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Accurate quantification of urinary metabolites for predictive models manifest clinicopathology of renal cell carcinoma

Using surgically resected tissue, we identified characteristic metabolites related to the diagnosis and malignant status of clear cell renal cell carcinoma (ccRCC). Specifically, we quantified these metabolites in urine samples to evaluate their potential as clinically useful noninvasive biomarkers...

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Autores principales: Sato, Tomonori, Kawasaki, Yoshihide, Maekawa, Masamitsu, Takasaki, Shinya, Shimada, Shuichi, Morozumi, Kento, Sato, Masahiko, Kawamorita, Naoki, Yamashita, Shinichi, Mitsuzuka, Koji, Mano, Nariyasu, Ito, Akihiro
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7385347/
https://www.ncbi.nlm.nih.gov/pubmed/32350988
http://dx.doi.org/10.1111/cas.14440
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author Sato, Tomonori
Kawasaki, Yoshihide
Maekawa, Masamitsu
Takasaki, Shinya
Shimada, Shuichi
Morozumi, Kento
Sato, Masahiko
Kawamorita, Naoki
Yamashita, Shinichi
Mitsuzuka, Koji
Mano, Nariyasu
Ito, Akihiro
author_facet Sato, Tomonori
Kawasaki, Yoshihide
Maekawa, Masamitsu
Takasaki, Shinya
Shimada, Shuichi
Morozumi, Kento
Sato, Masahiko
Kawamorita, Naoki
Yamashita, Shinichi
Mitsuzuka, Koji
Mano, Nariyasu
Ito, Akihiro
author_sort Sato, Tomonori
collection PubMed
description Using surgically resected tissue, we identified characteristic metabolites related to the diagnosis and malignant status of clear cell renal cell carcinoma (ccRCC). Specifically, we quantified these metabolites in urine samples to evaluate their potential as clinically useful noninvasive biomarkers of ccRCC. Between January 2016 and August 2018, we collected urine samples from 87 patients who had pathologically diagnosed ccRCC and from 60 controls who were patients with benign urological conditions. Metabolite concentrations in urine samples were investigated using liquid chromatography‐mass spectrometry with an internal standard and adjustment based on urinary creatinine levels. We analyzed the association between metabolite concentration and predictability of diagnosis and of malignant status by multiple logistic regression and receiver operating characteristic (ROC) curves to establish ccRCC predictive models. Of the 47 metabolites identified in our previous study, we quantified 33 metabolites in the urine samples. Multiple logistic regression analysis revealed 5 metabolites (l‐glutamic acid, lactate, d‐sedoheptulose 7‐phosphate, 2‐hydroxyglutarate, and myoinositol) for a diagnostic predictive model and 4 metabolites (l‐kynurenine, l‐glutamine, fructose 6‐phosphate, and butyrylcarnitine) for a predictive model for clinical stage III/IV. The sensitivity and specificity of the diagnostic predictive model were 93.1% and 95.0%, respectively, yielding an area under the ROC curve (AUC) of 0.966. The sensitivity and specificity of the predictive model for clinical stage were 88.5% and 75.4%, respectively, with an AUC of 0.837. In conclusion, quantitative analysis of urinary metabolites yielded predictive models for diagnosis and malignant status of ccRCC. Urinary metabolites have the potential to be clinically useful noninvasive biomarkers of ccRCC to improve patient outcomes.
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spelling pubmed-73853472020-07-30 Accurate quantification of urinary metabolites for predictive models manifest clinicopathology of renal cell carcinoma Sato, Tomonori Kawasaki, Yoshihide Maekawa, Masamitsu Takasaki, Shinya Shimada, Shuichi Morozumi, Kento Sato, Masahiko Kawamorita, Naoki Yamashita, Shinichi Mitsuzuka, Koji Mano, Nariyasu Ito, Akihiro Cancer Sci Original Articles Using surgically resected tissue, we identified characteristic metabolites related to the diagnosis and malignant status of clear cell renal cell carcinoma (ccRCC). Specifically, we quantified these metabolites in urine samples to evaluate their potential as clinically useful noninvasive biomarkers of ccRCC. Between January 2016 and August 2018, we collected urine samples from 87 patients who had pathologically diagnosed ccRCC and from 60 controls who were patients with benign urological conditions. Metabolite concentrations in urine samples were investigated using liquid chromatography‐mass spectrometry with an internal standard and adjustment based on urinary creatinine levels. We analyzed the association between metabolite concentration and predictability of diagnosis and of malignant status by multiple logistic regression and receiver operating characteristic (ROC) curves to establish ccRCC predictive models. Of the 47 metabolites identified in our previous study, we quantified 33 metabolites in the urine samples. Multiple logistic regression analysis revealed 5 metabolites (l‐glutamic acid, lactate, d‐sedoheptulose 7‐phosphate, 2‐hydroxyglutarate, and myoinositol) for a diagnostic predictive model and 4 metabolites (l‐kynurenine, l‐glutamine, fructose 6‐phosphate, and butyrylcarnitine) for a predictive model for clinical stage III/IV. The sensitivity and specificity of the diagnostic predictive model were 93.1% and 95.0%, respectively, yielding an area under the ROC curve (AUC) of 0.966. The sensitivity and specificity of the predictive model for clinical stage were 88.5% and 75.4%, respectively, with an AUC of 0.837. In conclusion, quantitative analysis of urinary metabolites yielded predictive models for diagnosis and malignant status of ccRCC. Urinary metabolites have the potential to be clinically useful noninvasive biomarkers of ccRCC to improve patient outcomes. John Wiley and Sons Inc. 2020-06-21 2020-07 /pmc/articles/PMC7385347/ /pubmed/32350988 http://dx.doi.org/10.1111/cas.14440 Text en © 2020 The Authors. Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.
spellingShingle Original Articles
Sato, Tomonori
Kawasaki, Yoshihide
Maekawa, Masamitsu
Takasaki, Shinya
Shimada, Shuichi
Morozumi, Kento
Sato, Masahiko
Kawamorita, Naoki
Yamashita, Shinichi
Mitsuzuka, Koji
Mano, Nariyasu
Ito, Akihiro
Accurate quantification of urinary metabolites for predictive models manifest clinicopathology of renal cell carcinoma
title Accurate quantification of urinary metabolites for predictive models manifest clinicopathology of renal cell carcinoma
title_full Accurate quantification of urinary metabolites for predictive models manifest clinicopathology of renal cell carcinoma
title_fullStr Accurate quantification of urinary metabolites for predictive models manifest clinicopathology of renal cell carcinoma
title_full_unstemmed Accurate quantification of urinary metabolites for predictive models manifest clinicopathology of renal cell carcinoma
title_short Accurate quantification of urinary metabolites for predictive models manifest clinicopathology of renal cell carcinoma
title_sort accurate quantification of urinary metabolites for predictive models manifest clinicopathology of renal cell carcinoma
topic Original Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7385347/
https://www.ncbi.nlm.nih.gov/pubmed/32350988
http://dx.doi.org/10.1111/cas.14440
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