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.

Binding indocyanine green to human serum albumin potentially enhances the detection of sentinel lymph nodes. An initial step for facilitating the detection of first-station nodes in penile and other urological cancers.

Introduction: Surgical oncology strives to remove the primary cancer tumor together with its local lymphatic tissue. One of the techniques improving the staging of lymph nodes is sentinel node biopsy. The most common agent used in SNB is indocyanine green (ICG). Indocyanine green is characterized by its high affinity for human serum albumin (HSA). In practice, the visualization of the sentinel node is enhanced by attaching a relatively large carrier to the ICG molecule. The aim of this study was to investigate whether the covalent linking of ICG to a nanocolloid would extend the time of detection of the dye as it binds to HSA, assessed by fluorescence measurements in vitro. Material and methods: The influence of the molar concentration of ICG on its ability to form a complex with HSA was investigated. The dye luminescence was measured, with an increasing amount of dye in the presence of a constant concentration of HSA. The stability of the ICG:HSA complex was also investigated. Results: The binding of ICG and human protein in a solution ratio of 3 : 1 made it possible to detect the ICG luminescence with better and prolonged visibility. In the case of the two lowest ratios, complex formation was not observed. The use of ICG bound to a nanocolloid based on human serum albumin increases the luminescence of the HSA : ICG complex up to 98%. Conclusions: Properly selected proportions of human albumin protein and ICH allowed higher and longer luminescence to be achieved. Nevertheless, further studies are necessary to establish the optimal concentration ratio.

Pre- and Post-Resection Urine Metabolic Profiles of Bladder Cancer Patients: Results of Preliminary Studies on Time Series Metabolomics Analysis.

The incidence of bladder cancer (BCa) has remained high for many years. Nevertheless, its pathomechanism has not yet been fully understood and is still being studied. Therefore, multiplatform untargeted urinary metabolomics analysis has been performed in order to study differences in the metabolic profiles of urine samples collected at three time points: before transurethral resection of bladder tumor (TURBT), the day after the procedure and two weeks after TURBT. Collected samples were analyzed with the use of high-performance liquid chromatography hyphenated with time-of-flight mass spectrometry detection (HPLC-TOF/MS) and gas chromatography coupled with triple quadrupole mass spectrometry detection (GC-QqQ/MS, in a scan mode). Levels of metabolites selected in our previous study were assessed in order to confirm their potential to differentiate the healthy and diseased samples, regardless of the risk factors and individual characteristics. Hippuric acid, pentanedioic acid and uridine confirmed their potential for sample differentiation. Based on the results of statistical analysis for the paired samples (comparison of metabolic profiles of samples collected before TURBT and two weeks after), a set of metabolites belonging to nucleotide metabolism and methylation processes was also selected. Longitudinal studies proved to be useful for the evaluation of metabolic changes in bladder cancer.

Untargeted Metabolomics Study of Three Matrices: Seminal Fluid, Urine, and Serum to Search the Potential Indicators of Prostate Cancer.

The simultaneous determination of metabolites from biological fluids may provide more accurate information about the current body condition. So far, the metabolomics approach has been successfully applied to study the mechanism of several disorders and to search for novel biomarkers. Urine and plasma are widely accepted matrices for the evaluation of several pathologies, while prostate cancer (CaP) development is still unknown. For this reason, an alternative matrix, the seminal fluid, was proposed to expand the knowledge about the CaP pathomechanism. The main aim of this study was to develop and optimize the sample preparation protocol to ensure the highest coverage of the metabolome of ejaculate samples. Parameters like the type and composition of the solvent mixture, time of extraction, and applied volume of the solvent were tested. The optimized method was applied for the untargeted metabolomics profiling of seminal fluid samples obtained from CaP patients. Moreover, urine and serum samples were also prepared for untargeted metabolomics analysis. Analyses were carried out with the use of two complementary analytical techniques: GC-EI-QqQ/MS and LC-ESI-TOF/MS. Finally, the metabolic signature of seminal fluid (n = 7), urine (n = 7), and plasma (n = 7) samples was compared. Furthermore, the hypothesis of the increased level of metabolites in ejaculate samples related to the CaP development was evaluated. The results indicated that the developed and optimized sample preparation protocol for seminal fluid may be successfully applied for metabolomics study. Untargeted analysis of ejaculate enabled to determine the following classes of compounds: fatty acids, sphingolipids, phospholipids, sugars, and their derivatives, as well as amino acids. Finally, a comparison of the three tested matrices was carried out. To our best knowledge, it is the first time when the metabolic profile of the three matrices, namely, urine, plasma, and seminal fluid, was compared. Based on the results, it can be pointed out that ejaculate comprises the metabolic signature of both matrices (polar compounds characteristic for urine, and non-polar ones present in plasma samples). Compared to plasma, semen samples revealed to have a similar profile; however, determined levels of metabolites were lower in case of ejaculate. In case of urine samples, compared to semen metabolic profiles, the levels of detected metabolites were decreased in the latter ones.

The potential role of fatty acids in prostate cancer determined by GC-MS analysis of formalin-fixed paraffin-embedded tissue samples.

Prostate cancer (PCa) is one of the leading types of cancer in men. Although the diagnosis of this disease is currently quite effective, there is still a need to search for noninvasive diagnostic and monitoring methods. Consequently, identifying the mechanisms underlying the development and progression of PCa is crucial. It has been confirmed that the hallmarks of PCa include changes in metabolism, particularly that of fatty acids. Therefore, the application of lipidomics with an accurate histopathological assessment can provide the necessary information and reveal the metabolites that are characteristic of the disease. The use of formalin-fixed, paraffin-embedded (FFPE) tissue samples as an alternative matrix in retrospective research makes this approach highly innovative. The main goal of this study was to perform an untargeted lipidomic analysis of FFPE PCa tissue samples (n = 52) using gas chromatography coupled with mass spectrometry (GC-MS), in comparison to controls (n = 50). To our knowledge, this study is the first to simultaneously conduct a metabolic analysis and histopathological assessment. In the latter, the samples were evaluated based on Gleason grading score and pTNM stage. The obtained results were evaluated by univariate (Student's t-test or Mann-Whitney U-test) as well as multivariate statistical analysis (principal component analysis, partial least squares-discriminant analysis, variable importance into projection, and selectivity ratio) in order to select the metabolites with the most discriminative power. Additionally, the correlation between the level of metabolites and pathological characteristics was determined. The results of the analyses confirmed the changes in the lipid metabolism pathway in PCa. It can be assumed that PCa is linked with elevated de novo biosynthesis of steroid hormone-related fatty acids and beta-oxidation of fatty acids. An increased level of three fatty acids, namely 9-octadecanoic acid, 9,12-octadecadienoic acid, and 5, 8, 1,14-eicosatetraenoic acid, was observed in the PCa samples. These fatty acids were assigned as metabolites with the best discriminative power for the two tested groups. In practice, these compounds could be considered as specific biochemical factors that may be implemented in the diagnosis of PCa, but their significance should be validated on a more extensive set of samples. Undoubtedly, these results are valuable as they provide important information on prostate cancerogenesis in the context of a metabolic switch.

GC-MS-based untargeted metabolomics of plasma and urine to evaluate metabolic changes in prostate cancer.

Prostate cancer (CaP) is a common cancer in men. Its late detection and inefficient diagnosis are a challenge for researchers who are currently searching for new cancer-related indicators that would facilitate better detectability of CaP and explain its pathogenesis. In the present preliminary study, endogenous volatile metabolites were detected in plasma and urine samples by using the metabolic fingerprinting approach. The analyses were performed using the GC-QqQ/MS technique in the scan mode. The detected and putatively identified metabolites were statistically analyzed using advanced univariate and multivariate statistical methods. Eleven urinary and three plasma metabolites were selected as statistically significant in patients with CaP as compared to those in healthy controls. Supervised methods such as logistic regression and quadratic support vector machine were applied to obtain the classification models. The accuracy, sensitivity, and specificity of the models were above 83%, 85%, and 81%, respectively. The putatively identified metabolites were associated with biochemical pathways such as tricarboxylic acid cycle, glycolysis, carbohydrate conversion, and steroidal lipid metabolism that are mainly involved in energy production for cell growth and proliferation.