NMR-derived targeted serum metabolic biomarkers appraisal of bladder cancer: A pre- and post-operative evaluation

Current look at the most promising proteomic and glycomic biomarkers of bladder cancer

BACKGROUND

Bladder cancer (BC) belongs to the most frequent cancer types. The diagnostic process is still long and costly, with a high percentage of false-positive or -negative results. Due to the cost and lack of effectiveness, older methods need to be supplemented or replaced by a newer more reliable method. In this regard, proteins and glycoproteins pose high potential.

METHODS

We performed an online search in PubMed/Medline, Scopus, and Web of Science databases to find relevant studies published in English up until May 2023. If applicable, we set the AUC threshold to 0.90 and sensitivity/specificity (SN/SP) to 90%.

FINDINGS

Protein and glycoprotein biomarkers are a demonstrably viable option in BC diagnostics. Cholinesterase shows promise in progression-free survival. BLCA-4, ORM-1 along with HTRA1 in the detection of BC. Matrix metallopeptidase 9 exhibits potential for stratification of muscle-invasive subtypes with high negative predictive value for aggressive phenotypes. Distinguishing non-muscle invasive subtypes benefits from Keratin 17. Neu5Gc-modified UMOD glycoproteins pose potential in BC diagnosis, while fibronectin, laminin-5, collagen type IV, and lamprey immunity protein in early detection of BC.

Metabolomic biomarkers in liquid biopsy: accurate cancer diagnosis and prognosis monitoring

Liquid biopsy, a novel detection method, has recently become an active research area in clinical cancer owing to its unique advantages. Studies on circulating free DNA, circulating tumor cells, and exosomes obtained by liquid biopsy have shown great advances and they have entered clinical practice as new cancer biomarkers. The metabolism of the body is dynamic as cancer originates and progresses. Metabolic abnormalities caused by cancer can be detected in the blood, sputum, urine, and other biological fluids via systemic or local circulation. A considerable number of recent studies have focused on the roles of metabolic molecules in cancer. The purpose of this review is to provide an overview of metabolic markers from various biological fluids in the latest clinical studies, which may contribute to cancer screening and diagnosis, differentiation of cancer typing, grading and staging, and prediction of therapeutic response and prognosis.

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.

Malonate differentially affects cell survival and confers chemoresistance in cancer cells via the induction of p53-dependent autophagy

Metabolic network intertwines with cancerous signaling and drug responses. Malonate is a prevailing metabolite in cancer and a competitive inhibitor of succinate dehydrogenase (SDH). Recent studies showed that malonate induced reactive oxygen species (ROS)-dependent apoptosis in neuroblastoma cells, but protected cells from ischemia-reperfusion injury. We here revealed that malonate differentially regulated cell death and survival in cancer cells. While high-dose malonate triggered ROS-dependent apoptosis, the low-dose malonate induced autophagy and conferred resistance to multiple chemotherapeutic agents. Mechanistically, our results showed that malonate increased p53 stability and transcriptionally up-regulated autophagy modulator DRAM (damage-regulated autophagy modulator), thus promoting autophagy. We further proved that autophagy is required for malonate-associated chemoresistance. Collectively, our findings suggest that malonate plays a double-edge function in cancer response to stressors, and highlights a pro-cancer impact of p53-induced autophagy in response to malonate.

Metabolomic Signatures of Treatment Response in Bladder Cancer

Bladder cancer (BC) stands as one of the most prevalent urological malignancies, with over 500 thousand newly diagnosed cases annually. Treatment decisions in BC depend on factors like the risk of recurrence, the type of tumor, and the stage of the disease. While standard therapeutic approaches encompass transurethral resection of the bladder tumor, radical cystectomy, and chemo- or immunotherapy, these methods exhibit limited efficacy in mitigating the aggressive and recurrent nature of bladder tumors. To overcome this challenge, it is crucial to develop innovative methods for monitoring and predicting treatment responses among patients with BC. Metabolomics is gaining recognition as a promising approach for discovering biomarkers. It has the potential to reveal metabolic disruptions that precisely reflect how BC patients respond to particular treatments, providing a revolutionary method to improve accuracy in monitoring and predicting outcomes. In this article, we present a comprehensive review of studies employing metabolomics approaches to investigate the metabolic responses associated with different treatment modalities for BC. The review encompasses an exploration of various models, samples, and analytical techniques applied in this context. Special emphasis is placed on the reported changes in metabolite levels derived from these studies, highlighting their potential as biomarkers for personalized medicine in BC.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSION

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

Causal associations of histidine and 12 site-specific cancers: a bidirectional Mendelian randomization study

An increasing number of studies indicate that cancer patients' histidine (HIS) circulating levels have changed. However, the causality between HIS and cancer is still not well established. Thus, to ascertain the causal link between HIS and cancers, we performed a bidirectional Mendelian randomization (MR) analysis. Summary-level data are derived from publicly available genome-wide association studies (GWAS). The causal effects were mainly estimated using the inverse-variance weighted method (IVW). The weighted-median (WM) method and MR-Egger regression were conducted as sensitivity analyses. In the forward-MR, we found malignant neoplasm of respiratory system and intrathoracic organs (OR: 1.020; 95% CI: 1.006-1.035; pIVW = 0.007) genetically associated with circulating HIS. And there was no significant genetic correlation between HIS and another 11 site-specific cancers using IVW method. In the reversed-MR, we did not observe the causal relationship between HIS and 12 site-specific cancers. Our findings help clarify that HIS, as a biomarker for malignant neoplasms of respiratory system and intrathoracic organs, is causal rather than a secondary biomarker of the cancerous progression. The mechanism between histidine and cancer progression deserves further investigation.

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.

Untargeted ultra-high-resolution mass spectrometry metabolomic profiling of blood serum in bladder cancer

Bladder cancer (BC) is a common urological cancer of high mortality and recurrence rates. Currently, cystoscopy is performed as standard examination for the diagnosis and subsequent monitoring for recurrence of the patients. Frequent expensive and invasive procedures may deterrent patients from regular follow-up screening, therefore it is important to look for new non-invasive methods to aid in the detection of recurrent and/or primary BC. In this study, ultra-high-performance liquid chromatography coupled with ultra-high-resolution mass spectrometry was employed for non-targeted metabolomic profiling of 200 human serum samples to identify biochemical signatures that differentiate BC from non-cancer controls (NCs). Univariate and multivariate statistical analyses with external validation revealed twenty-seven metabolites that differentiate between BC patients from NCs. Abundances of these metabolites displayed statistically significant differences in two independent training and validation sets. Twenty-three serum metabolites were also found to be distinguishing between low- and high-grade of BC patients and controls. Thirty-seven serum metabolites were found to differentiate between different stages of BC. The results suggest that measurement of serum metabolites may provide more facile and less invasive diagnostic methodology for detection of bladder cancer and recurrent disease management.

The Role of Novel Bladder Cancer Diagnostic and Surveillance Biomarkers-What Should a Urologist Really Know?

The aim of this review is to analyze and describe the current landscape of bladder cancer diagnostic and surveillance biomarkers. We researched the literature from 2016 to November 2021 to find the most promising new molecules and divided them into seven different subgroups based on their function and location in the cell. Although cystoscopy and cytology are still the gold standard for diagnosis and surveillance when it comes to bladder cancer (BCa), their cost is quite a burden for national health systems worldwide. Currently, the research is focused on finding a biomarker that has high negative predictive value (NPV) and can exclude with a certainty the presence of the tumor, considering missing it could be disastrous for the patient. Every subgroup has its own advantages and disadvantages; for example, protein biomarkers cost less than genomic ones, but on the other hand, they seem to be less precise. We tried to simplify this complicated topic as much as possible in order to make it comprehensible to doctors and urologists that are not as familiar with it, as well as encourage them to actively participate in ongoing research.

Proteomic Profiling of Plasma-Derived Biomarkers in Patients with Bladder Cancer: A Step towards Clinical Translation

Background: Bladder cancer is a life-threatening disease and a major cause of cancer-associated complications. The main challenges confronted during the clinical management of bladder cancer are associated with recurrence and disease progression to the muscle-invasive phenotype. Improved early detection of the disease is of paramount importance to prevent disease progression and improve survival. Hence, novel clinically applicable biomarkers for early detection are warranted. Methods: In the current study, a comparative proteomic approach was undertaken using plasma samples to identify protein biomarkers associated with the muscle-invasive phenotype of bladder carcinoma. Isolated plasma proteins were depleted, DIGE-labeled, then subjected to conventional 2D electrophoresis followed by mass spectrometry for identification of differentially expressed proteins. Western blot was used for data validation. Results: Fourteen differentially expressed proteins with statistically significant changes in abundance between the cancer group and control group were identified. Three differentially expressed proteins were selected for validation, among which apolipoprotein A1 exhibited high specificity and sensitivity (AUC = 0.906). Ingenuity pathway analysis identified IFN-γ and TNF-α as the main signaling hub for the differentially regulated proteins. Conclusion: Our findings provide additional insight into understanding bladder cancer pathogenesis. Our data identified potential non-invasive plasma-derived biomarker proteins that merit additional investigation to validate its clinical usefulness to prevent bladder cancer progression.