Cargando…

Integration of breast cancer gene signatures based on graph centrality

BACKGROUND: Various gene-expression signatures for breast cancer are available for the prediction of clinical outcome. However due to small overlap between different signatures, it is challenging to integrate existing disjoint signatures to provide a unified insight on the association between gene e...

Descripción completa

Detalles Bibliográficos
Autores principales: Wang, Jianxin, Chen, Gang, Li, Min, Pan, Yi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287565/
https://www.ncbi.nlm.nih.gov/pubmed/22784616
http://dx.doi.org/10.1186/1752-0509-5-S3-S10
_version_ 1782224692405338112
author Wang, Jianxin
Chen, Gang
Li, Min
Pan, Yi
author_facet Wang, Jianxin
Chen, Gang
Li, Min
Pan, Yi
author_sort Wang, Jianxin
collection PubMed
description BACKGROUND: Various gene-expression signatures for breast cancer are available for the prediction of clinical outcome. However due to small overlap between different signatures, it is challenging to integrate existing disjoint signatures to provide a unified insight on the association between gene expression and clinical outcome. RESULTS: In this paper, we propose a method to integrate different breast cancer gene signatures by using graph centrality in a context-constrained protein interaction network (PIN). The context-constrained PIN for breast cancer is built by integrating complete PIN and various gene signatures reported in literatures. Then, we use graph centralities to quantify the importance of genes to breast cancer. Finally, we get reliable gene signatures that are consisted by the genes with high graph centrality. The genes which are well-known breast cancer genes, such as TP53 and BRCA1, are ranked extremely high in our results. Compared with previous results by functional enrichment analysis, graph centralities, especially the eigenvector centrality and subgraph centrality, based gene signatures are more tightly related to breast cancer. We validate these signatures on genome-wide microarray dataset and found strong association between the expression of these signature genes and pathologic parameters. CONCLUSIONS: In summary, graph centralities provide a novel way to connect different cancer signatures and to understand the mechanism of relationship between gene expression and clinical outcome of breast cancer. Moreover, this method is not only can be used on breast cancer, but also can be used on other gene expression related diseases and drug studies.
format Online
Article
Text
id pubmed-3287565
institution National Center for Biotechnology Information
language English
publishDate 2011
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-32875652012-03-01 Integration of breast cancer gene signatures based on graph centrality Wang, Jianxin Chen, Gang Li, Min Pan, Yi BMC Syst Biol Research Article BACKGROUND: Various gene-expression signatures for breast cancer are available for the prediction of clinical outcome. However due to small overlap between different signatures, it is challenging to integrate existing disjoint signatures to provide a unified insight on the association between gene expression and clinical outcome. RESULTS: In this paper, we propose a method to integrate different breast cancer gene signatures by using graph centrality in a context-constrained protein interaction network (PIN). The context-constrained PIN for breast cancer is built by integrating complete PIN and various gene signatures reported in literatures. Then, we use graph centralities to quantify the importance of genes to breast cancer. Finally, we get reliable gene signatures that are consisted by the genes with high graph centrality. The genes which are well-known breast cancer genes, such as TP53 and BRCA1, are ranked extremely high in our results. Compared with previous results by functional enrichment analysis, graph centralities, especially the eigenvector centrality and subgraph centrality, based gene signatures are more tightly related to breast cancer. We validate these signatures on genome-wide microarray dataset and found strong association between the expression of these signature genes and pathologic parameters. CONCLUSIONS: In summary, graph centralities provide a novel way to connect different cancer signatures and to understand the mechanism of relationship between gene expression and clinical outcome of breast cancer. Moreover, this method is not only can be used on breast cancer, but also can be used on other gene expression related diseases and drug studies. BioMed Central 2011-12-23 /pmc/articles/PMC3287565/ /pubmed/22784616 http://dx.doi.org/10.1186/1752-0509-5-S3-S10 Text en Copyright ©2011 Wang et al. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Wang, Jianxin
Chen, Gang
Li, Min
Pan, Yi
Integration of breast cancer gene signatures based on graph centrality
title Integration of breast cancer gene signatures based on graph centrality
title_full Integration of breast cancer gene signatures based on graph centrality
title_fullStr Integration of breast cancer gene signatures based on graph centrality
title_full_unstemmed Integration of breast cancer gene signatures based on graph centrality
title_short Integration of breast cancer gene signatures based on graph centrality
title_sort integration of breast cancer gene signatures based on graph centrality
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3287565/
https://www.ncbi.nlm.nih.gov/pubmed/22784616
http://dx.doi.org/10.1186/1752-0509-5-S3-S10
work_keys_str_mv AT wangjianxin integrationofbreastcancergenesignaturesbasedongraphcentrality
AT chengang integrationofbreastcancergenesignaturesbasedongraphcentrality
AT limin integrationofbreastcancergenesignaturesbasedongraphcentrality
AT panyi integrationofbreastcancergenesignaturesbasedongraphcentrality