Development and verification of a novel disulfidptosis-related lncRNA prognostic model for predicting immune environment and treatment of breast cancer

Background. Breast cancer (BRCA) is the leading factor in female tumor-related deaths. Disulfidptosis is a recently identified kind of cell death that may present fresh possibilities for the treatment of cancer. But it's uncertain whether disulfidptosis-related lncRNAs (DRlncRNAs) are connected to BRCA. The primary objective of this study was to develop a prognostic model for BRCA based on DRlncRNAs and investigate variations in immunological treatment responses among different patient groups.Methods. We identified four DRlncRNAs using BRCA transcriptional expression profiles from The Cancer Genome Atlas (TCGA) database to construct the prognostic model. The effectiveness of this model was assessed through Kaplan-Meier survival analysis, independent prognostic analysis, and time-dependent receiver operating characteristic (ROC) analysis. We also conducted Gene Set Enrichment Analysis (GSEA) to elucidate variations in the biological functions of distinct patient groups. Additionally, we assessed the tumor microenvironment (TME) and utilized tumor mutation burden (TMB) as a metric to evaluate the potential efficacy of immunotherapy. Finally, drug sensitivity was also estimated in two risk groups.Results. We identified a set of 427 DRlncRNAs by Pearson correlation analysis, from which we selected four prognostically relevant DRlncRNAs to construct our prognostic model. Subsequently, we categorized all patients into high-risk and low-risk groups. The Kaplan-Meier survival curves clearly demonstrated that patients in the high-risk group had a less favorable prognosis. Furthermore, our analysis revealed that immune cells in the high-risk group exhibited heightened activity and higher expression levels of immune checkpoint genes such as CD28, CD80, CD86, PDCD1LG2 (PD-L2), and NRP1. Meanwhile, high TMB had a worse prognosis. In drug sensitivity analysis, it was found that patients were shown to be more susceptible to various targeted medicines in the low-risk group.Conclusion. These findings underscore the efficacy of DRlncRNA models in predicting prognosis and treatment responses, potentially opening new avenues for treatment options for BRCA patients.