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Molecular classification and prediction in gastric cancer

Gastric cancer, a highly heterogeneous disease, is the second leading cause of cancer death and the fourth most common cancer globally, with East Asia accounting for more than half of cases annually. Alongside TNM staging, gastric cancer clinic has two well-recognized classification systems, the Lau...

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Detalles Bibliográficos
Autores principales: Lin, Xiandong, Zhao, Yongzhong, Song, Won-min, Zhang, Bin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4556804/
https://www.ncbi.nlm.nih.gov/pubmed/26380657
http://dx.doi.org/10.1016/j.csbj.2015.08.001
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author Lin, Xiandong
Zhao, Yongzhong
Song, Won-min
Zhang, Bin
author_facet Lin, Xiandong
Zhao, Yongzhong
Song, Won-min
Zhang, Bin
author_sort Lin, Xiandong
collection PubMed
description Gastric cancer, a highly heterogeneous disease, is the second leading cause of cancer death and the fourth most common cancer globally, with East Asia accounting for more than half of cases annually. Alongside TNM staging, gastric cancer clinic has two well-recognized classification systems, the Lauren classification that subdivides gastric adenocarcinoma into intestinal and diffuse types and the alternative World Health Organization system that divides gastric cancer into papillary, tubular, mucinous (colloid), and poorly cohesive carcinomas. Both classification systems enable a better understanding of the histogenesis and the biology of gastric cancer yet have a limited clinical utility in guiding patient therapy due to the molecular heterogeneity of gastric cancer. Unprecedented whole-genome-scale data have been catalyzing and advancing the molecular subtyping approach. Here we cataloged and compared those published gene expression profiling signatures in gastric cancer. We summarized recent integrated genomic characterization of gastric cancer based on additional data of somatic mutation, chromosomal instability, EBV virus infection, and DNA methylation. We identified the consensus patterns across these signatures and identified the underlying molecular pathways and biological functions. The identification of molecular subtyping of gastric adenocarcinoma and the development of integrated genomics approaches for clinical applications such as prediction of clinical intervening emerge as an essential phase toward personalized medicine in treating gastric cancer.
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spelling pubmed-45568042015-09-17 Molecular classification and prediction in gastric cancer Lin, Xiandong Zhao, Yongzhong Song, Won-min Zhang, Bin Comput Struct Biotechnol J Mini Review Gastric cancer, a highly heterogeneous disease, is the second leading cause of cancer death and the fourth most common cancer globally, with East Asia accounting for more than half of cases annually. Alongside TNM staging, gastric cancer clinic has two well-recognized classification systems, the Lauren classification that subdivides gastric adenocarcinoma into intestinal and diffuse types and the alternative World Health Organization system that divides gastric cancer into papillary, tubular, mucinous (colloid), and poorly cohesive carcinomas. Both classification systems enable a better understanding of the histogenesis and the biology of gastric cancer yet have a limited clinical utility in guiding patient therapy due to the molecular heterogeneity of gastric cancer. Unprecedented whole-genome-scale data have been catalyzing and advancing the molecular subtyping approach. Here we cataloged and compared those published gene expression profiling signatures in gastric cancer. We summarized recent integrated genomic characterization of gastric cancer based on additional data of somatic mutation, chromosomal instability, EBV virus infection, and DNA methylation. We identified the consensus patterns across these signatures and identified the underlying molecular pathways and biological functions. The identification of molecular subtyping of gastric adenocarcinoma and the development of integrated genomics approaches for clinical applications such as prediction of clinical intervening emerge as an essential phase toward personalized medicine in treating gastric cancer. Research Network of Computational and Structural Biotechnology 2015-08-13 /pmc/articles/PMC4556804/ /pubmed/26380657 http://dx.doi.org/10.1016/j.csbj.2015.08.001 Text en © 2015 Lin et al. Published by Elsevier B.V. on behalf of the Research Network of Computational and Structural Biotechnology. http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Mini Review
Lin, Xiandong
Zhao, Yongzhong
Song, Won-min
Zhang, Bin
Molecular classification and prediction in gastric cancer
title Molecular classification and prediction in gastric cancer
title_full Molecular classification and prediction in gastric cancer
title_fullStr Molecular classification and prediction in gastric cancer
title_full_unstemmed Molecular classification and prediction in gastric cancer
title_short Molecular classification and prediction in gastric cancer
title_sort molecular classification and prediction in gastric cancer
topic Mini Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4556804/
https://www.ncbi.nlm.nih.gov/pubmed/26380657
http://dx.doi.org/10.1016/j.csbj.2015.08.001
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