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UPLC-MS based urine untargeted metabolomic analyses to differentiate bladder cancer from renal cell carcinoma
BACKGROUND: To discover biomarker panels that could distinguish cancers (BC and RCC) from healthy controls (HCs) and bladder cancers (BC) from renal cell carcinoma (RCC), regardless of whether the patients have haematuria. In addition, we also explored the altered metabolomic pathways of BC and RCC....
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6896793/ https://www.ncbi.nlm.nih.gov/pubmed/31805976 http://dx.doi.org/10.1186/s12885-019-6354-1 |
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author | Wang, Zhan Liu, Xiaoyan Liu, Xiang Sun, Haidan Guo, Zhengguang Zheng, Guoyang Zhang, Yushi Sun, Wei |
author_facet | Wang, Zhan Liu, Xiaoyan Liu, Xiang Sun, Haidan Guo, Zhengguang Zheng, Guoyang Zhang, Yushi Sun, Wei |
author_sort | Wang, Zhan |
collection | PubMed |
description | BACKGROUND: To discover biomarker panels that could distinguish cancers (BC and RCC) from healthy controls (HCs) and bladder cancers (BC) from renal cell carcinoma (RCC), regardless of whether the patients have haematuria. In addition, we also explored the altered metabolomic pathways of BC and RCC. METHODS: In total, 403 participants were enrolled in our study, which included 146 BC patients (77 without haematuria and 69 with haematuria), 115 RCC patients (94 without haematuria and 21 with haematuria) and 142 sex- and age-matched HCs. Their midstream urine samples were collected and analysed by performing UPLC-MS. The statistical methods and pathway analyses were applied to discover potential biomarker panels and altered metabolic pathways. RESULTS: The panel of α-CEHC, β-cortolone, deoxyinosine, flunisolide, 11b,17a,21-trihydroxypreg-nenolone and glycerol tripropanoate could distinguish the patients with cancer from the HCs (the AUC was 0.950) and the external validation also displayed a good predictive ability (the AUC was 0.867). The panel of 4-ethoxymethylphenol, prostaglandin F2b, thromboxane B3, hydroxybutyrylcarnitine, 3-hydroxyphloretin and N′-formylkynurenine could differentiate BC from RCC without haematuria. The AUC was 0.829 in the discovering group and 0.76 in the external validation. The metabolite panel comprising 1-hydroxy-2-oxopropyl tetrahydropterin, 1-acetoxy-2-hydroxy-16-heptadecyn-4-one, 1,2-dehydrosalsolinol and L-tyrosine could significantly discriminate BC from RCC with haematuria (AUC was 0.913). Pathway analyses revealed altered lipid and purine metabolisms between cancer patients and HCs, together with disordered amino acid and purine metabolisms between BC and RCC with haematuria. CONCLUSIONS: UPLC-MS urine metabolomic analyses could not only differentiate cancers from HCs but also discriminate BC from RCC. In addition, pathway analyses demonstrated a deeper metabolic mechanism of BC and RCC. |
format | Online Article Text |
id | pubmed-6896793 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-68967932019-12-11 UPLC-MS based urine untargeted metabolomic analyses to differentiate bladder cancer from renal cell carcinoma Wang, Zhan Liu, Xiaoyan Liu, Xiang Sun, Haidan Guo, Zhengguang Zheng, Guoyang Zhang, Yushi Sun, Wei BMC Cancer Research Article BACKGROUND: To discover biomarker panels that could distinguish cancers (BC and RCC) from healthy controls (HCs) and bladder cancers (BC) from renal cell carcinoma (RCC), regardless of whether the patients have haematuria. In addition, we also explored the altered metabolomic pathways of BC and RCC. METHODS: In total, 403 participants were enrolled in our study, which included 146 BC patients (77 without haematuria and 69 with haematuria), 115 RCC patients (94 without haematuria and 21 with haematuria) and 142 sex- and age-matched HCs. Their midstream urine samples were collected and analysed by performing UPLC-MS. The statistical methods and pathway analyses were applied to discover potential biomarker panels and altered metabolic pathways. RESULTS: The panel of α-CEHC, β-cortolone, deoxyinosine, flunisolide, 11b,17a,21-trihydroxypreg-nenolone and glycerol tripropanoate could distinguish the patients with cancer from the HCs (the AUC was 0.950) and the external validation also displayed a good predictive ability (the AUC was 0.867). The panel of 4-ethoxymethylphenol, prostaglandin F2b, thromboxane B3, hydroxybutyrylcarnitine, 3-hydroxyphloretin and N′-formylkynurenine could differentiate BC from RCC without haematuria. The AUC was 0.829 in the discovering group and 0.76 in the external validation. The metabolite panel comprising 1-hydroxy-2-oxopropyl tetrahydropterin, 1-acetoxy-2-hydroxy-16-heptadecyn-4-one, 1,2-dehydrosalsolinol and L-tyrosine could significantly discriminate BC from RCC with haematuria (AUC was 0.913). Pathway analyses revealed altered lipid and purine metabolisms between cancer patients and HCs, together with disordered amino acid and purine metabolisms between BC and RCC with haematuria. CONCLUSIONS: UPLC-MS urine metabolomic analyses could not only differentiate cancers from HCs but also discriminate BC from RCC. In addition, pathway analyses demonstrated a deeper metabolic mechanism of BC and RCC. BioMed Central 2019-12-05 /pmc/articles/PMC6896793/ /pubmed/31805976 http://dx.doi.org/10.1186/s12885-019-6354-1 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Wang, Zhan Liu, Xiaoyan Liu, Xiang Sun, Haidan Guo, Zhengguang Zheng, Guoyang Zhang, Yushi Sun, Wei UPLC-MS based urine untargeted metabolomic analyses to differentiate bladder cancer from renal cell carcinoma |
title | UPLC-MS based urine untargeted metabolomic analyses to differentiate bladder cancer from renal cell carcinoma |
title_full | UPLC-MS based urine untargeted metabolomic analyses to differentiate bladder cancer from renal cell carcinoma |
title_fullStr | UPLC-MS based urine untargeted metabolomic analyses to differentiate bladder cancer from renal cell carcinoma |
title_full_unstemmed | UPLC-MS based urine untargeted metabolomic analyses to differentiate bladder cancer from renal cell carcinoma |
title_short | UPLC-MS based urine untargeted metabolomic analyses to differentiate bladder cancer from renal cell carcinoma |
title_sort | uplc-ms based urine untargeted metabolomic analyses to differentiate bladder cancer from renal cell carcinoma |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6896793/ https://www.ncbi.nlm.nih.gov/pubmed/31805976 http://dx.doi.org/10.1186/s12885-019-6354-1 |
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