SPME-LC/MS-based serum metabolomic phenotyping for distinguishing ovarian cancer histologic subtypes: a pilot study
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ABSTRACT:
SUBMITTER: Olkowicz M
PROVIDER: S-EPMC8599860 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Renal cell carcinomas (RCC) are classified according to their histological features. Accurate classification of RCC and comprehensive understanding of their metabolic dysregulation are of critical importance. Here we investigate the use of metabolomic analyses to classify the main RCC subtypes and to describe the metabolic variation for each subtype. To this end, we performed metabolomic profiling of 65 RCC frozen samples (40 clear cell, 14 papillary and 11 chromophobe) using liquid chromatography-mass spectrometry. OPLS-DA multivariate analysis based on metabolomic data showed clear discrimination of all three main subtypes of RCC (R2?=?75.0%, Q2?=?59.7%). The prognostic performance was evaluated using an independent cohort and showed an AUROC of 0.924, 0.991 and 1 for clear cell, papillary and chromophobe RCC, respectively. Further pathway analysis using the 21 top metabolites showed significant differences in amino acid and fatty acid metabolism between three RCC subtypes. In conclusion, this study shows that metabolomic profiling could serve as a tool that is complementary to histology for RCC subtype classification. An overview of metabolic dysregulation in RCC subtypes was established giving new insights into the understanding of their clinical behaviour and for the development of targeted therapeutic strategies.
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Project description:Ovarian carcinosarcoma is a rare subtype of this disease that has not been thoroughly investigated. The aim of this study was to evaluate the prognostic factors and out comes in patients with ovarian carcinosarcoma.All patients with histologically confirmed ovarian carcinosarcoma who were treated at Cheil General Hospital and Women's Healthcare Center between January 2000 and December 2015 were identified and analyzed. Data were extracted from medical records, and statistical analyses were performed to determine correlations between clinicopathological parameters and survival outcomes.Of the 822 patients diagnosed with ovarian cancer over 16 years, 11 (1.3%) had ovarian carcinosarcoma histology. Every patient underwent surgery as the initial treatment followed by intravenous adjuvant chemotherapy. Only 18.1% of cases were early stage (I or II) while 81.8% were advanced stage (III or IV) according to the FIGO (International Federation of Gynecology and Obstetrics) classification. Six cases were of the homologous subtype (54.5%) and five were of the heterologous subtype (45.5%). There was no significant difference in survival according to stage (P=0.24). The heterologous subtype and residual disease were associated with poor disease-free survival (P=0.02 and P=0.04) and overall survival (P=0.02 and P=0.04), On multivariate analysis, the histological subtype was an independent prognostic factor (P=0.02).Optimal cytoreduction without gross residual disease and a homologous subtype are favorable prognostic factors in terms of disease relapse and survival.
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Project description:Carbonyl-containing metabolites widely exist in biological samples and have important physiological functions. Thus, accurate and sensitive quantitative analysis of carbonyl-containing metabolites is crucial to provide insight into metabolic pathways as well as disease mechanisms. Although reversed phase liquid chromatography electrospray ionization mass spectrometry (RPLC-ESI-MS) is widely used due to the powerful separation capability of RPLC and high specificity and sensitivity of MS, but it is often challenging to directly analyze carbonyl-containing metabolites using RPLC-ESI-MS due to the poor ionization efficiency of neutral carbonyl groups in ESI. Modification of carbonyl-containing metabolites by a chemical derivatization strategy can overcome the obstacle of sensitivity; however, it is insufficient to achieve accurate quantification due to instrument drift and matrix effects. The emergence of stable isotope-coded derivatization (ICD) provides a good solution to the problems encountered above. Thus, LC-MS methods that utilize ICD have been applied in metabolomics including quantitative targeted analysis and untargeted profiling analysis. In addition, ICD makes multiplex or multichannel submetabolome analysis possible, which not only reduces instrument running time but also avoids the variation of MS response. In this review, representative derivatization reagents and typical applications in absolute quantification and submetabolome profiling are discussed to highlight the superiority of the ICD strategy for detection of carbonyl-containing metabolites.
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Project description:Metabolomic analysis by liquid chromatography-high-resolution mass spectrometry results in data sets with thousands of features arising from metabolites, fragments, isotopes, and adducts. Here we describe a software package, Metabolomic Analysis and Visualization ENgine (MAVEN), designed for efficient interactive analysis of LC-MS data, including in the presence of isotope labeling. The software contains tools for all aspects of the data analysis process, from feature extraction to pathway-based graphical data display. To facilitate data validation, a machine learning algorithm automatically assesses peak quality. Users interact with raw data primarily in the form of extracted ion chromatograms, which are displayed with overlaid circles indicating peak quality, and bar graphs of peak intensities for both unlabeled and isotope-labeled metabolite forms. Click-based navigation leads to additional information, such as raw data for specific isotopic forms or for metabolites changing significantly between conditions. Fast data processing algorithms result in nearly delay-free browsing. Drop-down menus provide tools for the overlay of data onto pathway maps. These tools enable animating series of pathway graphs, e.g., to show propagation of labeled forms through a metabolic network. MAVEN is released under an open source license at http://maven.princeton.edu.
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Project description:Objective: Hypopituitarism (Hypo-Pit) is partial or complete insufficiency of anterior pituitary hormones. Besides hormone metabolism, the global metabolomics in Hypo-Pit are largely unknown. We aimed to explore potential biomarkers to aid in diagnosis and personalized treatment. Methods: Using both univariate and multivariate statistical methods, we identified 72 differentially abundant features through liquid chromatography coupled to high-resolution mass spectrometry, obtained in 134 males with Hypo-Pit and 90 age matched healthy controls. Results: Hypopituitarism exhibits an increased abundance of metabolites involved in amino acid degradation and glycerophospholipid synthesis, but decreased content of metabolites in steroid hormone synthesis and fatty acid beta-oxidation. Significantly changed metabolites included creatine, creatinine, L-alanine, phosphocholines, androstenedione, hydroprenenolone, and acylcarnitines. In Hypo-Pit patients, the increased ratio of creatine/creatinine suggested reduced creatine uptake and impaired creatine utilization, whereas the decreased level of beta-hydroxybutyrate, acetylcarnitine (C2) and a significantly decreased ratio of decanoylcarnitine (C10) to free carnitine suggested an impaired beta-oxidation. Furthermore, the creatine/creatinine and decanoylcarnitine/carnitine ratio were identified as diagnostic biomarkers for Hypo-Pit with AUCs of 0.976 and 0.988, respectively. Finally, we found that the creatinine and decanoylcarnitine/carnitine ratio could distinguish cases that were sensitive vs. resistant to human chorionic gonadotropin therapy. Conclusion: We provided a global picture of altered metabolic pathways in Hypo-Pit, and the identified biomarkers in creatine metabolism and beta-oxidation might be useful for the preliminary screening and diagnosis of Hypo-Pit.
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Project description:The objective of the present work was to develop an advanced fast phenotyping tool to explore the cashew apple compositions from different genotypes, based on a portable near-infrared (MicroNIR) spectroscopy. This will be in addition to associating the variability of the respective cashew apple pulps with the genotypes by ultra-performance liquid chromatography (UPLC), coupled with high-resolution mass spectrometry (HRMS). The NIR analysis is a non-destructive, low-cost procedure that provides prompt results, while considering the morphology of different cashew apples (shape, size, and color). The UPLC-HRMS analysis is characterized by specific bioactive compounds, such as the derivatives of hydroxybutanoic acid, galloyl, and flavonoids. Furthermore, both techniques allowed the identification of a group of accessions, which presented similarities among the chemical profiling. However, to improve the understanding of cashew chemical and physical variability, further variables related to the cashew apple composition, such as edaphoclimatic conditions, should be considered for future studies. These approaches lead to the conclusion that these two tools are useful for the maintenance of BAG-Caju (Cashew Germplasm Bank) and for the cashew-breeding program.
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Project description:BackgroundThe analysis of LC-MS metabolomic datasets appears to be a challenging task in a wide range of disciplines since it demands the highly extensive processing of a vast amount of data. Different LC-MS data analysis packages have been developed in the last few years to facilitate this analysis. However, most of these strategies involve chromatographic alignment and peak shaping and often associate each "feature" (i.e., chromatographic peak) with a unique m/z measurement. Thus, the development of an alternative data analysis strategy that is applicable to most types of MS datasets and properly addresses these issues is still a challenge in the metabolomics field.ResultsHere, we present an alternative approach called ROIMCR to: i) filter and compress massive LC-MS datasets while transforming their original structure into a data matrix of features without losing relevant information through the search of regions of interest (ROIs) in the m/z domain and ii) resolve compressed data to identify their contributing pure components without previous alignment or peak shaping by applying a Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS) analysis. In this study, the basics of the ROIMCR method are presented in detail and a detailed description of its implementation is also provided. Data were analyzed using the MATLAB (The MathWorks, Inc., www.mathworks.com ) programming and computing environment. The application of the ROIMCR methodology is described in detail, with an example of LC-MS data generated in a lipidomic study and with other examples of recent applications.ConclusionsThe methodology presented here combines the benefits of data filtering and compression based on the searching of ROI features, without the loss of spectral accuracy. The method has the benefits of the application of the powerful MCR-ALS data resolution method without the necessity of performing chromatographic peak alignment or modelling. The presented method is a powerful alternative to other existing data analysis approaches that do not use the MCR-ALS method to resolve LC-MS data. The ROIMCR method also represents an improved strategy compared to the direct applications of the MCR-ALS method that use less-powerful data compression strategies such as binning and windowing. Overall, the strategy presented here confirms the usefulness of the ROIMCR chemometrics method for analyzing LC-MS untargeted metabolomics data.
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