Gas chromatography-mass spectrometry untargeted profiling of non-Hodgkin’s lymphoma urinary metabolite markers
Autor: | Ana Valéria Colnaghi Simionato, Vladmir Cláudio Cordeiro de Lima, Alan Gonçalves Amaral, Jayr Schmidt-Filho, Anna Maria A. P. Fernandes, Hans Rolando Zamora-Obando, Gustavo Henrique Bueno Duarte, Andreia M Porcari, Felipe D'Almeida Costa, Alex Aparecido Rosini Silva, Victor P. Andrade, Alessandra de Sousa Mesquita, Marcos N. Eberlin |
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Rok vydání: | 2020 |
Předmět: |
Adult
Male Metabolite Urinary system 02 engineering and technology Urine 01 natural sciences Biochemistry Gas Chromatography-Mass Spectrometry Analytical Chemistry chemistry.chemical_compound Metabolomics hemic and lymphatic diseases Biomarkers Tumor medicine Humans Aged Receiver operating characteristic business.industry Lymphoma Non-Hodgkin 010401 analytical chemistry Middle Aged 021001 nanoscience & nanotechnology medicine.disease 0104 chemical sciences Lymphoma Non-Hodgkin's lymphoma chemistry Metabolome Cancer research Female Gas chromatography–mass spectrometry 0210 nano-technology business |
Zdroj: | Analytical and Bioanalytical Chemistry. 412:7469-7480 |
ISSN: | 1618-2650 1618-2642 |
DOI: | 10.1007/s00216-020-02881-5 |
Popis: | Non-Hodgkin's lymphoma (NHL) is a cancer of the lymphatic system where the lymphoid and hematopoietic tissues are infiltrated by malignant neoplasms of B, T, and natural killer lymphocytes. Effective and less invasive methods for NHL screening are urgently needed. Herein, we report an untargeted gas chromatography-mass spectrometry (GC-MS) method to investigate metabolic changes in non-volatile derivatized compounds from urine samples of NHL patients (N = 15) and compare them to healthy controls (N = 34). Uni- and multivariate data analysis showed 18 endogenous metabolites, including amino acids and their metabolites, sugars, small organic acids, and vitamins, as statistically significant for group differentiation. A receiver operating characteristic curve (ROC) generated from a support vector machine (SVM) algorithm-based model achieved 0.998 of predictive accuracy, displaying the potential and relevance of GC-MS-detected urinary non-volatile compounds for predictive purposes. Furthermore, a specific panel of key metabolites was also evaluated, showing similar results. All in all, our results indicate that this robust GC-MS method is an effective screening tool for NHL diagnosis and it is able to highlight different pathways of the disease. Graphical Abstract. |
Databáze: | OpenAIRE |
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