Lili Hou1, Shuang Hou1, Lei Yin1, Shuai Zhao1, Xiaohua Li1. 1. Department of Breast and Thyroid Surgery, Wuzhong People's Hospital of Suzhou City, Suzhou, 215128, People's Republic of China.
Abstract
Purpose: Regulation of inducers and transcription factor families influence epithelial-mesenchymal transition (EMT), a contributing factor to breast cancer invasion and progression. Methods: Molecular subtypes were classified based on EMT-related mRNAs using ConsensusClusterPlus package. Differences in tumor immune microenvironment and prognosis were assessed among subtypes. Based on EMT genes, a gene signature for prognosis was built using TCGA training set by performing multivariate and univariate Cox regression analyses. Prediction accuracy of the signature was validated by receiver operating characteristic (ROC) curves and overall survival analysis on internal and external datasets. By conducting univariate and multivariate Cox regression analyses, the risk signature as an independent prognostic indicator was assessed. A nomogram was constructed and validated by calibration analysis and decision curve analysis (DCA). Results: Five molecular subtypes were characterized based on EMT genes. Patients in Cluster 2 exhibited an activated immune state and a better prognosis. An 11-EMT gene-signature was built to predict breast cancer prognosis. After validation, the signature showed independence and robustness in predicting clinical outcomes of patients. A nomogram combining the RiskScore and pTNM_stage accurately predicted 1-, 2-, 3-, and 5-year survival chance. In comparison with published model, the current model showed a higher area under the curve (AUC). Conclusion: We characterized five breast cancer subtypes with distinct clinical outcomes and immune status. The study developed an 11-EMT gene-signature as an independent prognostic factor for predicting clinical outcomes of breast cancer.
Purpose: Regulation of inducers and transcription factor families influence epithelial-mesenchymal transition (EMT), a contributing factor to breast cancer invasion and progression. Methods: Molecular subtypes were classified based on EMT-related mRNAs using ConsensusClusterPlus package. Differences in tumor immune microenvironment and prognosis were assessed among subtypes. Based on EMT genes, a gene signature for prognosis was built using TCGA training set by performing multivariate and univariate Cox regression analyses. Prediction accuracy of the signature was validated by receiver operating characteristic (ROC) curves and overall survival analysis on internal and external datasets. By conducting univariate and multivariate Cox regression analyses, the risk signature as an independent prognostic indicator was assessed. A nomogram was constructed and validated by calibration analysis and decision curve analysis (DCA). Results: Five molecular subtypes were characterized based on EMT genes. Patients in Cluster 2 exhibited an activated immune state and a better prognosis. An 11-EMT gene-signature was built to predict breast cancer prognosis. After validation, the signature showed independence and robustness in predicting clinical outcomes of patients. A nomogram combining the RiskScore and pTNM_stage accurately predicted 1-, 2-, 3-, and 5-year survival chance. In comparison with published model, the current model showed a higher area under the curve (AUC). Conclusion: We characterized five breast cancer subtypes with distinct clinical outcomes and immune status. The study developed an 11-EMT gene-signature as an independent prognostic factor for predicting clinical outcomes of breast cancer.
Authors: Steven T Sizemore; Gina M Sizemore; Christine N Booth; Cheryl L Thompson; Paula Silverman; Gurkan Bebek; Fadi W Abdul-Karim; Stefanie Avril; Ruth A Keri Journal: Breast Cancer Res Treat Date: 2014-05-22 Impact factor: 4.872
Authors: Carol E DeSantis; Jiemin Ma; Mia M Gaudet; Lisa A Newman; Kimberly D Miller; Ann Goding Sauer; Ahmedin Jemal; Rebecca L Siegel Journal: CA Cancer J Clin Date: 2019-10-02 Impact factor: 508.702