ArticlePloS one2011
A signature inferred from Drosophila mitotic genes predicts survival of breast cancer patients.
Article in PloS one, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Prognostic genes of triple-negative breast cancer identified by weighted gene co-expression network analysis.Oncology letters · 2020Article
- Poly-omic prediction of complex traits: OmicKriging.Genetic epidemiology · 2014Article
- Computer-based image studies on tumor nests mathematical features of breast cancer and their clinical prognostic value.PloS one · 2013Article
- Mixed effects modeling of proliferation rates in cell-based models: consequence for pharmacogenomics and cancer.PLoS genetics · 2012Article
- Weighted frequent gene co-expression network mining to identify genes involved in genome stability.PLoS computational biology · 2012Article
- Prediction of breast cancer metastasis by gene expression profiles: a comparison of metagenes and single genes.Cancer informatics · 2012Article
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Authors and funding
6 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionThe classification of breast cancer patients into risk groups provides a powerful tool for the identification of patients who will benefit from aggressive systemic therapy. The analysis of microarray data has generated several gene expression signatures that improve diagnosis and allow risk assessment. There is also evidence that cell proliferation-related genes have a high predictive power within these signatures.
methodsWe thus constructed a gene expression signature (the DM signature) using the human orthologues of 108 Drosophila melanogaster genes required for either the maintenance of chromosome integrity (36 genes) or mitotic division (72 genes).
resultsThe DM signature has minimal overlap with the extant signatures and is highly predictive of survival in 5 large breast cancer datasets. In addition, we show that the DM signature outperforms many widely used breast cancer signatures in predictive power, and performs comparably to other proliferation-based signatures. For most genes of the DM signature, an increased expression is negatively correlated with patient survival. The genes that provide the highest contribution to the predictive power of the DM signature are those involved in cytokinesis.
conclusionThis finding highlights cytokinesis as an important marker in breast cancer prognosis and as a possible target for antimitotic therapies.
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