Necrotic Apoptosis -Associated Signature Predicts Prognosis and Immunotherapy in Triple-Negative Breast Cancer

Abstract Background Triple-negative breast cancer (TNBC) lacks targeted therapies and is associated with a poor prognosis, especially for women. Necrotic plays a critical role in the progression of TNBC. To investigate the prognosis of TNBC patients, we aimed to explore characteristics of Necrotic apoptosis (NRGs) and construct a risk signature based on NRGs. Methods The TNBC transcriptome and corresponding were obtained from the TCGA database. Ninety-nine normal mammary epithelial tissue samples from the GTEx database were analyzed. Genes associated with NRGs were extracted from the MSigDB database. We conducted differential gene expression analysis using the limma package. Cox regressions and LASSO were analyzed to identify the genes associated with NRGs. Predictive models were constructed using multivariate Cox regression analysis. The K-M survival curve and the time-dependent receiver operating characteristic (ROC) curve were used to evaluate the predictive ability of the prognostic model. The fractions of immune cells were determined using the CIBERSORT algorithm. In this study, we investigated somatic mutations in the analyzed samples and utilized our findings to predict the potential effectiveness of immunotherapy in patients. The expression patterns of risk genes were analyzed using real-time quantitative PCR and Western blot analysis. Results A total of 200 differentially expressed NRGs were acquired. A risk model containing three NRGs. The high-risk group demonstrates a significantly shorter survival time than the low-risk group (p < 0.05). The ROC curve areas for 3-year, 5-year, and 8-year survival were 0.891, 0.833, and 0.845, respectively. This model exhibited highly accurate prognostic predictions in both the training and test data sets, and it proved to be an independent prognostic factor. An analysis of the immune environment and immunotherapy was conducted. High-risk and low-risk groups differed significantly in gene mutations. Western blotting and RT-qPCR revealed significantly higher CCL25 and GGT7 expression (p < 0.05) in cancer tissues, whereas TNSRSF11B expression was lower. Conclusion Our study has resulted in the development of independent prognostic indicators for TNBC, which can aid in the customized treatment of patients with varying levels of risk. We analyzed genetic mutations, which offered new insights into the immunological properties of the high and low-risk subgroups, and evaluated the possibility of incorporating immunotherapy into personalized breast cancer management.