Identification of a Prognostic Model for Breast Cancer Based on Genes Associated with Neutrophil Extracellular Traps

Abstract

Abstract We know that cancer is rich in neutrophil extracellular traps (NETs) and NETs can promote breast cancer (BC) metastasis, but whether NETs-related genes are associated with the prognosis of BC patients is unclear. As part of this study, we used the TCGA database to obtain 1113 BC samples and 113 normal samples and screened for 102 differentially expressed genes associated with NETs. Following that, we modeled the prognostic risk for six genes (CYBA, RAC2, ITGAL, C3 down-regulated and VDAC1, SLC25A5 up-regulated) using multivariate Cox regression and LASSO regression analyses. In order to determine the risk groups for BC patients, we calculated a risk score and then classified the patients into high and low risk groups based on their median risk value. A significant difference in survival rates was found between high-risk and low-risk BC patients (p < 0.001), according to Kaplan-Meier survival analysis. The same conclusion was obtained for the dataset we obtained in the GEO database. An independent prognostic analysis of the constructed model revealed that the risk score correlated with BC survival independently of other clinical features. And the clinical correlation analysis showed that the change model correlated with the patient's age, gender, the stage of the tumor and the T-stage of the tumor. Furthermore, the risk values of our constructed Nomogram model were less than 0.01 in both univariate and multivariate, correlated with BC prognosis, and were independent of other clinical characteristics. According to the analysis of mutated genes in BC patients, the mutated genes in high and low risk BC patients were PIK3CA, TP53, TTN, CDH1, GATA3, MUC16, KMT2C, MAP3K1, HMCN1, RYR2, FLG, USH2A, SYNE2, ZFHX5 and PTEN. A comparison of immune cell differences between high and low risk groups revealed relatively lower levels of infiltrating immune cells in the high risk group. It is concluded that BC patients' prognosis can be independently predicted by risk profiles derived from the NET-related gene expression.