Literature DB >> 30830422

Metabolomic identification of diagnostic serum-based biomarkers for advanced stage melanoma.

A W L Bayci1, D A Baker2,3, A E Somerset1, O Turkoglu4, Z Hothem1, R E Callahan1, R Mandal5, B Han5, T Bjorndahl5, D Wishart5, R Bahado-Singh4, S F Graham4, R Keidan1.   

Abstract

INTRODUCTION: Melanoma is a highly aggressive malignancy and is currently one of the fastest growing cancers worldwide. While early stage (I and II) disease is highly curable with excellent prognosis, mortality rates rise dramatically after distant spread. We sought to identify differences in the metabolome of melanoma patients to further elucidate the pathophysiology of melanoma and identify potential biomarkers to aid in earlier detection of recurrence.
METHODS: Using 1H NMR and DI-LC-MS/MS, we profiled serum samples from 26 patients with stage III (nodal metastasis) or stage IV (distant metastasis) melanoma and compared their biochemical profiles with 46 age- and gender-matched controls.
RESULTS: We accurately quantified 181 metabolites in serum using a combination of 1H NMR and DI-LC-MS/MS. We observed significant separation between cases and controls in the PLS-DA scores plot (permutation test p-value = 0.002). Using the concentrations of PC-aa-C40:3, DL-carnitine, octanoyl-L-carnitine, ethanol, and methylmalonyl-L-carnitine we developed a diagnostic algorithm with an AUC (95% CI) = 0.822 (0.665-0.979) with sensitivity and specificity of 100 and 56%, respectively. Furthermore, we identified arginine, proline, tryptophan, glutamine, glutamate, glutathione and ornithine metabolism to be significantly perturbed due to disease (p < 0.05).
CONCLUSION: Targeted metabolomic analysis demonstrated significant differences in metabolic profiles of advanced stage (III and IV) melanoma patients as compared to controls. These differences may represent a potential avenue for the development of multi-marker serum-based assays for earlier detection of recurrences, allow for newer, more effective targeted therapy when tumor burden is less, and further elucidate the pathophysiologic changes that occur in melanoma.

Entities:  

Keywords:  Amino acids; Melanoma; Metabolomics; Serum biomarkers

Mesh:

Substances:

Year:  2018        PMID: 30830422     DOI: 10.1007/s11306-018-1398-9

Source DB:  PubMed          Journal:  Metabolomics        ISSN: 1573-3882            Impact factor:   4.290


  113 in total

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Authors:  Paul B Chapman; Axel Hauschild; Caroline Robert; John B Haanen; Paolo Ascierto; James Larkin; Reinhard Dummer; Claus Garbe; Alessandro Testori; Michele Maio; David Hogg; Paul Lorigan; Celeste Lebbe; Thomas Jouary; Dirk Schadendorf; Antoni Ribas; Steven J O'Day; Jeffrey A Sosman; John M Kirkwood; Alexander M M Eggermont; Brigitte Dreno; Keith Nolop; Jiang Li; Betty Nelson; Jeannie Hou; Richard J Lee; Keith T Flaherty; Grant A McArthur
Journal:  N Engl J Med       Date:  2011-06-05       Impact factor: 91.245

2.  Improved overall survival in melanoma with combined dabrafenib and trametinib.

Authors:  Caroline Robert; Boguslawa Karaszewska; Jacob Schachter; Piotr Rutkowski; Andrzej Mackiewicz; Daniil Stroiakovski; Michael Lichinitser; Reinhard Dummer; Florent Grange; Laurent Mortier; Vanna Chiarion-Sileni; Kamil Drucis; Ivana Krajsova; Axel Hauschild; Paul Lorigan; Pascal Wolter; Georgina V Long; Keith Flaherty; Paul Nathan; Antoni Ribas; Anne-Marie Martin; Peng Sun; Wendy Crist; Jeff Legos; Stephen D Rubin; Shonda M Little; Dirk Schadendorf
Journal:  N Engl J Med       Date:  2014-11-16       Impact factor: 91.245

Review 3.  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

4.  The strengths and weaknesses of NMR spectroscopy and mass spectrometry with particular focus on metabolomics research.

Authors:  Abdul-Hamid M Emwas
Journal:  Methods Mol Biol       Date:  2015

Review 5.  The role of cyclooxygenase and lipoxygenase in cancer chemoprevention.

Authors:  M Cuendet; J M Pezzuto
Journal:  Drug Metabol Drug Interact       Date:  2000

6.  A strategy for combating melanoma with oncogenic c-Myc inhibitors and targeted nanotherapy.

Authors:  Dipanjan Pan; Benjamin Kim; Grace Hu; Deepti Sood Gupta; Angana Senpan; Xiaoxia Yang; Anne Schmieder; Corban Swain; Samuel A Wickline; Michael H Tomasson; Gregory M Lanza
Journal:  Nanomedicine (Lond)       Date:  2015-01       Impact factor: 5.307

Review 7.  Metabolomic Biomarkers of Prostate Cancer: Prediction, Diagnosis, Progression, Prognosis, and Recurrence.

Authors:  Rachel S Kelly; Matthew G Vander Heiden; Edward Giovannucci; Lorelei A Mucci
Journal:  Cancer Epidemiol Biomarkers Prev       Date:  2016-04-06       Impact factor: 4.254

Review 8.  Variability in melanoma post-treatment surveillance practices by country and physician specialty: a systematic review.

Authors:  Kate D Cromwell; Merrick I Ross; Yan Xing; Jeffrey E Gershenwald; Richard E Royal; Anthony Lucci; Jeffrey E Lee; Janice N Cormier
Journal:  Melanoma Res       Date:  2012-10       Impact factor: 3.599

Review 9.  Phospholipase A2 expression in tumours: a target for therapeutic intervention?

Authors:  Jonathan P Laye; Jason H Gill
Journal:  Drug Discov Today       Date:  2003-08-01       Impact factor: 7.851

10.  MetaboAnalyst: a web server for metabolomic data analysis and interpretation.

Authors:  Jianguo Xia; Nick Psychogios; Nelson Young; David S Wishart
Journal:  Nucleic Acids Res       Date:  2009-05-08       Impact factor: 16.971

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  6 in total

Review 1.  Recent applications of chemometrics in one- and two-dimensional chromatography.

Authors:  Tijmen S Bos; Wouter C Knol; Stef R A Molenaar; Leon E Niezen; Peter J Schoenmakers; Govert W Somsen; Bob W J Pirok
Journal:  J Sep Sci       Date:  2020-03-19       Impact factor: 3.645

2.  1H-NMR Based Metabolomics Technology Identifies Potential Serum Biomarkers of Colorectal Cancer Lung Metastasis in a Mouse Model.

Authors:  Junfei Zhang; Yuanxin Du; Yongcai Zhang; Yanan Xu; Yanying Fan; Yan Li
Journal:  Cancer Manag Res       Date:  2022-04-14       Impact factor: 3.602

3.  Predictive Modeling for Metabolomics Data.

Authors:  Tusharkanti Ghosh; Weiming Zhang; Debashis Ghosh; Katerina Kechris
Journal:  Methods Mol Biol       Date:  2020

4.  Targeted Metabolomics Identifies Plasma Biomarkers in Mice with Metabolically Heterogeneous Melanoma Xenografts.

Authors:  Daniela D Weber; Maheshwor Thapa; Sepideh Aminzadeh-Gohari; Anna-Sophia Redtenbacher; Luca Catalano; René G Feichtinger; Peter Koelblinger; Guido Dallmann; Michael Emberger; Barbara Kofler; Roland Lang
Journal:  Cancers (Basel)       Date:  2021-01-23       Impact factor: 6.639

5.  Different effects of tryptophan 2,3-dioxygenase inhibition on SK-Mel-28 and HCT-8 cancer cell lines.

Authors:  Sara Paccosi; Marta Cecchi; Angela Silvano; Sergio Fabbri; Astrid Parenti
Journal:  J Cancer Res Clin Oncol       Date:  2020-08-10       Impact factor: 4.553

6.  Metabolomic profile of cancer stem cell-derived exosomes from patients with malignant melanoma.

Authors:  José Luis Palacios-Ferrer; María Belén García-Ortega; María Gallardo-Gómez; María Ángel García; Caridad Díaz; Houria Boulaiz; Javier Valdivia; José Miguel Jurado; Francisco M Almazan-Fernandez; Salvador Arias-Santiago; Víctor Amezcua; Héctor Peinado; Francisca Vicente; José Pérez Del Palacio; Juan A Marchal
Journal:  Mol Oncol       Date:  2020-11-25       Impact factor: 7.449

  6 in total

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