High accordance in prognosis prediction of colorectal cancer across independent datasets by multi-gene module expression profiles.

A considerable portion of patients with colorectal cancer have a high risk of disease recurrence after surgery. These patients can be identified by analyzing the expression profiles of signature genes in tumors. But there is no consensus on which genes should be used and the performance of specific...

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Autores principales:	Wenting Li, Rui Wang, Zhangming Yan, Linfu Bai, Zhirong Sun
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/2f071752ad7c4f23b748bb2e6f9692d4
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spelling	oai:doaj.org-article:2f071752ad7c4f23b748bb2e6f9692d42021-11-18T07:24:56ZHigh accordance in prognosis prediction of colorectal cancer across independent datasets by multi-gene module expression profiles.1932-620310.1371/journal.pone.0033653https://doaj.org/article/2f071752ad7c4f23b748bb2e6f9692d42012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22438977/?tool=EBIhttps://doaj.org/toc/1932-6203A considerable portion of patients with colorectal cancer have a high risk of disease recurrence after surgery. These patients can be identified by analyzing the expression profiles of signature genes in tumors. But there is no consensus on which genes should be used and the performance of specific set of signature genes varies greatly with different datasets, impeding their implementation in the routine clinical application. Instead of using individual genes, here we identified functional multi-gene modules with significant expression changes between recurrent and recurrence-free tumors, used them as the signatures for predicting colorectal cancer recurrence in multiple datasets that were collected independently and profiled on different microarray platforms. The multi-gene modules we identified have a significant enrichment of known genes and biological processes relevant to cancer development, including genes from the chemokine pathway. Most strikingly, they recruited a significant enrichment of somatic mutations found in colorectal cancer. These results confirmed the functional relevance of these modules for colorectal cancer development. Further, these functional modules from different datasets overlapped significantly. Finally, we demonstrated that, leveraging above information of these modules, our module based classifier avoided arbitrary fitting the classifier function and screening the signatures using the training data, and achieved more consistency in prognosis prediction across three independent datasets, which holds even using very small training sets of tumors.Wenting LiRui WangZhangming YanLinfu BaiZhirong SunPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 3, p e33653 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Wenting Li Rui Wang Zhangming Yan Linfu Bai Zhirong Sun High accordance in prognosis prediction of colorectal cancer across independent datasets by multi-gene module expression profiles.
description	A considerable portion of patients with colorectal cancer have a high risk of disease recurrence after surgery. These patients can be identified by analyzing the expression profiles of signature genes in tumors. But there is no consensus on which genes should be used and the performance of specific set of signature genes varies greatly with different datasets, impeding their implementation in the routine clinical application. Instead of using individual genes, here we identified functional multi-gene modules with significant expression changes between recurrent and recurrence-free tumors, used them as the signatures for predicting colorectal cancer recurrence in multiple datasets that were collected independently and profiled on different microarray platforms. The multi-gene modules we identified have a significant enrichment of known genes and biological processes relevant to cancer development, including genes from the chemokine pathway. Most strikingly, they recruited a significant enrichment of somatic mutations found in colorectal cancer. These results confirmed the functional relevance of these modules for colorectal cancer development. Further, these functional modules from different datasets overlapped significantly. Finally, we demonstrated that, leveraging above information of these modules, our module based classifier avoided arbitrary fitting the classifier function and screening the signatures using the training data, and achieved more consistency in prognosis prediction across three independent datasets, which holds even using very small training sets of tumors.
format	article
author	Wenting Li Rui Wang Zhangming Yan Linfu Bai Zhirong Sun
author_facet	Wenting Li Rui Wang Zhangming Yan Linfu Bai Zhirong Sun
author_sort	Wenting Li
title	High accordance in prognosis prediction of colorectal cancer across independent datasets by multi-gene module expression profiles.
title_short	High accordance in prognosis prediction of colorectal cancer across independent datasets by multi-gene module expression profiles.
title_full	High accordance in prognosis prediction of colorectal cancer across independent datasets by multi-gene module expression profiles.
title_fullStr	High accordance in prognosis prediction of colorectal cancer across independent datasets by multi-gene module expression profiles.
title_full_unstemmed	High accordance in prognosis prediction of colorectal cancer across independent datasets by multi-gene module expression profiles.
title_sort	high accordance in prognosis prediction of colorectal cancer across independent datasets by multi-gene module expression profiles.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/2f071752ad7c4f23b748bb2e6f9692d4
work_keys_str_mv	AT wentingli highaccordanceinprognosispredictionofcolorectalcanceracrossindependentdatasetsbymultigenemoduleexpressionprofiles AT ruiwang highaccordanceinprognosispredictionofcolorectalcanceracrossindependentdatasetsbymultigenemoduleexpressionprofiles AT zhangmingyan highaccordanceinprognosispredictionofcolorectalcanceracrossindependentdatasetsbymultigenemoduleexpressionprofiles AT linfubai highaccordanceinprognosispredictionofcolorectalcanceracrossindependentdatasetsbymultigenemoduleexpressionprofiles AT zhirongsun highaccordanceinprognosispredictionofcolorectalcanceracrossindependentdatasetsbymultigenemoduleexpressionprofiles
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High accordance in prognosis prediction of colorectal cancer across independent datasets by multi-gene module expression profiles.

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