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A computational model to predict bone metastasis in breast cancer by integrating the dysregulated pathways

BACKGROUND: Although there are a lot of researches focusing on cancer prognosis or prediction of cancer metastases, it is still a big challenge to predict the risks of cancer metastasizing to a specific organ such as bone. In fact, little work has been published for such a purpose nowadays. METHODS:...

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Autores principales: Zhou, Xionghui, Liu, Juan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4161863/
https://www.ncbi.nlm.nih.gov/pubmed/25163697
http://dx.doi.org/10.1186/1471-2407-14-618
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author Zhou, Xionghui
Liu, Juan
author_facet Zhou, Xionghui
Liu, Juan
author_sort Zhou, Xionghui
collection PubMed
description BACKGROUND: Although there are a lot of researches focusing on cancer prognosis or prediction of cancer metastases, it is still a big challenge to predict the risks of cancer metastasizing to a specific organ such as bone. In fact, little work has been published for such a purpose nowadays. METHODS: In this work, we propose a Dysregulated Pathway Based prediction Model (DPBM) built on a merged data set with 855 samples. First, we use bootstrapping strategy to select bone metastasis related genes. Based on the selected genes, we then detect out the dysregulated pathways involved in the process of bone metastasis via enrichment analysis. And then we use the discriminative genes in each dysregulated pathway, called as dysregulated genes, to construct a sub-model to forecast the risk of bone metastasis. Finally we combine all sub-models as an ensemble model (DPBM) to predict the risk of bone metastasis. RESULTS: We have validated DPBM on the training, test and independent sets separately, and the results show that DPBM can significantly distinguish the bone metastases risks of patients (with p-values of 3.82E-10, 0.00007 and 0.0003 on three sets respectively). Moreover, the dysregulated genes are generally with higher topological coefficients (degree and betweenness centrality) in the PPI network, which means that they may play critical roles in the biological functions. Further functional analysis of these genes demonstrates that the immune system seems to play an important role in bone-specific metastasis of breast cancer. CONCLUSIONS: Each of the dysregulated pathways that are enriched with bone metastasis related genes may uncover one critical aspect of influencing the bone metastasis of breast cancer, thus the ensemble strategy can help to describe the comprehensive view of bone metastasis mechanism. Therefore, the constructed DPBM is robust and able to significantly distinguish the bone metastases risks of patients in both test set and independent set. Moreover, the dysregulated genes in the dysregulated pathways tend to play critical roles in the biological process of bone metastasis of breast cancer. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2407-14-618) contains supplementary material, which is available to authorized users.
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spelling pubmed-41618632014-09-13 A computational model to predict bone metastasis in breast cancer by integrating the dysregulated pathways Zhou, Xionghui Liu, Juan BMC Cancer Research Article BACKGROUND: Although there are a lot of researches focusing on cancer prognosis or prediction of cancer metastases, it is still a big challenge to predict the risks of cancer metastasizing to a specific organ such as bone. In fact, little work has been published for such a purpose nowadays. METHODS: In this work, we propose a Dysregulated Pathway Based prediction Model (DPBM) built on a merged data set with 855 samples. First, we use bootstrapping strategy to select bone metastasis related genes. Based on the selected genes, we then detect out the dysregulated pathways involved in the process of bone metastasis via enrichment analysis. And then we use the discriminative genes in each dysregulated pathway, called as dysregulated genes, to construct a sub-model to forecast the risk of bone metastasis. Finally we combine all sub-models as an ensemble model (DPBM) to predict the risk of bone metastasis. RESULTS: We have validated DPBM on the training, test and independent sets separately, and the results show that DPBM can significantly distinguish the bone metastases risks of patients (with p-values of 3.82E-10, 0.00007 and 0.0003 on three sets respectively). Moreover, the dysregulated genes are generally with higher topological coefficients (degree and betweenness centrality) in the PPI network, which means that they may play critical roles in the biological functions. Further functional analysis of these genes demonstrates that the immune system seems to play an important role in bone-specific metastasis of breast cancer. CONCLUSIONS: Each of the dysregulated pathways that are enriched with bone metastasis related genes may uncover one critical aspect of influencing the bone metastasis of breast cancer, thus the ensemble strategy can help to describe the comprehensive view of bone metastasis mechanism. Therefore, the constructed DPBM is robust and able to significantly distinguish the bone metastases risks of patients in both test set and independent set. Moreover, the dysregulated genes in the dysregulated pathways tend to play critical roles in the biological process of bone metastasis of breast cancer. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/1471-2407-14-618) contains supplementary material, which is available to authorized users. BioMed Central 2014-08-27 /pmc/articles/PMC4161863/ /pubmed/25163697 http://dx.doi.org/10.1186/1471-2407-14-618 Text en © Zhou and Liu; licensee BioMed Central Ltd. 2014 This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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
Zhou, Xionghui
Liu, Juan
A computational model to predict bone metastasis in breast cancer by integrating the dysregulated pathways
title A computational model to predict bone metastasis in breast cancer by integrating the dysregulated pathways
title_full A computational model to predict bone metastasis in breast cancer by integrating the dysregulated pathways
title_fullStr A computational model to predict bone metastasis in breast cancer by integrating the dysregulated pathways
title_full_unstemmed A computational model to predict bone metastasis in breast cancer by integrating the dysregulated pathways
title_short A computational model to predict bone metastasis in breast cancer by integrating the dysregulated pathways
title_sort computational model to predict bone metastasis in breast cancer by integrating the dysregulated pathways
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4161863/
https://www.ncbi.nlm.nih.gov/pubmed/25163697
http://dx.doi.org/10.1186/1471-2407-14-618
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