Predicting the Prognosis and Immunotherapeutic Response of Triple-Negative Breast Cancer by Constructing a Prognostic Model Based on CD8+T Cell-Related Immune Genes* *

Objective Triple-negative breast cancer (TNBC) poses a significant challenge for treatment efficacy. CD8+ T cells, which are pivotal immune cells, can be effectively analyzed for differential gene expression across diverse cell populations owing to rapid advancements in sequencing technology. By leveraging these genes, our objective was to develop a prognostic model that accurately predicts the prognosis of patients with TNBC and their responsiveness to immunotherapy. Methods Sample information and clinical data of TNBC were sourced from The Cancer Genome Atlas and METABRIC databases. In the initial stage, we identified 67 differentially expressed genes associated with immune response in CD8+ T cells. Subsequently, we narrowed our focus to three key genes, namely CXCL13, , GBP2, , and GZMB, , which were used to construct a prognostic model. The accuracy of the model was assessed using the validation set data and receiver operating characteristic (ROC) curves. Furthermore, we employed various methods, including Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, immune infiltration, and correlation analyses with CD274 (PD-L1) to explore the model's predictive efficacy in immunotherapeutic responses. Additionally, we investigated the potential underlying biological pathways that contribute to divergent treatment responses. Results We successfully developed a model capable of predicting the prognosis of patients with TNBC. The areas under the curve (AUC) values for the 1-, 3-, and 5-year survival predictions were 0.618, 0.652, and 0.826, respectively. Employing this risk model, we stratified the samples into high- and low-risk groups. Through KEGG enrichment analysis, we observed that the high-risk group predominantly exhibited enrichment in metabolism-related pathways such as drug and chlorophyll metabolism, whereas the low-risk group demonstrated significant enrichment in cytokine pathways. Furthermore, immune landscape analysis revealed noteworthy variations between (PD-L1) expression and risk scores, indicating that our model effectively predicted the response of patients to immune-based treatments. Conclusion Our study demonstrates the potential of CXCL13, GBP2, and GZMB as prognostic indicators of clinical outcomes and immunotherapy responses in patients with TNBC. These findings provide valuable insights and novel avenues for developing immunotherapeutic approaches targeting TNBC.