment. In order to reduce this challenge, anti-cancer drugs can be loaded into NPs that can improve water solubility. In our review, we state several nanosystem, which can effective and useful for the diagnosis, treatment of BC. We investigate the function of metal NPs, polymeric NPs, liposomes, and exosomes accompanied therapeutic agents for BC Therapy, and then focused on the potential of nanotechnology to improve conventional approaches in sensing.

Tumor Metabolome: Therapeutic Opportunities Targeting Cancer Metabolic Reprogramming

The study of cancer metabolism is regaining center stage and becoming a hot topic in tumor biology and clinical research, after a period where such kind of experimental approaches were somehow forgotten or disregarded in favor of powerful functional genomic and proteomic studies [...].

Genome-wide linkage analysis of families with primary hyperhidrosis

Primary focal hyperhidrosis (PFH, OMIM %144110) is a genetically influenced condition characterised by excessive sweating. Prevalence varies between 1.0-6.1% in the general population, dependent on ethnicity. The aetiology of PFH remains unclear but an autosomal dominant mode of inheritance, incomplete penetrance and variable phenotypes have been reported. In our study, nine pedigrees (50 affected, 53 non-affected individuals) were included. Clinical characterisation was performed at the German Hyperhidrosis Centre, Munich, by using physiological and psychological questionnaires. Genome-wide parametric linkage analysis with GeneHunter was performed based on the Illumina genome-wide SNP arrays. Haplotypes were constructed using easyLINKAGE and visualised via HaploPainter. Whole-exome sequencing (WES) with 100x coverage in 31 selected members (24 affected, 7 non-affected) from our pedigrees was achieved by next generation sequencing. We identified four genome-wide significant loci, 1q41-1q42.3, 2p14-2p13.3, 2q21.2-2q23.3 and 15q26.3-15q26.3 for PFH. Three pedigrees map to a shared locus at 2q21.2-2q23.3, with a genome-wide significant LOD score of 3.45. The chromosomal region identified here overlaps with a locus at chromosome 2q22.1-2q31.1 reported previously. Three families support 1q41-1q42.3 (LOD = 3.69), two families share a region identical by descent at 2p14-2p13.3 (LOD = 3.15) and another two families at 15q26.3 (LOD = 3.01). Thus, our results point to considerable genetic heterogeneity. WES did not reveal any causative variants, suggesting that variants or mutations located outside the coding regions might be involved in the molecular pathogenesis of PFH. We suggest a strategy based on whole-genome or targeted next generation sequencing to identify causative genes or variants for PFH.

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.

Targeted Metabolomics Analyses Reveal Specific Metabolic Alterations in High-Grade Prostate Cancer Patients

Prostate cancer (PCa) is a hormone-dependent tumor characterized by an extremely heterogeneous prognosis. Despite recent advances in partially uncovering some of the biological processes involved in its progression, there is still an urgent need for identifying more accurate and specific prognostic procedures to differentiate between disease stages. In this context, targeted approaches, focused on mapping dysregulated metabolic pathways, could play a critical role in identifying the mechanisms driving tumorigenesis and metastasis. In this study, a targeted analysis of the nuclear magnetic resonance-based metabolomic profile of PCa patients with different tumor grades, guided by transcriptomics profiles associated with their stages, was performed. Serum and urine samples were collected from 73 PCa patients. Samples were classified according to their Gleason score (GS) into low-GS (GS < 7) and high-GS PCa (GS ≥ 7) groups. A total of 36 metabolic pathways were found to be dysregulated in the comparison between different PCa grades. Particularly, the levels of glucose, glycine and 1-methlynicotinamide, metabolites involved in energy metabolism and nucleotide synthesis were significantly altered between both groups of patients. These results underscore the potential of targeted metabolomic profiling to characterize relevant metabolic changes involved in the progression of this neoplastic process.

Internal cross-linked polymeric nanoparticles with dual sensitivity for combination therapy of muscle-invasive bladder cancer

Background

Chemotherapy is a standard cancer treatment which uses anti-cancer drugs to destroy or slow the growth of cancer cells. However, chemotherapy has limited therapeutic effects in bladder cancer. One of the reasons of this resistance to chemotherapy is that higher levels of glutathione in invasive bladder cancer cells. We have fabricated nanoparticles that respond to high concentrations of glutathione and near-infrared laser irradiation in order to increase the drug accumulation at the tumor sites and combine chemotherapy with photothermal therapy to overcome the challenges of bladder cancer treatment.

Methods

The DOX&IR780@PEG-PCL-SS NPs were prepared by co-precipitation method. We investigated the tumor targeting capability of NPs in vitro and in vivo. The orthotopic bladder cancer model in C57BL/6 mice was established for in vivo study and the photothermal effects and therapeutic efficacy of NPs were evaluated.

Results

The DOX&IR780@PEG-PCL-SS NPs were synthesized using internal cross-linking strategy to increase the stability of nanoparticles. Nanoparticles can be ingested by tumor cells in a short time. The DOX&IR780@PEG-PCL-SS NPs have dual sensitivity to high levels of glutathione in bladder cancer cells and near-infrared laser irradiation. Glutathione triggers chemical structural changes of nanoparticles and preliminarily releases drugs, Near-infrared laser irradiation can promote the complete release of the drugs from the nanoparticles and induce a photothermal effect, leading to destroying the tumor cells. Given the excellent tumor-targeting ability and negligible toxicity to normal tissue, DOX&IR780@PEG-PCL-SS NPs can greatly increase the concentration of the anti-cancer drugs in tumor cells. The mice treated with DOX&IR780@PEG-PCL-SS NPs have a significant reduction in tumor volume. The DOX&IR780@PEG-PCL-SS NPs can be tracked by in vivo imaging system and have good tumor targeting ability, to facilitate our assessment during the experiment.

Conclusion

A nanoparticle delivery system with dual sensitivity to glutathione and near-infrared laser irradiation was developed for delivering IR780 and DOX. Chemo-photothermal synergistic therapy of both primary bladder cancer and their metastases was achieved using this advanced delivery system.

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.

Traditional Classification and Novel Subtyping Systems for Bladder Cancer

Bladder cancer is the most common tumor in the urinary system, with approximately 420,000 new cases and 160,000 deaths per year. The European Organization for Research and Treatment of Cancer (EOTRC) classifies non-muscular invasive bladder cancer (NMIBC) into low-risk, medium-risk and high-risk groups based on a comprehensive analysis of NMIBC pathological parameters and the risk of recurrence and progression to muscular invasive bladder cancer (MIBC). Traditional classification systems are based on pathologic grading, staging systems, and clinical prognosis. However, the pathological parameters of the tumor cannot fully reflect the “intrinsic characteristics” of bladder cancer, and tumors with a similar pathology exhibit different biological behaviors. Furthermore, although the traditional classification system cannot accurately predict the risk of recurrence or the progression of bladder cancer patients (BCs) individually, this method is widely used in clinical practice because of its convenient operation. With the development of sequencing and other technologies, the genetics-based molecular subtyping of bladder cancer has become increasingly studied. Compared with the traditional classification system, it provides more abundant tumor biological information and is expected to assist or even replace the traditional typing system in the future.

Urinary Metabolic Signatures Detect Recurrences in Non-Muscle Invasive Bladder Cancer

Patients with non-muscle invasive bladder cancer (NMIBC) undergo lifelong monitoring based on repeated cystoscopy and urinary cytology due to the high recurrence rate of this tumor. Nevertheless, these techniques have some drawbacks, namely, low accuracy in detection of low-grade tumors, omission of pre-neoplastic lesions and carcinomas in situ (CIS), invasiveness, and high costs. This work aims to identify a urinary metabolomic signature of recurrence by proton Nuclear Magnetic Resonance (¹H NMR) spectroscopy for the follow-up of NMIBC patients. To do this, changes in the urinary metabolome before and after transurethral resection (TUR) of tumors are analyzed and a Partial Least Square Discriminant Analysis (PLS-DA) model is developed. The usefulness of this discriminant model for the detection of tumor recurrences is assessed using a cohort of patients undergoing monitoring. The trajectories of the metabolomic profile in the follow-up period provide a negative predictive value of 92.7% in the sample classification. Pathway analyses show taurine, alanine, aspartate, glutamate, and phenylalanine perturbed metabolism associated with NMIBC. These results highlight the potential of ¹H NMR metabolomics to detect bladder cancer (BC) recurrences through a non-invasive approach.