Development of endosome-related gene signature for the prediction of prognosis and therapeutic response in breast cancer.

Endosomes play a pivotal role in cellular biology, orchestrating processes such as endocytosis, molecular trafficking, signal transduction, and recycling of cellular materials. This study aims to construct an endosome-related gene (ERG)-derived risk signature for breast cancer prognosis. Transcriptomic and clinical data were retrieved from The Cancer Genome Atlas and the University of California Santa Cruz databases to build and validate the model. A Lasso Cox regression model was employed for risk signature construction. The immune landscape was assessed using CIBERSORT and ESTIMATE algorithms, while drug sensitivity was evaluated via the pRRophetic algorithm. Gene set enrichment analysis and gene set variation analysis were applied to evaluate gene expression patterns. A nomogram was constructed and validated for predicting breast cancer outcomes. The expression of ERGs in breast cancer cells and tissues was further validated. Sixty-one ERGs associated with breast cancer prognosis were identified, with 23 selected for constructing the risk signature. This signature stratified breast cancer patients into high- and low-risk groups, where the low-risk group exhibited significantly better prognosis. Notably, younger patients tended to have lower risk scores compared to older ones. The low-risk group exhibited enhanced sensitivity to the majority of the drugs tested, accompanied by increased infiltration of T cells and M1 macrophages. Additionally, cell cycle pathways were suppressed in the low-risk group, whereas antigen binding functions were significantly activated. Ultimately, risk score and age were identified as independent prognostic factors for breast cancer, and these factors were incorporated into a nomogram that demonstrated excellent performance in prognosis assessment. Finally, external cohort validated the dysregulation of the risk score-associated ERGs in breast cancer cells and tissues. This study successfully established an ERG-derived breast cancer risk signature and nomogram, elucidating their potential value in prognosis prediction and evaluation of therapeutic response.