Construction of a prognostic model of purine metabolism-related genes for breast cancer and validation of key gene PAICS

Background: Breast cancer (BC) is one of the most common cancers worldwide and the second leading cause of cancer-related deaths. Abnormalities in purine metabolism (PM) play an essential role in cancer development and progression; however, the integrative role of purine metabolism-related genes (PMGs) in BC is unclear. Methods: Based on publicly available BC transcriptomic data, the DESeq2 method was used to screen biomarkers and construct survival risk models. Additionally, correlations were examined between immune checkpoints, immune cell infiltration, drug sensitivity, and prognostic scores. The reliability of the selected biomarkers was verified by quantitative PCR, immunohistochemical staining, and protein detection in BC cell lines. The role of biomarkers associated with BC cell proliferation and migration was further validated by in vitro experiments. Results: The risk score model, constructed based on genes related to the purine metabolic pathway in BC, including NT5C1A, PAICS, PRDX1, ZDHHC15, and TH, demonstrated high reliability. The risk score was significantly and positively correlated with the expression of multiple immune cells and immune checkpoints. The results showed that the expression of the prognosis-related gene PAICS was considerably higher in BC tissues than in paracancerous tissues, and its deletion was able to inhibit the proliferation and migration ability of BC cells cultured in vitro. Conclusion: The PMGs prognostic model established in this study not only accurately predicts the survival outcomes of BC patients, but also reflects the characteristics of the tumor immune microenvironment and its therapeutic sensitivity. PAICS has been identified as a key molecule driving the progression of BC, with significant potential for targeted therapy. This model provides a new theoretical basis for individualized prognostic assessment and treatment planning of BC.