BackgroundCell death mechanisms are integral to the pathogenesis of breast cancer (BC), with ATP-induced cell death (AICD) attracting increasing attention due to its distinctive specificity and potential therapeutic applications.MethodsThis study employed genomic methodologies to investigate the correlation between drug sensitivity and types of AICD in BC. Initially, data from TCGA were utilized to construct a prognostic model and classification system for AICD. Subsequently, a series of bioinformatics analyses assessed the prognostic and clinical significance of this model within the context of BC.ResultsAnalysis revealed a cohort of 18 genes associated with AICD, exhibiting prognostic relevance. Survival analyses indicated that overall survival rates were significantly lower in high-risk populations compared to their low-risk counterparts. Furthermore, prognostic indicators linked to AICD demonstrated high accuracy in predicting survival outcomes in BC. Immunological assessments indicated heightened expression of anti-tumor infiltrating immune cells and immune checkpoint molecules in low-risk populations, correlating with various anti-tumor immune functions. Ultimately, a comprehensive prognostic model related to AICD was developed through univariate analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis. As Adenosine triphosphate (ATP) concentration increased, the viability of BC cells exhibited a general decline at each time point. Notably, ATP diminished the mitochondrial membrane potential in BC cells while enhancing it in normal breast epithelial cells. Additionally, ATP inhibited the migration of BC cells and promoted their apoptosis. ATP also stimulated reactive oxygen species (ROS) production in MCF-10A cells, with implications for the immune response in BC cells. Compared to the control group, expression levels of CLIC6, SLC1A1, and CEMIP were significantly reduced in the ATP intervention group, whereas ANO6 expression was elevated. ANO6, CEMIP, and CLIC6 share genetic variants with BC, while SLC1A1 does not exhibit genetic causal variation with the disease.ConclusionA valuable prognostic model associated with AICD has been established, capable of accurately predicting BC prognosis. The induction of cell death by ATP appears to play a protective role in BC progression. These findings carry significant implications for the implementation of personalized and tailored treatment strategies for BC patients.
