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Molecular subtyping of nasopharyngeal carcinoma (NPC) and a microRNA-based prognostic model for distant metastasis

BACKGROUND: Nasopharyngeal carcinoma (NPC) is a highly invasive and metastatic cancer, with diverse molecular characteristics and clinical outcomes. This study aims to dissect the molecular heterogeneity of NPC, followed by the construction of a microRNA (miRNA)-based prognostic model for prediction...

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Autores principales: Zhao, Lan, Fong, Alvin H. W., Liu, Na, Cho, William C. S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5817810/
https://www.ncbi.nlm.nih.gov/pubmed/29455649
http://dx.doi.org/10.1186/s12929-018-0417-5
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author Zhao, Lan
Fong, Alvin H. W.
Liu, Na
Cho, William C. S.
author_facet Zhao, Lan
Fong, Alvin H. W.
Liu, Na
Cho, William C. S.
author_sort Zhao, Lan
collection PubMed
description BACKGROUND: Nasopharyngeal carcinoma (NPC) is a highly invasive and metastatic cancer, with diverse molecular characteristics and clinical outcomes. This study aims to dissect the molecular heterogeneity of NPC, followed by the construction of a microRNA (miRNA)-based prognostic model for prediction of distant metastasis. METHODS: We retrieved two NPC datasets: GSE32960 and GSE70970 as training and validation cohorts, respectively. Consensus clustering was employed for cluster discovery, and support vector machine was used to build a classifier. Finally, Cox regression analysis was applied to constructing a prognostic model for predicting risk of distant metastasis. RESULTS: Three NPC subtypes (immunogenic, classical and mesenchymal) were identified that are molecularly distinct and clinically relevant, of which mesenchymal subtype (~ 36%) is associated with poor prognosis, characterized by suppressing tumor suppressor miRNAs and the activation of epithelial-­mesenchymal transition. Out of the 25 most differentially expressed miRNAs in mesenchymal subtype, miR-142, miR-26a, miR-141 and let-7i have significant prognostic power (P < 0.05). CONCLUSIONS: We proposed for the first time that NPC can be stratified into three subtypes. Using a panel of 4 miRNAs, we established a prognostic model that can robustly stratify NPC patients into high- and low- risk groups of distant metastasis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12929-018-0417-5) contains supplementary material, which is available to authorized users.
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spelling pubmed-58178102018-02-23 Molecular subtyping of nasopharyngeal carcinoma (NPC) and a microRNA-based prognostic model for distant metastasis Zhao, Lan Fong, Alvin H. W. Liu, Na Cho, William C. S. J Biomed Sci Research BACKGROUND: Nasopharyngeal carcinoma (NPC) is a highly invasive and metastatic cancer, with diverse molecular characteristics and clinical outcomes. This study aims to dissect the molecular heterogeneity of NPC, followed by the construction of a microRNA (miRNA)-based prognostic model for prediction of distant metastasis. METHODS: We retrieved two NPC datasets: GSE32960 and GSE70970 as training and validation cohorts, respectively. Consensus clustering was employed for cluster discovery, and support vector machine was used to build a classifier. Finally, Cox regression analysis was applied to constructing a prognostic model for predicting risk of distant metastasis. RESULTS: Three NPC subtypes (immunogenic, classical and mesenchymal) were identified that are molecularly distinct and clinically relevant, of which mesenchymal subtype (~ 36%) is associated with poor prognosis, characterized by suppressing tumor suppressor miRNAs and the activation of epithelial-­mesenchymal transition. Out of the 25 most differentially expressed miRNAs in mesenchymal subtype, miR-142, miR-26a, miR-141 and let-7i have significant prognostic power (P < 0.05). CONCLUSIONS: We proposed for the first time that NPC can be stratified into three subtypes. Using a panel of 4 miRNAs, we established a prognostic model that can robustly stratify NPC patients into high- and low- risk groups of distant metastasis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12929-018-0417-5) contains supplementary material, which is available to authorized users. BioMed Central 2018-02-19 /pmc/articles/PMC5817810/ /pubmed/29455649 http://dx.doi.org/10.1186/s12929-018-0417-5 Text en © The Author(s). 2018 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
Zhao, Lan
Fong, Alvin H. W.
Liu, Na
Cho, William C. S.
Molecular subtyping of nasopharyngeal carcinoma (NPC) and a microRNA-based prognostic model for distant metastasis
title Molecular subtyping of nasopharyngeal carcinoma (NPC) and a microRNA-based prognostic model for distant metastasis
title_full Molecular subtyping of nasopharyngeal carcinoma (NPC) and a microRNA-based prognostic model for distant metastasis
title_fullStr Molecular subtyping of nasopharyngeal carcinoma (NPC) and a microRNA-based prognostic model for distant metastasis
title_full_unstemmed Molecular subtyping of nasopharyngeal carcinoma (NPC) and a microRNA-based prognostic model for distant metastasis
title_short Molecular subtyping of nasopharyngeal carcinoma (NPC) and a microRNA-based prognostic model for distant metastasis
title_sort molecular subtyping of nasopharyngeal carcinoma (npc) and a microrna-based prognostic model for distant metastasis
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5817810/
https://www.ncbi.nlm.nih.gov/pubmed/29455649
http://dx.doi.org/10.1186/s12929-018-0417-5
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