Literature DB >> 18484357

Metabonomics in cancer diagnosis: mass spectrometry-based profiling of urinary nucleosides from breast cancer patients.

Antje Frickenschmidt1, Holger Frohlich, Dino Bullinger, Andreas Zell, Stefan Laufer, Christoph H Gleiter, Hartmut Liebich, Bernd Kammerer.   

Abstract

Modified nucleosides are formed post-transcriptionally in RNA. In cancer disease, the cell turnover and thus RNA metabolism is increased, yielding higher concentrations of excreted modified nucleosides. In the presented study, urinary ribonucleosides were used to differentiate between breast cancer patients and healthy volunteers. The nucleosides were extracted from urine samples using affinity chromatography and subsequently analyzed via liquid chromatography ion trap mass spectrometry (LC-IT-MS). The peak areas were related to the internal standard isoguanosine and to the urinary creatinine level. For bioinformatic pattern recognition we used the support vector machine. We examined 113 urine samples from breast cancer patients (stage Tis-T4) and 99 control samples from healthy volunteers. We achieved a sensitivity of 87.67% and a specificity of 89.90% when including 31 nucleosides. The medical metabonomics concept based on the urinary nucleoside profile reveals a significantly improved classification compared with currently applied breast cancer biomarkers such as CA15-3.

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Year:  2008        PMID: 18484357     DOI: 10.1080/13547500802012858

Source DB:  PubMed          Journal:  Biomarkers        ISSN: 1354-750X            Impact factor:   2.658


  18 in total

Review 1.  Metabolomics: moving to the clinic.

Authors:  Anders Nordström; Rolf Lewensohn
Journal:  J Neuroimmune Pharmacol       Date:  2009-04-28       Impact factor: 4.147

Review 2.  Review of mass spectrometry-based metabolomics in cancer research.

Authors:  David B Liesenfeld; Nina Habermann; Robert W Owen; Augustin Scalbert; Cornelia M Ulrich
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2013-10-04       Impact factor: 4.254

3.  Extraction of nucleobases, nucleosides and nucleotides by employing a magnetized graphene oxide functionalized with hydrophilic phytic acid and titanium(IV) ions.

Authors:  Qian Zhang; Dong-Dong Zhou; Fan Li; Yin-Zhen Wang; Feng-Qing Yang
Journal:  Mikrochim Acta       Date:  2019-02-15       Impact factor: 5.833

4.  Simultaneous Quantification of Nucleosides and Nucleotides from Biological Samples.

Authors:  Liqing He; Xiaoli Wei; Xipeng Ma; Xinmin Yin; Ming Song; Howard Donninger; Kavitha Yaddanapudi; Craig J McClain; Xiang Zhang
Journal:  J Am Soc Mass Spectrom       Date:  2019-03-07       Impact factor: 3.109

Review 5.  -The advancement of biomarker-based diagnostic tools for ovarian, breast, and pancreatic cancer through the use of urine as an analytical biofluid.

Authors:  Brian M Nolen; Anna E Lokshin
Journal:  Int J Biol Markers       Date:  2011-09-21       Impact factor: 2.659

6.  Metabolic Response to XD14 Treatment in Human Breast Cancer Cell Line MCF-7.

Authors:  Daqiang Pan; Michel Kather; Lucas Willmann; Manuel Schlimpert; Christoph Bauer; Simon Lagies; Karin Schmidtkunz; Steffen U Eisenhardt; Manfred Jung; Stefan Günther; Bernd Kammerer
Journal:  Int J Mol Sci       Date:  2016-10-24       Impact factor: 5.923

7.  Identification of metabolites in the normal ovary and their transformation in primary and metastatic ovarian cancer.

Authors:  Miranda Y Fong; Jonathan McDunn; Sham S Kakar
Journal:  PLoS One       Date:  2011-05-19       Impact factor: 3.240

Review 8.  The state-of-the-art determination of urinary nucleosides using chromatographic techniques "hyphenated" with advanced bioinformatic methods.

Authors:  Wiktoria Struck; Małgorzata Waszczuk-Jankowska; Roman Kaliszan; Michał J Markuszewski
Journal:  Anal Bioanal Chem       Date:  2011-02-27       Impact factor: 4.142

Review 9.  Metabolomic fingerprinting: challenges and opportunities.

Authors:  Alyssa K Kosmides; Kubra Kamisoglu; Steve E Calvano; Siobhan A Corbett; Ioannis P Androulakis
Journal:  Crit Rev Biomed Eng       Date:  2013

10.  Prediction of breast cancer by profiling of urinary RNA metabolites using Support Vector Machine-based feature selection.

Authors:  Carsten Henneges; Dino Bullinger; Richard Fux; Natascha Friese; Harald Seeger; Hans Neubauer; Stefan Laufer; Christoph H Gleiter; Matthias Schwab; Andreas Zell; Bernd Kammerer
Journal:  BMC Cancer       Date:  2009-04-05       Impact factor: 4.430

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