Prognostic and immune microenvironment analysis of cuproptosis-related LncRNAs in breast cancer.

Breast cancer is the most common tumor and the leading cause of cancer death in women. Cuproptosis is a new type of cell death, which can induce proteotoxic stress and eventually lead to cell death. Therefore, regulating copper metabolism in tumor cells is a new therapeutic approach. Long non-coding RNAs play an important regulatory role in immune response. At present, cuproptosis-related lncRNAs in breast cancer have not been reported. Breast cancer RNA sequencing, genomic mutations, and clinical data were downloaded from The Cancer Genome Atlas (TCGA). Patients with breast cancer were randomly assigned to the train group or the test group. Co-expression network analysis, Cox regression method, and least absolute shrinkage and selection operator (LASSO) method were used to identify cuproptosis-related lncRNAs and to construct a risk prognostic model. The prediction performance of the model is verified and recognized. In addition, the nomogram was used to predict the prognosis of breast cancer patients. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and immunoassay were used to detect the differences in biological function. Tumor mutation burden (TMB) was used to measure immunotherapy response. A total of 19 cuproptosis genes were obtained and a prognostic model based on 10 cuproptosis-related lncRNAs was constructed. Kaplan-Meier survival curves showed statistically significant overall survival (OS) between the high-risk and low-risk groups. Receiver operating characteristic curve (ROC) and principal component analysis (PCA) show that the model has accurate prediction ability. Compared with other clinical features, cuproptosis-related lncRNAs model has higher diagnostic efficiency. Univariate and multivariate Cox regression analysis showed that risk score was an independent prognostic factor for breast cancer patients. In addition, the nomogram model analysis showed that the tumor mutation burden was significantly different between the high-risk and low-risk groups. Of note, the additive effect of patients in the high-risk group and patients with high TMB resulted in reduced survival in breast cancer patients. Our study identified 10 cuproptosis-related lncRNAs, which may be promising biomarkers for predicting the survival prognosis of breast cancer.