Identification novel Necroptosis-related LncRNAs to predict prognosis and screen the population benefiting from anti-PD-1/PDL-1 treatment in breast cancer

Abstract

Abstract Background: Breast Cancer (BC) is the most common cancer in the world. The rapid development of tumor immunotherapy and necroptosis has brought new directions to the treatment of breast cancer. To improve the situation, this project aims to identify biomarkers that can help predict prognosis and determine a precise treatment. Method: The Cancer Genome Atlas (TCGA) data was analyzed for coexpression relationships and univariate Cox regressions to identify lncRNAs associated with necroptosis.In addition, differential expression analysis, prognostic analysis, and time-dependent receiver operating characteristics (ROC) analysis were performed to determine whether Necroptosis-related lncRNA is an independent prognostic factor. Then, based on the risk model, principal component analysis (PCA), immune cell infiltration,immune functions, prediction of the half-maximal inhibitory concentration (IC50), and immune checkpoints were evaluated .Additionally, we divided the entire set into two clusters based on immunotherapy response between cold and hot tumors. Results: We developed a signature consisting of seven necroptosis-related LncRNAs. Over 1, 3, and 5 years, the area's OS under the ROC curve (AUC) was 0.748, 0.753, and 0.714, respectively. High-risk group immune cells were infiltrated less frequently and had lower immune functions than low-risk group immune cells. In addition, using clusters as a means to make a distinction between a cold tumor and a hot tumor could provide more precise treatment options.In Cluster 2, the tumor is considered to be hot tumor, which is more sensitive to immunotherapy drugs, such as anti-PD-1/PDL-1. Conclusion: According to our findings, necroptosis-related lncRNA can be used to predict prognosis and to distinguish cold and hot tumors in BC, thereby optimizing individual treatment.