ArticleBMC systems biology2011
Integration of breast cancer gene signatures based on graph centrality.
Article in BMC systems biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 46 citations in OpenAlex.
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- A side-effect free method for identifying cancer drug targets.Scientific reports · 2018Article
- Polypharmacology-based ceritinib repurposing using integrated functional proteomics.Nature chemical biology · 2017Article
- Graph-theoretical comparison of normal and tumor networks in identifying BRCA genes.BMC systems biology · 2017Article
- Transcriptomics reveals multiple resistance mechanisms against cotton leaf curl disease in a naturally immune cotton species, Gossypium arboreum.Scientific reports · 2017Article
- Global temporal dynamic landscape of pathogen-mediated subversion of Arabidopsis innate immunity.Scientific reports · 2017Article
- Predicting disease-related genes using integrated biomedical networks.BMC genomics · 2017Article
- Neighbours of cancer-related proteins have key influence on pathogenesis and could increase the drug target space for anticancer therapies.NPJ systems biology and applications · 2017Article
- Computational dynamic approaches for temporal omics data with applications to systems medicine.BioData mining · 2017Review
- Graph theory and stability analysis of protein complex interaction networks.IET systems biology · 2016Article
- Identification of important interacting proteins (IIPs) in Plasmodium falciparum using large-scale interaction network analysis and in-silico knock-out studies.Malaria journal · 2015Article
- A comparative study of disease genes and drug targets in the human protein interactome.BMC bioinformatics · 2015Article
- A system based network approach to ethanol tolerance in Saccharomyces cerevisiae.BMC systems biology · 2014Article
- ComPlEx: conservation and divergence of co-expression networks in A. thaliana, Populus and O. sativa.BMC genomics · 2014Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundVarious 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.
resultsIn 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.
conclusionsIn 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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.