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Linking cellular metabolism and metabolomics to risk-stratification of prostate cancer clinical aggressiveness and potential therapeutic pathways

Prostate cancer treatment is based on the stratification of disease as low-, intermediate- or high-risk. This stratification has been largely based on anatomic pathology of the disease, as well as through the use of prostate specific antigen (PSA). However, despite this stratification, there remains...

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Autores principales: Eidelman, Eric, Tripathi, Hemantkumar, Fu, De-Xue, Siddiqui, M. Minhaj
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6178321/
https://www.ncbi.nlm.nih.gov/pubmed/30363493
http://dx.doi.org/10.21037/tau.2018.04.08
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author Eidelman, Eric
Tripathi, Hemantkumar
Fu, De-Xue
Siddiqui, M. Minhaj
author_facet Eidelman, Eric
Tripathi, Hemantkumar
Fu, De-Xue
Siddiqui, M. Minhaj
author_sort Eidelman, Eric
collection PubMed
description Prostate cancer treatment is based on the stratification of disease as low-, intermediate- or high-risk. This stratification has been largely based on anatomic pathology of the disease, as well as through the use of prostate specific antigen (PSA). However, despite this stratification, there remains heterogeneity within the current classification schema. Utilizing a metabolic approach may help to further establish novel biomolecular markers of disease aggressiveness. These markers may eventually be useful in not only the diagnosis of disease but in creating tumor specific targeted therapy for improved clinical outcomes.
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spelling pubmed-61783212018-10-24 Linking cellular metabolism and metabolomics to risk-stratification of prostate cancer clinical aggressiveness and potential therapeutic pathways Eidelman, Eric Tripathi, Hemantkumar Fu, De-Xue Siddiqui, M. Minhaj Transl Androl Urol Review Article Prostate cancer treatment is based on the stratification of disease as low-, intermediate- or high-risk. This stratification has been largely based on anatomic pathology of the disease, as well as through the use of prostate specific antigen (PSA). However, despite this stratification, there remains heterogeneity within the current classification schema. Utilizing a metabolic approach may help to further establish novel biomolecular markers of disease aggressiveness. These markers may eventually be useful in not only the diagnosis of disease but in creating tumor specific targeted therapy for improved clinical outcomes. AME Publishing Company 2018-09 /pmc/articles/PMC6178321/ /pubmed/30363493 http://dx.doi.org/10.21037/tau.2018.04.08 Text en 2018 Translational Andrology and Urology. All rights reserved.
spellingShingle Review Article
Eidelman, Eric
Tripathi, Hemantkumar
Fu, De-Xue
Siddiqui, M. Minhaj
Linking cellular metabolism and metabolomics to risk-stratification of prostate cancer clinical aggressiveness and potential therapeutic pathways
title Linking cellular metabolism and metabolomics to risk-stratification of prostate cancer clinical aggressiveness and potential therapeutic pathways
title_full Linking cellular metabolism and metabolomics to risk-stratification of prostate cancer clinical aggressiveness and potential therapeutic pathways
title_fullStr Linking cellular metabolism and metabolomics to risk-stratification of prostate cancer clinical aggressiveness and potential therapeutic pathways
title_full_unstemmed Linking cellular metabolism and metabolomics to risk-stratification of prostate cancer clinical aggressiveness and potential therapeutic pathways
title_short Linking cellular metabolism and metabolomics to risk-stratification of prostate cancer clinical aggressiveness and potential therapeutic pathways
title_sort linking cellular metabolism and metabolomics to risk-stratification of prostate cancer clinical aggressiveness and potential therapeutic pathways
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6178321/
https://www.ncbi.nlm.nih.gov/pubmed/30363493
http://dx.doi.org/10.21037/tau.2018.04.08
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