Identification and comparison of two risk models based on characteristic gene sets of breast cancer for predictive prognosis

Background: Breast cancer (BC) is one of the main causes of cancer related deaths in women, and also has a high mortality rate in men, resulting in great healthy problems worldwide. Because of its low cure rate, poor late prognosis, and high mortality, it is of great significance to find new biomarkers for diagnosis and prognosis. Methods: : In this study, 1030 cases of BCs from The Cancer Genome Atlas (TCGA) were obtained for differential expression analysis and short time-series expression miner (STEM) analysis to identify the BC development characteristic genes, divided into up-regulated genes and down-regulated genes with the development of BC. Two predictive prognosis models were both defined by Lasso. Survival analysis and ROC curve analysis were used respectively to determine the diagnostic and prognostic abilities of the two gene set models scores. Univariate and multivariate Cox regression analyses were used to determine whether the risk models are independent prognostic indicators and whether our models are better than the present clinicopathological features in prognosis. Results: : Our findings from this study suggest both the breast cancer unfavorable (BC1) and the breast cancer favorable (BC2) gene sets are reliable biomarkers for the diagnosis and independent biomarkers for predictive prognosis of BC, while BC1 model presents better diagnostic and prognostic value than BC2 model. Associations between the models and macrophages M2, the sensitivity to Bortezomib were also found, implying that the BC unfavorable genes are significantly involved in the tumor immune microenvironment of BC. Conclusion: In conclusion, we successfully established two predictive prognosis models based on characteristic gene sets of BC, and one prognosis model (BC1) to diagnosis and predict the survival time of BC patients using a cluster of 12 DEGs. Additionally, the high infiltration of macrophages M2 and high sensitivity to Bortezomib in high risk group scored by our risk models provided theoretical support for further basic and clinical research on the tumor immune microenvironment of BC.