Nuclear magnetic resonance and surface-assisted laser desorption/ionization mass spectrometry-based metabolome profiling of urine samples from kidney cancer patients
•Fifty human urine samples and similar number of controls were analyzed.•NMR and MS were used for urine metabolic profiling.•New potential biomarkers of kidney cancer were found. Kidney cancer is one of the most frequently diagnosed cancers of the urinary tract in the world. Despite significant adva...
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Published in | Journal of pharmaceutical and biomedical analysis Vol. 193; p. 113752 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
England
Elsevier B.V
30.01.2021
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Subjects | |
Online Access | Get full text |
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Summary: | •Fifty human urine samples and similar number of controls were analyzed.•NMR and MS were used for urine metabolic profiling.•New potential biomarkers of kidney cancer were found.
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 |
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Bibliography: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0731-7085 1873-264X |
DOI: | 10.1016/j.jpba.2020.113752 |