Construction and validation of a cuproptosis-related five-lncRNA signature for predicting prognosis, immune response and drug sensitivity in breast cancer.

BACKGROUND

Despite advances in treatment, recurrence and mortality rates from breast cancer (BrCa) continue to rise, clinical effectiveness is limited, and prognosis remains disappointing, especially for patients with HER2-positive, triple-negative, or advanced breast cancer. Based on cuproptosis-related long noncoding RNAs (CRLs), this study aims to create a predictive signature to assess the prognosis in patients with BrCa.

METHODS

The related CRLs RNA-seq data clinicopathological data were collected from The Cancer Genome Atlas (TCGA) database, and the predictive model was constructed after correlation analysis. Subsequently, we examined and validated connections and changes in the CRLs model with prognostic features (including risk curves, ROC curves and nomograms), pathway and functional enrichment, tumor mutation (TMB), tumor immune dysfunction and exclusion (TIDE) and treatment sensitivity.

RESULTS

A prediction model formula composed of 5 CRLs was obtained, and divided breast cancer patients into high and low risk subgroups according to the obtained risk scores. The results showed that the overall survival (OS) of patients in the high-risk group was lower than that in the low-risk group, and the AUC of all samples at 1, 3 and 5 years were 0.704, 0.668 and 0.647, respectively. It was indicated that CRLs prognostic model could independently predict prognostic indicators of BrCa patients. In addition, analysis of gene set enrichment, immune function, TMB, and TIDE showed that these differentially expressed CRLs had a wealth of related pathways and functions, and might be closely related to immune response and immune microenvironment. Additionally, TP53 was found to have the highest mutation frequency in high-risk group (40%), while PIK3CA was found to have the highest mutation frequency in low-risk group (42%), which might become new targets for targeted therapy. Finally, we compared susceptibility to anticancer agents to identify potential treatment options for breast cancer. Lapatinib, Sunitinib, Phenformin, Idelalisib, Ruxolitinib, Cabozantinib were more sensitive to patients in the low-risk group, while Sorafenib, Vinorelbine, Pyrimethamine were more sensitive to patients in high-risk group, namely, these drugs could potentially be used in the future to treat breast cancer patients grouped according to the risk model.

CONCLUSION

This study identified CRLs associated with breast cancer and provided a tailored tool for predicting prognosis, immune response, and drug sensitivity in patients with BrCa.