Purpose: Breast cancer is the most common cancer in women. The aim of this study was to build a prognostic signatures model based on the immune score of the ESTIMATE algorithm to predict survival of breast cancer patients.
Methods: The RNA-seq expression data and clinical characteristics of patients were derived from TCGA and GSE88770 of GEO. The ESTIMATE algorithm was used to calculate the patients' immune scores and to obtain DEGs. The LASSO Cox regression model was applied to select prognostic genes. Survival analysis and the ROC curve were used to evaluate the predictive efficacy of the prognostic signatures model. Independent prognostic factors of breast cancer were assessed using the Cox regression analyses, and a nomogram was constructed to enhance the clinical value.
Results: Based on the immune score, we found that the high-score group showed better clinical outcomes than the low-score group. Twenty-five (25) genes of 616 DEGs were confirmed as prognostic signatures through the LASSO Cox regression. The risk score for each patient was calculated according to the prognostic signatures. Survival analysis showed that the low-risk group had longer overall survival than the high-risk group. We also found that the risk score was an independent prognostic factor. To improve the clinical application value, a nomogram combing the risk score according to the 25-gene prognostic signatures and several clinicopathological prognostic factors was constructed.
Conclusions: This study revealed the significance of immune infiltration and constructed a 25-gene prognostic signatures model, that has a strong prognostic value for patients with breast cancer.
Keywords: TCGA; breast cancer; cancer genetics; prognosis.
© 2021 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.