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Candidate genes involved in metastasis of colon cancer identified by integrated analysis

Colon cancer is one of the most malignant cancers worldwide. Nearly 20% of all colon cancer patients are diagnosed at stage IV (metastasis). However, further study of colon cancer is difficult due to a lack of understanding of its pathogenesis. In this study, we acquired high–throughput sequence dat...

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Detalles Bibliográficos
Autores principales: Zhou, Yiming, Zang, Yiwen, Yang, Yi, Xiang, Jianbin, Chen, Zongyou
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6536975/
https://www.ncbi.nlm.nih.gov/pubmed/30884206
http://dx.doi.org/10.1002/cam4.2071
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author Zhou, Yiming
Zang, Yiwen
Yang, Yi
Xiang, Jianbin
Chen, Zongyou
author_facet Zhou, Yiming
Zang, Yiwen
Yang, Yi
Xiang, Jianbin
Chen, Zongyou
author_sort Zhou, Yiming
collection PubMed
description Colon cancer is one of the most malignant cancers worldwide. Nearly 20% of all colon cancer patients are diagnosed at stage IV (metastasis). However, further study of colon cancer is difficult due to a lack of understanding of its pathogenesis. In this study, we acquired high–throughput sequence data from TCGA datasets and performed integrated bioinformatic analysis including differential gene expression analysis, gene ontology and KEGG pathways analysis, protein–protein analysis, survival analysis, and multivariate Cox proportional hazards regression analysis in order to identify a panel of key candidate genes involved in the metastasis of colon cancer. We then constructed a prognostic signature based on the expression of REG1B, TGM6, NTF4, PNMA5, and HOXC13 which could provide significant prognostic value for colon cancer.
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spelling pubmed-65369752019-06-03 Candidate genes involved in metastasis of colon cancer identified by integrated analysis Zhou, Yiming Zang, Yiwen Yang, Yi Xiang, Jianbin Chen, Zongyou Cancer Med Cancer Biology Colon cancer is one of the most malignant cancers worldwide. Nearly 20% of all colon cancer patients are diagnosed at stage IV (metastasis). However, further study of colon cancer is difficult due to a lack of understanding of its pathogenesis. In this study, we acquired high–throughput sequence data from TCGA datasets and performed integrated bioinformatic analysis including differential gene expression analysis, gene ontology and KEGG pathways analysis, protein–protein analysis, survival analysis, and multivariate Cox proportional hazards regression analysis in order to identify a panel of key candidate genes involved in the metastasis of colon cancer. We then constructed a prognostic signature based on the expression of REG1B, TGM6, NTF4, PNMA5, and HOXC13 which could provide significant prognostic value for colon cancer. John Wiley and Sons Inc. 2019-03-18 /pmc/articles/PMC6536975/ /pubmed/30884206 http://dx.doi.org/10.1002/cam4.2071 Text en © 2019 The Authors. Cancer Medicine published by John Wiley & Sons Ltd. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Cancer Biology
Zhou, Yiming
Zang, Yiwen
Yang, Yi
Xiang, Jianbin
Chen, Zongyou
Candidate genes involved in metastasis of colon cancer identified by integrated analysis
title Candidate genes involved in metastasis of colon cancer identified by integrated analysis
title_full Candidate genes involved in metastasis of colon cancer identified by integrated analysis
title_fullStr Candidate genes involved in metastasis of colon cancer identified by integrated analysis
title_full_unstemmed Candidate genes involved in metastasis of colon cancer identified by integrated analysis
title_short Candidate genes involved in metastasis of colon cancer identified by integrated analysis
title_sort candidate genes involved in metastasis of colon cancer identified by integrated analysis
topic Cancer Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6536975/
https://www.ncbi.nlm.nih.gov/pubmed/30884206
http://dx.doi.org/10.1002/cam4.2071
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