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LC-MS based cell metabolic profiling of tumor cells: a new predictive method for research on the mechanism of action of anticancer candidates
In the process of anticancer drug development, research on the mechanism of action remains a major obstacle. In the present study, a cell metabolic profiling based discriminatory model was designed to give general direction on anticancer candidate mechanisms. Firstly, ultra-performance liquid chroma...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
The Royal Society of Chemistry
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9080298/ https://www.ncbi.nlm.nih.gov/pubmed/35540548 http://dx.doi.org/10.1039/c8ra00242h |
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author | Wang, Hua Hu, Jia-hui Liu, Cui-chai Liu, Min Liu, Zheng Sun, Li-xin |
author_facet | Wang, Hua Hu, Jia-hui Liu, Cui-chai Liu, Min Liu, Zheng Sun, Li-xin |
author_sort | Wang, Hua |
collection | PubMed |
description | In the process of anticancer drug development, research on the mechanism of action remains a major obstacle. In the present study, a cell metabolic profiling based discriminatory model was designed to give general direction on anticancer candidate mechanisms. Firstly, ultra-performance liquid chromatography in tandem with high-definition mass spectrometry was applied to obtain a comprehensive metabolic view of 12 human tumor cells. Secondly, multivariate data analysis was used to assess the metabolites’ variations, and 42 metabolites were identified as the main contributors to the discrimination of different groups. Then a metabolite-based prediction model was constructed for the first time and verified by cross validation (R(2) = 0.909 and Q(2) = 0.869) and a permutation test (R(2) = 0.0871 and Q(2) = −0.4360). To validate if the model can be applied for mechanism prediction, 4 independent sample sets were used to train the model and the data dots of different drugs were located in different regions. Finally, the model was applied to predict the anticancer mechanism of two natural compounds and the results were consistent with several other studies. Overall, this is the first experimental evidence which reveals that a metabolic profiling based prediction model has good performance in anticancer mechanism research, and thus it may be a new method for rapid mechanism screening. |
format | Online Article Text |
id | pubmed-9080298 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | The Royal Society of Chemistry |
record_format | MEDLINE/PubMed |
spelling | pubmed-90802982022-05-09 LC-MS based cell metabolic profiling of tumor cells: a new predictive method for research on the mechanism of action of anticancer candidates Wang, Hua Hu, Jia-hui Liu, Cui-chai Liu, Min Liu, Zheng Sun, Li-xin RSC Adv Chemistry In the process of anticancer drug development, research on the mechanism of action remains a major obstacle. In the present study, a cell metabolic profiling based discriminatory model was designed to give general direction on anticancer candidate mechanisms. Firstly, ultra-performance liquid chromatography in tandem with high-definition mass spectrometry was applied to obtain a comprehensive metabolic view of 12 human tumor cells. Secondly, multivariate data analysis was used to assess the metabolites’ variations, and 42 metabolites were identified as the main contributors to the discrimination of different groups. Then a metabolite-based prediction model was constructed for the first time and verified by cross validation (R(2) = 0.909 and Q(2) = 0.869) and a permutation test (R(2) = 0.0871 and Q(2) = −0.4360). To validate if the model can be applied for mechanism prediction, 4 independent sample sets were used to train the model and the data dots of different drugs were located in different regions. Finally, the model was applied to predict the anticancer mechanism of two natural compounds and the results were consistent with several other studies. Overall, this is the first experimental evidence which reveals that a metabolic profiling based prediction model has good performance in anticancer mechanism research, and thus it may be a new method for rapid mechanism screening. The Royal Society of Chemistry 2018-05-08 /pmc/articles/PMC9080298/ /pubmed/35540548 http://dx.doi.org/10.1039/c8ra00242h Text en This journal is © The Royal Society of Chemistry https://creativecommons.org/licenses/by/3.0/ |
spellingShingle | Chemistry Wang, Hua Hu, Jia-hui Liu, Cui-chai Liu, Min Liu, Zheng Sun, Li-xin LC-MS based cell metabolic profiling of tumor cells: a new predictive method for research on the mechanism of action of anticancer candidates |
title | LC-MS based cell metabolic profiling of tumor cells: a new predictive method for research on the mechanism of action of anticancer candidates |
title_full | LC-MS based cell metabolic profiling of tumor cells: a new predictive method for research on the mechanism of action of anticancer candidates |
title_fullStr | LC-MS based cell metabolic profiling of tumor cells: a new predictive method for research on the mechanism of action of anticancer candidates |
title_full_unstemmed | LC-MS based cell metabolic profiling of tumor cells: a new predictive method for research on the mechanism of action of anticancer candidates |
title_short | LC-MS based cell metabolic profiling of tumor cells: a new predictive method for research on the mechanism of action of anticancer candidates |
title_sort | lc-ms based cell metabolic profiling of tumor cells: a new predictive method for research on the mechanism of action of anticancer candidates |
topic | Chemistry |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9080298/ https://www.ncbi.nlm.nih.gov/pubmed/35540548 http://dx.doi.org/10.1039/c8ra00242h |
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