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Pathway analysis of gene signatures predicting metastasis of node-negative primary breast cancer

BACKGROUND: Published prognostic gene signatures in breast cancer have few genes in common. Here we provide a rationale for this observation by studying the prognostic power and the underlying biological pathways of different gene signatures. METHODS: Gene signatures to predict the development of me...

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Detalles Bibliográficos
Autores principales: Yu, Jack X, Sieuwerts, Anieta M, Zhang, Yi, Martens, John WM, Smid, Marcel, Klijn, Jan GM, Wang, Yixin, Foekens, John A
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2007
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2077336/
https://www.ncbi.nlm.nih.gov/pubmed/17894856
http://dx.doi.org/10.1186/1471-2407-7-182
Descripción
Sumario:BACKGROUND: Published prognostic gene signatures in breast cancer have few genes in common. Here we provide a rationale for this observation by studying the prognostic power and the underlying biological pathways of different gene signatures. METHODS: Gene signatures to predict the development of metastases in estrogen receptor-positive and estrogen receptor-negative tumors were identified using 500 re-sampled training sets and mapping to Gene Ontology Biological Process to identify over-represented pathways. The Global Test program confirmed that gene expression profilings in the common pathways were associated with the metastasis of the patients. RESULTS: The apoptotic pathway and cell division, or cell growth regulation and G-protein coupled receptor signal transduction, were most significantly associated with the metastatic capability of estrogen receptor-positive or estrogen-negative tumors, respectively. A gene signature derived of the common pathways predicted metastasis in an independent cohort. Mapping of the pathways represented by different published prognostic signatures showed that they share 53% of the identified pathways. CONCLUSION: We show that divergent gene sets classifying patients for the same clinical endpoint represent similar biological processes and that pathway-derived signatures can be used to predict prognosis. Furthermore, our study reveals that the underlying biology related to aggressiveness of estrogen receptor subgroups of breast cancer is quite different.