Identification of prognostic genes for recurrent risk prediction in triple negative breast cancer patients in Taiwan

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Abstract

Discrepancies in the prognosis of triple negative breast cancer exist between Caucasian and Asian populations. Yet, the gene signature of triple negative breast cancer specifically for Asians has not become available. Therefore, the purpose of this study is to construct a prediction model for recurrence of triple negative breast cancer in Taiwanese patients. Whole genome expression profiling of breast cancers from 185 patients in Taiwan from 1995 to 2008 was performed, and the results were compared to the previously published literature to detect differences between Asian and Western patients. Pathway analysis and Cox proportional hazard models were applied to construct a prediction model for the recurrence of triple negative breast cancer. Hierarchical cluster analysis showed that triple negative breast cancers from different races were in separate sub-clusters but grouped in a bigger cluster. Two pathways, cAMP-mediated signaling and ephrin receptor signaling, were significantly associated with the recurrence of triple negative breast cancer. After using stepwise model selection from the combination of the initial filtered genes, we developed a prediction model based on the genes SLC22A23, PRKAG3, DPEP3, MORC2, GRB7, and FAM43A. The model had 91.7% accuracy, 81.8% sensitivity, and 94.6% specificity under leave-one-out support vector regression. In this study, we identified pathways related to triple negative breast cancer and developed a model to predict its recurrence. These results could be used for assisting with clinical prognosis and warrant further investigation into the possibility of targeted therapy of triple negative breast cancer in Taiwanese patients. © 2011 Chen et al.

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Chen, L. H., Kuo, W. H., Tsai, M. H., Chen, P. C., Hsiao, C. K., Chuang, E. Y., … Chang, K. J. (2011). Identification of prognostic genes for recurrent risk prediction in triple negative breast cancer patients in Taiwan. PLoS ONE, 6(11). https://doi.org/10.1371/journal.pone.0028222

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