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Associating transcriptional modules with colon cancer survival through weighted gene co-expression network analysis
BACKGROUND: Colon cancer (CC) is a heterogeneous disease influenced by complex gene networks. As such, the relationship between networks and CC should be elucidated to obtain further insights into tumour biology. RESULTS: Weighted gene co-expression network analysis, a powerful technique used to ext...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5424422/ https://www.ncbi.nlm.nih.gov/pubmed/28486948 http://dx.doi.org/10.1186/s12864-017-3761-z |
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author | Liu, Rong Zhang, Wei Liu, Zhao-Qian Zhou, Hong-Hao |
author_facet | Liu, Rong Zhang, Wei Liu, Zhao-Qian Zhou, Hong-Hao |
author_sort | Liu, Rong |
collection | PubMed |
description | BACKGROUND: Colon cancer (CC) is a heterogeneous disease influenced by complex gene networks. As such, the relationship between networks and CC should be elucidated to obtain further insights into tumour biology. RESULTS: Weighted gene co-expression network analysis, a powerful technique used to extract co-expressed gene networks from mRNA expressions, was conducted to identify 11 co-regulated modules in a discovery dataset with 461 patients. A transcriptional module enriched in cell cycle processes was correlated with the recurrence-free survival of the CC patients in the discovery (HR = 0.59; 95% CI = 0.42–0.81) and validation (HR = 0.51; 95% CI = 0.25–1.05) datasets. The prognostic potential of the hub gene Centromere Protein-A (CENPA) was also identified and the upregulation of this gene was associated with good survival. Another cell cycle phase-related gene module was correlated with the survival of the patients with a KRAS mutation CC subtype. The downregulation of several genes, including those found in this co-expression module, such as cyclin-dependent kinase 1 (CDK1), was associated with poor survival. CONCLUSION: Network-based approaches may facilitate the discovery of biomarkers for the prognosis of a subset of patients with stage II or III CC, these approaches may also help direct personalised therapies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-017-3761-z) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5424422 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-54244222017-05-11 Associating transcriptional modules with colon cancer survival through weighted gene co-expression network analysis Liu, Rong Zhang, Wei Liu, Zhao-Qian Zhou, Hong-Hao BMC Genomics Research Article BACKGROUND: Colon cancer (CC) is a heterogeneous disease influenced by complex gene networks. As such, the relationship between networks and CC should be elucidated to obtain further insights into tumour biology. RESULTS: Weighted gene co-expression network analysis, a powerful technique used to extract co-expressed gene networks from mRNA expressions, was conducted to identify 11 co-regulated modules in a discovery dataset with 461 patients. A transcriptional module enriched in cell cycle processes was correlated with the recurrence-free survival of the CC patients in the discovery (HR = 0.59; 95% CI = 0.42–0.81) and validation (HR = 0.51; 95% CI = 0.25–1.05) datasets. The prognostic potential of the hub gene Centromere Protein-A (CENPA) was also identified and the upregulation of this gene was associated with good survival. Another cell cycle phase-related gene module was correlated with the survival of the patients with a KRAS mutation CC subtype. The downregulation of several genes, including those found in this co-expression module, such as cyclin-dependent kinase 1 (CDK1), was associated with poor survival. CONCLUSION: Network-based approaches may facilitate the discovery of biomarkers for the prognosis of a subset of patients with stage II or III CC, these approaches may also help direct personalised therapies. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12864-017-3761-z) contains supplementary material, which is available to authorized users. BioMed Central 2017-05-09 /pmc/articles/PMC5424422/ /pubmed/28486948 http://dx.doi.org/10.1186/s12864-017-3761-z Text en © The Author(s). 2017 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 Article Liu, Rong Zhang, Wei Liu, Zhao-Qian Zhou, Hong-Hao Associating transcriptional modules with colon cancer survival through weighted gene co-expression network analysis |
title | Associating transcriptional modules with colon cancer survival through weighted gene co-expression network analysis |
title_full | Associating transcriptional modules with colon cancer survival through weighted gene co-expression network analysis |
title_fullStr | Associating transcriptional modules with colon cancer survival through weighted gene co-expression network analysis |
title_full_unstemmed | Associating transcriptional modules with colon cancer survival through weighted gene co-expression network analysis |
title_short | Associating transcriptional modules with colon cancer survival through weighted gene co-expression network analysis |
title_sort | associating transcriptional modules with colon cancer survival through weighted gene co-expression network analysis |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5424422/ https://www.ncbi.nlm.nih.gov/pubmed/28486948 http://dx.doi.org/10.1186/s12864-017-3761-z |
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