Cargando…

Metabolic phenotyping for monitoring ovarian cancer patients

Epithelial ovarian cancer (EOC) is the most deadly of the gynecological cancers. New approaches and better tools for monitoring treatment efficacy and disease progression of EOC are required. In this study, metabolomics using rapid resolution liquid chromatography mass spectrometry was applied to a...

Descripción completa

Detalles Bibliográficos
Autores principales: Ke, Chaofu, Li, Ang, Hou, Yan, Sun, Meng, Yang, Kai, Cheng, Jinlong, Wang, Jingtao, Ge, Tingting, Zhang, Fan, Li, Qiang, Li, Junnan, Wu, Ying, Lou, Ge, Li, Kang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4800393/
https://www.ncbi.nlm.nih.gov/pubmed/26996990
http://dx.doi.org/10.1038/srep23334
_version_ 1782422476699992064
author Ke, Chaofu
Li, Ang
Hou, Yan
Sun, Meng
Yang, Kai
Cheng, Jinlong
Wang, Jingtao
Ge, Tingting
Zhang, Fan
Li, Qiang
Li, Junnan
Wu, Ying
Lou, Ge
Li, Kang
author_facet Ke, Chaofu
Li, Ang
Hou, Yan
Sun, Meng
Yang, Kai
Cheng, Jinlong
Wang, Jingtao
Ge, Tingting
Zhang, Fan
Li, Qiang
Li, Junnan
Wu, Ying
Lou, Ge
Li, Kang
author_sort Ke, Chaofu
collection PubMed
description Epithelial ovarian cancer (EOC) is the most deadly of the gynecological cancers. New approaches and better tools for monitoring treatment efficacy and disease progression of EOC are required. In this study, metabolomics using rapid resolution liquid chromatography mass spectrometry was applied to a systematic investigation of metabolic changes in response to advanced EOC, surgery and recurrence. The results revealed considerable metabolic differences between groups. Moreover, 37, 30, and 26 metabolites were identified as potential biomarkers for primary, surgical and recurrent EOC, respectively. Primary EOC was characterized by abnormal lipid metabolism and energy disorders. Oxidative stress and surgical efficacy were clear in the post-operative EOC patients. Recurrent EOC patients showed increased amino acid and lipid metabolism compared with primary EOC patients. After cytoreductive surgery, eight metabolites (e.g. l-kynurenine, retinol, hydroxyphenyllactic acid, 2-octenoic acid) corrected towards levels of the control group, and four (e.g. hydroxyphenyllactic acid, 2-octenoic acid) went back again to primary EOC levels after disease relapse. In conclusion, this study delineated metabolic changes in response to advanced EOC, surgery and recurrence, and identified biomarkers that could facilitate both understanding and monitoring of EOC development and progression.
format Online
Article
Text
id pubmed-4800393
institution National Center for Biotechnology Information
language English
publishDate 2016
publisher Nature Publishing Group
record_format MEDLINE/PubMed
spelling pubmed-48003932016-03-22 Metabolic phenotyping for monitoring ovarian cancer patients Ke, Chaofu Li, Ang Hou, Yan Sun, Meng Yang, Kai Cheng, Jinlong Wang, Jingtao Ge, Tingting Zhang, Fan Li, Qiang Li, Junnan Wu, Ying Lou, Ge Li, Kang Sci Rep Article Epithelial ovarian cancer (EOC) is the most deadly of the gynecological cancers. New approaches and better tools for monitoring treatment efficacy and disease progression of EOC are required. In this study, metabolomics using rapid resolution liquid chromatography mass spectrometry was applied to a systematic investigation of metabolic changes in response to advanced EOC, surgery and recurrence. The results revealed considerable metabolic differences between groups. Moreover, 37, 30, and 26 metabolites were identified as potential biomarkers for primary, surgical and recurrent EOC, respectively. Primary EOC was characterized by abnormal lipid metabolism and energy disorders. Oxidative stress and surgical efficacy were clear in the post-operative EOC patients. Recurrent EOC patients showed increased amino acid and lipid metabolism compared with primary EOC patients. After cytoreductive surgery, eight metabolites (e.g. l-kynurenine, retinol, hydroxyphenyllactic acid, 2-octenoic acid) corrected towards levels of the control group, and four (e.g. hydroxyphenyllactic acid, 2-octenoic acid) went back again to primary EOC levels after disease relapse. In conclusion, this study delineated metabolic changes in response to advanced EOC, surgery and recurrence, and identified biomarkers that could facilitate both understanding and monitoring of EOC development and progression. Nature Publishing Group 2016-03-21 /pmc/articles/PMC4800393/ /pubmed/26996990 http://dx.doi.org/10.1038/srep23334 Text en Copyright © 2016, Macmillan Publishers Limited http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Ke, Chaofu
Li, Ang
Hou, Yan
Sun, Meng
Yang, Kai
Cheng, Jinlong
Wang, Jingtao
Ge, Tingting
Zhang, Fan
Li, Qiang
Li, Junnan
Wu, Ying
Lou, Ge
Li, Kang
Metabolic phenotyping for monitoring ovarian cancer patients
title Metabolic phenotyping for monitoring ovarian cancer patients
title_full Metabolic phenotyping for monitoring ovarian cancer patients
title_fullStr Metabolic phenotyping for monitoring ovarian cancer patients
title_full_unstemmed Metabolic phenotyping for monitoring ovarian cancer patients
title_short Metabolic phenotyping for monitoring ovarian cancer patients
title_sort metabolic phenotyping for monitoring ovarian cancer patients
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4800393/
https://www.ncbi.nlm.nih.gov/pubmed/26996990
http://dx.doi.org/10.1038/srep23334
work_keys_str_mv AT kechaofu metabolicphenotypingformonitoringovariancancerpatients
AT liang metabolicphenotypingformonitoringovariancancerpatients
AT houyan metabolicphenotypingformonitoringovariancancerpatients
AT sunmeng metabolicphenotypingformonitoringovariancancerpatients
AT yangkai metabolicphenotypingformonitoringovariancancerpatients
AT chengjinlong metabolicphenotypingformonitoringovariancancerpatients
AT wangjingtao metabolicphenotypingformonitoringovariancancerpatients
AT getingting metabolicphenotypingformonitoringovariancancerpatients
AT zhangfan metabolicphenotypingformonitoringovariancancerpatients
AT liqiang metabolicphenotypingformonitoringovariancancerpatients
AT lijunnan metabolicphenotypingformonitoringovariancancerpatients
AT wuying metabolicphenotypingformonitoringovariancancerpatients
AT louge metabolicphenotypingformonitoringovariancancerpatients
AT likang metabolicphenotypingformonitoringovariancancerpatients