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Immune-related gene based prognostic signature for the risk stratification analysis of breast cancer

Author(s):

Dongqing Su, Qianzi Lu, Yi Pan, Yao Yu, Shiyuan Wang, Yongchun Zuo* and Lei Yang*  

Abstract:


Background: Breast cancer has plagued women for many years and caused many deaths around the world.

Method: In this study, based on the weighted correlation network analysis, univariate Cox regression analysis and least absolute shrinkage and selection operator, 12 immune-related genes were selected to construct the risk score for breast cancer patients. The multivariable Cox regression analysis, gene set enrichment analysis and nomogram were also conducted in this study.

Results: Good results were obtained in the survival analysis, enrichment analysis, multivariable Cox regression analysis and immune-related feature analysis. When the risk score model was applied in 22 breast cancer cohorts, the univariate Cox regression analysis demonstrated that the risk score model was significantly associated with overall survival in most of the breast cancer cohorts.

Conclusion: Based on these results, we could conclude that the proposed risk score model may be a promising method, and may improve the treatment stratification of breast cancer patients in the future work.

Keywords:

breast cancer, risk score model, immune-related gene, prognosis, enrichment analysis, malignant.

Affiliation:

College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, The State Key Laboratory of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University, Hohhot, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin



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