Literature DB >> 33197834

Nuclear magnetic resonance and surface-assisted laser desorption/ionization mass spectrometry-based metabolome profiling of urine samples from kidney cancer patients.

Joanna Nizioł1, Krzysztof Ossoliński2, Brian P Tripet3, Valérie Copié3, Adrian Arendowski4, Tomasz Ruman4.   

Abstract

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.
Copyright © 2020 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Biomarkers; Cancer; Kidney; Mass spectrometry; Proton nuclear magnetic resonance; Urine

Mesh:

Year:  2020        PMID: 33197834     DOI: 10.1016/j.jpba.2020.113752

Source DB:  PubMed          Journal:  J Pharm Biomed Anal        ISSN: 0731-7085            Impact factor:   3.935


  2 in total

1.  Metabolomic and elemental profiling of human tissue in kidney cancer.

Authors:  Joanna Nizioł; Valérie Copié; Brian P Tripet; Leonardo B Nogueira; Katiane O P C Nogueira; Krzysztof Ossoliński; Adrian Arendowski; Tomasz Ruman
Journal:  Metabolomics       Date:  2021-03-04       Impact factor: 4.290

2.  Urine-Based Metabolomics and Machine Learning Reveals Metabolites Associated with Renal Cell Carcinoma Stage.

Authors:  Olatomiwa O Bifarin; David A Gaul; Samyukta Sah; Rebecca S Arnold; Kenneth Ogan; Viraj A Master; David L Roberts; Sharon H Bergquist; John A Petros; Arthur S Edison; Facundo M Fernández
Journal:  Cancers (Basel)       Date:  2021-12-13       Impact factor: 6.575

  2 in total

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