Identification and validation of a prognostic model for HER2-low breast cancer based on unfolded protein response-related genes.

Objectives: Human epidermal growth factor receptor 2 (HER2) is down-regulated in approximately 45-55% of breast cancer patients. Cancer cells activate endoplasmic reticulum stress, which is counteracted by the unfolded protein response (UPR). The objective of the present research is to assess the predictive significance of UPR-related genes and investigate their effects on the immune landscape in HER2-low breast cancer patients.

Methods: The prognostic UPR related genes in patients were identified by univariate Cox regression analysis, and the risk score model was created using LASSO Cox regression analysis. The nomogram model was built using RMS package, and the protein-protein interaction network was created via the STRING database.

Results: Through comprehensive analysis, we identified four UPR-related genes (COPS5, DKC1, NOP56, and EIF4G1) that were significantly associated with HER2-low breast cancer prognosis. These genes were utilized to construct a robust risk score model, which effectively stratified patients into high- and low-risk groups. Patients in the high-risk group exhibited significantly worse clinical outcomes, confirming the independent prognostic value of the risk score in multivariate analysis. Furthermore, pathway enrichment analysis revealed significant suppression of immune-related signaling pathways (e.g. PI3K-AKT) in high-risk patients, alongside distinct tumor microenvironment profiles characterized by differential immune cell infiltration, altered expression of immune checkpoints, and significantly different TIDE scores, suggesting potential implications for immunotherapy response stratification.

Conclusions: The prognostic model based on UPR related genes, COPS5, DKC1, NOP56, and EIF4G1, could predict the prognosis of HER2-low breast cancer patients.