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Background: In breast cancer oncogenesis, the precise role of cell apoptosis holds untapped potential for prognostic and therapeutic insights. Thus, it is important to develop a model predicated for breast cancer patients' prognosis and immunotherapy response based on apoptosis-related signature. Methods: Our approach involved leveraging a training dataset from The Cancer Genome Atlas (TCGA) to construct an apoptosis-related gene prognostic model. The model's validity was then tested across several cohorts, including METABRIC, Sun Yat-sen Memorial Hospital Sun Yat-sen University (SYSMH), and IMvigor210, to ensure its applicability and robustness across different patient demographics and treatment scenarios. Furthermore, we utilized Quantitative Polymerase Chain Reaction (qPCR) analysis to explore the expression patterns of these model genes in breast cancer cell lines compared to immortalized mammary epithelial cell lines, aiming to confirm their differential expression and underline their significance in the context of breast cancer. Results: Through the development and validation of our prognostic model based on seven apoptosis-related genes, we have demonstrated its substantial predictive power for the survival outcomes of breast cancer patients. The model effectively stratified patients into high and low-risk categories, with high-risk patients showing significantly poorer overall survival in the training cohort and across all validation cohorts. Importantly, qPCR analysis confirmed that the genes constituting our model indeed exhibit differential expression in breast cancer cell lines when contrasted with immortalized mammary epithelial cell lines. Conclusion: Our study establishes a groundbreaking prognostic model using apoptosis-related genes to enhance the precision of breast cancer prognosis and treatment, particularly in predicting immunotherapy response.
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BACKGROUND: To create effective medicines, researchers must first identify the common or unique genes that drive oncogenic processes in human cancers. Serine protease 27 (PRSS27) has been recently defined as a possible driver gene in esophageal squamous cell carcinoma. However, no thorough pan-cancer study has been performed to date, including breast cancer. METHODS: Using the TCGA (The Cancer Genome Atlas), the GEO (Gene Expression Omnibus) dataset, and multiple bioinformatic tools, we investigated the function of PRSS27 in 33 tumor types. In addition, prognosis analysis of PRSS27 in breast cancer was carried out, as well as in vitro experiments to verify its role as an oncogene. We first explored the expression of PRSS27 in over 10 tumors and then we looked into PRSS27 genomic mutations. RESULTS: We discovered that PRSS27 has prognostic significance in breast cancer and other cancers' survival, and we developed a breast cancer prognostic prediction model by combining a defined set of clinical factors. Besides, we confirmed PRSS27 as an oncogene in breast cancer using some primary in vitro experiments. CONCLUSION: Our pan-cancer survey has comprehensively reviewed the oncogenic function of PRSS27 in various human malignancies, suggesting that it may be a promising prognostic biomarker and tumor therapeutic target in breast cancer.