Gas chromatography-mass spectrometry untargeted profiling of non-Hodgkin’s lymphoma urinary metabolite markers
Tipo
Artigo
Data de publicação
2020
Periódico
Analytical and Bioanalytical Chemistry
Citações (Scopus)
8
Autores
Bueno Duarte G.H.
de Piloto Fernandes A.M.A.
Silva A.A.R.
Zamora-Obando H.R.
Amaral A.G.
de Sousa Mesquita A.
Schmidt-Filho J.
Cordeiro de Lima V.C.
D'Almeida Costa F.
Andrade V.P.
Porcari A.M.
Eberlin M.N.
Simionato A.V.C.
de Piloto Fernandes A.M.A.
Silva A.A.R.
Zamora-Obando H.R.
Amaral A.G.
de Sousa Mesquita A.
Schmidt-Filho J.
Cordeiro de Lima V.C.
D'Almeida Costa F.
Andrade V.P.
Porcari A.M.
Eberlin M.N.
Simionato A.V.C.
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Resumo
© 2020, Springer-Verlag GmbH Germany, part of Springer Nature.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. [Figure not available: see fulltext.]
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Assuntos Scopus
Endogenous metabolites , Gas chromatography-mass spectrometry , Gas chromatography-mass spectrometry methods , Multivariate data analysis , Non-volatile compounds , Predictive accuracy , Receiver operating characteristic curves , Support vector machine algorithm , Adult , Aged , Biomarkers, Tumor , Female , Gas Chromatography-Mass Spectrometry , Humans , Lymphoma, Non-Hodgkin , Male , Metabolome , Metabolomics , Middle Aged