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.

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

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

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.

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.

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.

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.

Serum and urine analysis with gold nanoparticle-assisted laser desorption/ionization mass spectrometry for renal cell carcinoma metabolic biomarkers discovery.

PURPOSE: Renal cell carcinoma (RCC) is a very aggressive and often fatal heterogeneous disease that is usually asymptomatic until late in the disease. There is an urgent need for RCC specific biomarkers that may be exploited clinically for diagnostic and prognostic purposes. MATERIALS/METHODS: Serum and urine samples were collected from patients with diagnosed kidney cancer and assessed with gold nanoparticle enhanced target (AuNPET) surface assisted-laser desorption/ionization mass spectrometry (SALDI MS) based metabolomics and statistical analysis. RESULTS: A database search allowed providing assignment of signals for the most promising features with a satisfactory value of the area under the curve and accuracy. Four potential biomarkers were found in urine and serum samples to distinguish clear cell renal cell carcinoma (ccRCC) from controls, 4 for the ccRCC with and without metastases, and 6 metabolites to distinguish low and high stages or grades. CONCLUSIONS: This pilot study suggests that serum and urine metabolomics based on AuNPET-LDI MS may be useful in distinguishing types, grades and stages of human RCC.

Metabolomic and elemental profiling of human tissue in kidney cancer.

INTRODUCTION: Kidney cancer is one of the most frequently diagnosed and the most lethal urinary cancer. Despite advances in treatment, no specific biomarker is currently in use to guide therapeutic interventions. OBJECTIVES: Major aim of this work was to perform metabolomic and elemental profiling of human kidney cancer and normal tissue and to evaluate cancer biomarkers. METHODS: Metabolic and elemental profiling of tumor and adjacent normal human kidney tissue from 50 patients with kidney cancer was undertaken using three different analytical methods. RESULTS: Five potential tissue biomarkers of kidney cancer were identified and quantified using with high-resolution nuclear magnetic resonance spectroscopy. The contents of selected chemical elements in tissues was analyzed using inductively coupled plasma optical emission spectrometry. Eleven mass spectral features differentiating between kidney cancer and normal tissues were detected using silver-109 nanoparticle enhanced steel target laser desorption/ionization mass spectrometry. CONCLUSIONS: Our results, derived from the combination of ICP-OES, LDI MS and 1H NMR methods, suggest that tissue biomarkers identified herein appeared to have great potential for use in clinical prognosis and/or diagnosis of kidney cancer.

Nuclear magnetic resonance and surface-assisted laser desorption/ionization mass spectrometry-based metabolome profiling of urine samples from kidney cancer patients.

Kidney cancer is one of the most frequently diagnosed cancers of the urinary tract in the world. Despite significant advances in kidney cancer treatment, no urine specific biomarker is currently used to guide therapeutic interventions. In an effort to address this knowledge gap, metabolic profiling of urine samples from 50 patients with kidney cancer and 50 healthy volunteers was undertaken using high-resolution proton nuclear magnetic resonance spectroscopy (1H NMR) and silver-109 nanoparticle enhanced steel target laser desorption/ionization mass spectrometry (109AgNPET LDI MS). Twelve potential urine biomarkers of kidney cancer were identified and quantified using one-dimensional (1D) 1H NMR metabolomics. Seven mass spectral features which differed significantly in abundance (p < 0.05) between kidney cancer patients and healthy volunteers were also detected using 109AgNPET-based laser desorption/ionization mass spectrometry (LDI MS). This work provides a framework to expand biomarker discovery that could be used as useful diagnostic or prognostic of kidney cancer progression.

Review on significance of GDNF, PTCH1 and RNF213 in chromophobe renal cell carcinoma illustrated by the case of 71-years-old man.

Here we review the role of GDNF, PTCH1, RNF213 illustrated by a case of renal cell carcinoma, chromophobe type (pT2a 8th pTNM edition) of the left kidney of 71-year-old man. Status of potential hotspots in 409 tumor genes were studied by means of next generation sequencing (NGS) technology (IonTorrent - Thermo Fisher Scientific, USA) using Ion AmpliSeq™ Comprehensive Cancer Panel. Next-generation sequencing (NGS) revealed mutations of GDNF (NM_001190468: c. 328C>T, p.R110W, allelic frequency 46%), PTCH1 (NM_001083607:c. 2969C

Screening of Urinary Renal Cancer Metabolic Biomarkers with Gold Nanoparticles-assisted Laser Desorption/Ionization Mass Spectrometry.

Renal cell carcinoma is a very aggressive and often fatal disease for which there are no specific biomarkers found to date. The purpose of this work was to find features that differentiate urine metabolic profiles of healthy people and cancer patients. Laser desorption/ionization mass spectrometry on gold nanostructures-based techniques were used for the metabolic analysis of urine of 50 patients with kidney cancer. Comparison with data from 50 healthy volunteers led to the discovery of several compounds that may be considered potential renal cell carcinoma (RCC) biomarkers. Statistical analysis of data allowed for the discovery of m/z values that had the greatest impact on group differentiation. A database search enabled the assignment of signals for the most promising 15 features among them: serine, heptanol, 3-methylene-indolenine, 2-methyl-3-hydroxy-5-formylpyridine-4-carboxylate, phosphodimethylethanolamine, 4-methoxyphenylacetic acid, N-acetylglutamine, 3,5-dihydroxyphenylvaleric acid, hydroxyhexanoylglycine, valyl-leucine, leucyl-histidine, oleamide, 9,12,13-trihydroxyoctadecenoic acid, stearidonyl carnitine and squalene. Differences of metabolite profiles of human urine could be identified by gold nanoparticle-enhanced target (AuNPET) LDI MS method and used for the detection of renal cancer.

Nuclear magnetic resonance and surface-assisted laser desorption/ionization mass spectrometry-based serum metabolomics of kidney cancer.

Kidney cancer is one of the most frequently diagnosed and the most lethal urinary cancer. Despite all the efforts made, no serum-specific biomarker is currently used in the clinical management of patients with this tumor. In this study, comprehensive high-resolution proton nuclear magnetic resonance spectroscopy (1H NMR) and silver-109 nanoparticle-enhanced steel target laser desorption/ionization mass spectrometry (109AgNPET LDI MS) approaches were conducted, in conjunction with multivariate data analysis, to discriminate the global serum metabolic profiles of kidney cancer (n = 50) and healthy volunteers (n = 49). Eight potential biomarkers have been identified using 1H NMR metabolomics and nine mass spectral features which differed significantly (p < 0.05) between kidney cancer patients and healthy volunteers, as observed by LDI MS. A partial least squares discriminant analysis (OPLS-DA) model generated from metabolic profiles obtained by both analytical approaches could robustly discriminate normal from cancerous samples (Q2 > 0.7), area under the receiver operative characteristic curve (ROC) AUC > 0.96. Compared with healthy human serum, kidney cancer serum had higher levels of glucose and lower levels of choline, glycerol, glycine, lactate, leucine, myo-inositol, and 1-methylhistidine. Analysis of differences between these metabolite levels in patients with different types and grades of kidney cancer was undertaken. Our results, derived from the combination of LDI MS and 1H NMR methods, suggest that serum biomarkers identified herein appeared to have great potential for use in clinical prognosis and/or diagnosis of kidney cancer. Graphical abstract.

Localization of Metabolites of Human Kidney Tissue with Infrared Laser-Based Selected Reaction Monitoring Mass Spectrometry Imaging and Silver-109 Nanoparticle-Based Surface Assisted Laser Desorption/Ionization Mass Spectrometry Imaging.

Infrared (IR) laser ablation-remote-electrospray ionization (LARESI) platform coupled to a tandem mass spectrometer (MS/MS) operated in selected reaction monitoring (SRM) or multiple reaction monitoring (MRM) modes was developed and employed for imaging of target metabolites in human kidney cancer tissue. SRM or MRM modes were employed to avoid artifacts that are present in full scan MS mode. Four tissue samples containing both cancerous and noncancerous regions, obtained from three patients with renal cell carcinoma (RCC), were imaged. Sixteen endogenous metabolites that were reported in the literature as varying in abundance between cancerous and noncancerous areas in various human tissues were selected for analysis. Target metabolites comprised ten amino acids, four nucleosides and nucleobases, lactate, and vitamin E. For comparison purposes, images of the same metabolites were obtained with ultraviolet (UV) desorption/ionization mass spectrometry imaging (UV-LDI-MSI) using monoisotopic silver-109 nanoparticle-enhanced target (109AgNPET) in full-scan MS mode. The acquired MS images revealed differences in abundances of selected metabolites between cancerous and noncancerous regions of the kidney tissue. Importantly, the two imaging methods offered similar results. This study demonstrates the applicability of the novel ambient LARESI SRM/MRM MSI method to both investigating and discovering cancer biomarkers in human tissue.

Contemporary treatment of metastatic renal cell carcinoma.

Renal cell carcinoma is the 14th most common cancer worldwide. It is a heterogeneous group of histopathological entities, of which the most common is clear cell renal cell carcinoma. Approximately 20-30% of patients present initially with metastatic disease and an additional 20% will progress after radical surgical treatment. Metastatic disease that is non-feasible for surgical treatment remains incurable. Numerous studies have demonstrated that-with the introduction of new drugs-the treatment outcomes of metastatic disease have improved. The development of new therapies as well as the optimization and individualization of procedures allow us to hope for further progress in this area.

Metabolomic study of human tissue and urine in clear cell renal carcinoma by LC-HRMS and PLS-DA.

Renal cell carcinoma (RCC) is the most prevalent and lethal malignancy of the kidney. Despite all the efforts made, no tissue biomarker is currently used in the clinical management of patients with kidney cancer. A search for possible biomarkers in urine for clear cell renal cell carcinoma (ccRCC) has been conducted. Non-targeted metabolomic analyses were performed on paired samples of surgically removed renal cancer and normal tissue, as well as on urine samples. Extracts were analyzed by liquid chromatography/high-resolution mass spectrometry (LC-HRMS). Hydroxybutyrylcarnitine, decanoylcarnitine, propanoylcarnitine, carnitine, dodecanoylcarnitine, and norepinephrine sulfate were found in much higher concentrations in both cancer tissues (compared with the paired normal tissue) and in urine of cancer patients (compared with control urine). In contrast, riboflavin and acetylaspartylglutamate (NAAG) were present at significantly higher concentrations both in normal kidney tissue as well as in urine samples of healthy persons. This preliminary study resulted in the identification of several compounds that may be considered potential clear cell renal carcinoma biomarkers. Graphical abstract PLS-DA plot based on LC-MS data for normal and cancer human tissue samples. The aim of this work was the identification of up- and downregulated compounds that could potentially serve as renal cancer biomarkers.

Laser desorption/ionization MS imaging of cancer kidney tissue on silver nanoparticle-enhanced target.

AIM: Renal cell carcinoma is a very aggressive and often fatal disease for which there are no specific biomarkers found to date. The purpose of work was to find substances that differentiate the cancerous and healthy tissue by using laser desorption/ionization MS imaging combined with silver nanoparticle-enhanced target. RESULTS: Ion images and comparative analysis of spectra revealed differences in intensities for several metabolites, for which their biochemical properties were discussed. Statistical analysis allowed to distinguish healthy and cancer tissue without the involvement of a pathologist. CONCLUSION: Laser desorption/ionization MS imaging technology combined with silver nanoparticle-enhanced target enabled rapid visualization of the differences between the clear cell renal cell carcinoma and the healthy part of the kidney tissue.

Surface-Transfer Mass Spectrometry Imaging of Renal Tissue on Gold Nanoparticle Enhanced Target.

Renal cell carcinoma (RCC) accounts for several percent of all adult malignant tumor cases and is directly associated with over 120 thousand death cases worldwide annually. Therefore, there is a need for cancer biomarker tests and methods capable of discriminating between normal and malignant tissue. It is demonstrated that gold nanoparticle enhanced target (AuNPET), a nanoparticle-based, surface-assisted laser desorption/ionization (SALDI)-type mass spectrometric method for analysis and imaging, can differentiate between normal and cancerous renal tissue. Diglyceride DG(18:1/20:0)-sodium adduct and protonated octadecanamide ions were found to have greatly elevated intensities in cancerous part of analyzed tissue specimen. Compounds responsible for mentioned ions formation were pointed out as a potential clear cell RCC biomarkers. Their biological properties and localization on the tissue surface are also discussed. Potential application of presented results may also facilitate clinical decision making during surgery for large renal masses.

The impact of physical training on endothelial function in myocardial infarction survivors: pilot study.

BACKGROUND: Endothelial dysfunction (ED) may indirectly influence the outcome of patients with coronary artery disease. AIM: To assess the influence of cardiac rehabilitation (CR) on endothelial function in patients after ST-segment elevation myocardial infarction (STEMI). METHODS: Twenty-nine patients scheduled for CR were included in the study. CR began at least four weeks after STEMI and consisted of 12 or 24 training sessions. Endothelial function assessment was performed before and after CR, using reactive hyperaemia peripheral arterial tonometry. RESULTS: Before the CR, ED was diagnosed in 16 of 29 (55.2%) patients. A total of 25 patients had two assessments of endothelial function: before and after CR. In univariate analysis the factors of negative response of endothelial function to CR were: higher baseline hyperaemia index (lnRHI) (odds ratio [OR] for positive response to CR 0.01; 95% confidence interval [CI] 0.00-0.33; p = 0.01) and higher peak serum troponin I level during index hospitalisation (OR 0.97; 95% CI 0.94-1.00; p = 0.04). The independent, negative predictor of response to CR was lnRHI (OR 0.01; 95% CI 0.01-0.16; p = 0.03). Patients training for 24 sessions (n = 16) had similar lnRHI changes to those of patients training for 12 sessions (n = 9); [0.16 (-0.06)-0.30 vs. 0.10 (0.05-0.15); p = 0.44, respectively]. CONCLUSIONS: ED is a frequent abnormality in STEMI survivors. Despite the lack of statistically significant improvement of endothelial function after CR in the analysed group of patients, some factors can influence the efficacy of this type of physical activity. The best effect of CR on endothelial function was observed in patients with baseline ED.