Affiliation:
1. College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China
2. The State Key Laboratory
of Reproductive Regulation and Breeding of Grassland Livestock, College of Life Sciences, Inner Mongolia University,
Hohhot, China
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.
Publisher
Bentham Science Publishers Ltd.
Subject
Computational Mathematics,Genetics,Molecular Biology,Biochemistry
Cited by
5 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献