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Urine Metabolomics for Renal Cell Carcinoma (RCC) Prediction: Tryptophan Metabolism as an Important Pathway in RCC
Renal cell carcinoma (RCC) is the second most lethal urinary cancer. RCC is frequently asymptomatic and it is already metastatic at diagnosis. There is an urgent necessity for RCC specific biomarkers selection for diagnostic and prognostic purposes. In present study, we applied liquid chromatography...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6653643/ https://www.ncbi.nlm.nih.gov/pubmed/31380290 http://dx.doi.org/10.3389/fonc.2019.00663 |
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author | Liu, Xiaoyan Zhang, Mingxin Liu, Xiang Sun, Haidan Guo, Zhengguang Tang, Xiaoyue Wang, Zhan Li, Jing Li, Hanzhong Sun, Wei Zhang, Yushi |
author_facet | Liu, Xiaoyan Zhang, Mingxin Liu, Xiang Sun, Haidan Guo, Zhengguang Tang, Xiaoyue Wang, Zhan Li, Jing Li, Hanzhong Sun, Wei Zhang, Yushi |
author_sort | Liu, Xiaoyan |
collection | PubMed |
description | Renal cell carcinoma (RCC) is the second most lethal urinary cancer. RCC is frequently asymptomatic and it is already metastatic at diagnosis. There is an urgent necessity for RCC specific biomarkers selection for diagnostic and prognostic purposes. In present study, we applied liquid chromatography—mass spectrometry (LC-MS) based metabolomics to analyze urine samples of 100 RCC, 34 benign kidney tumors and 129 healthy controls. Differential metabolites were analyzed to investigate if urine metabolites could differentiate RCC from non-RCC. A panel consisting of 9 metabolites showed the best predictive ability for RCC from the health controls with an area under the curve (AUC) values of 0.905 for the training dataset and 0.885 for the validation dataset. Separation was observed between the RCC and benign samples with an AUC of 0.816. RCC clinical stages (T1 and T2 vs. T3 and T4) could be separated using a panel of urine metabolites with an AUC of 0.813. One metabolite, N-formylkynurenine, was discovered to have potential value for RCC diagnosis from non-RCC subjects with an AUC of 0.808. Pathway enrichment analysis indicated that tryptophan metabolism was an important pathway in RCC. Our data concluded that urine metabolomics could be used for RCC diagnosis and would provide candidates for further targeted metabolomics analysis of RCC. |
format | Online Article Text |
id | pubmed-6653643 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-66536432019-08-02 Urine Metabolomics for Renal Cell Carcinoma (RCC) Prediction: Tryptophan Metabolism as an Important Pathway in RCC Liu, Xiaoyan Zhang, Mingxin Liu, Xiang Sun, Haidan Guo, Zhengguang Tang, Xiaoyue Wang, Zhan Li, Jing Li, Hanzhong Sun, Wei Zhang, Yushi Front Oncol Oncology Renal cell carcinoma (RCC) is the second most lethal urinary cancer. RCC is frequently asymptomatic and it is already metastatic at diagnosis. There is an urgent necessity for RCC specific biomarkers selection for diagnostic and prognostic purposes. In present study, we applied liquid chromatography—mass spectrometry (LC-MS) based metabolomics to analyze urine samples of 100 RCC, 34 benign kidney tumors and 129 healthy controls. Differential metabolites were analyzed to investigate if urine metabolites could differentiate RCC from non-RCC. A panel consisting of 9 metabolites showed the best predictive ability for RCC from the health controls with an area under the curve (AUC) values of 0.905 for the training dataset and 0.885 for the validation dataset. Separation was observed between the RCC and benign samples with an AUC of 0.816. RCC clinical stages (T1 and T2 vs. T3 and T4) could be separated using a panel of urine metabolites with an AUC of 0.813. One metabolite, N-formylkynurenine, was discovered to have potential value for RCC diagnosis from non-RCC subjects with an AUC of 0.808. Pathway enrichment analysis indicated that tryptophan metabolism was an important pathway in RCC. Our data concluded that urine metabolomics could be used for RCC diagnosis and would provide candidates for further targeted metabolomics analysis of RCC. Frontiers Media S.A. 2019-07-17 /pmc/articles/PMC6653643/ /pubmed/31380290 http://dx.doi.org/10.3389/fonc.2019.00663 Text en Copyright © 2019 Liu, Zhang, Liu, Sun, Guo, Tang, Wang, Li, Li, Sun and Zhang. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Oncology Liu, Xiaoyan Zhang, Mingxin Liu, Xiang Sun, Haidan Guo, Zhengguang Tang, Xiaoyue Wang, Zhan Li, Jing Li, Hanzhong Sun, Wei Zhang, Yushi Urine Metabolomics for Renal Cell Carcinoma (RCC) Prediction: Tryptophan Metabolism as an Important Pathway in RCC |
title | Urine Metabolomics for Renal Cell Carcinoma (RCC) Prediction: Tryptophan Metabolism as an Important Pathway in RCC |
title_full | Urine Metabolomics for Renal Cell Carcinoma (RCC) Prediction: Tryptophan Metabolism as an Important Pathway in RCC |
title_fullStr | Urine Metabolomics for Renal Cell Carcinoma (RCC) Prediction: Tryptophan Metabolism as an Important Pathway in RCC |
title_full_unstemmed | Urine Metabolomics for Renal Cell Carcinoma (RCC) Prediction: Tryptophan Metabolism as an Important Pathway in RCC |
title_short | Urine Metabolomics for Renal Cell Carcinoma (RCC) Prediction: Tryptophan Metabolism as an Important Pathway in RCC |
title_sort | urine metabolomics for renal cell carcinoma (rcc) prediction: tryptophan metabolism as an important pathway in rcc |
topic | Oncology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6653643/ https://www.ncbi.nlm.nih.gov/pubmed/31380290 http://dx.doi.org/10.3389/fonc.2019.00663 |
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