Role of SERPINA1 in the tumor immune microenvironment of breast cancer and construction of a prognostic model
Year: 2026
Type: article
Abstract: BACKGROUND: Breast cancer is a common malignancy where the immune system plays a key role in disease progression and treatment response. SERPINA1 is an immune-related gene, but its function in breast cancer remains unclear. This study explored the expression, immune role, and prognostic value of SERPINA1 in breast cancer. METHODS: We analyzed data from TCGA and other public databases to evaluate SERPINA1 expression and performed Western blot and qPCR experiments for validation. Various bioinformatics tools were used to assess immune-related functions, identify differentially expressed genes, conduct pathway enrichment analyses, and construct a prognostic risk model. External datasets were used for validation, and immune activity and drug sensitivity were compared between different risk groups. RESULTS: SERPINA1 was significantly overexpressed in breast cancer tissues compared to normal tissues, and high expression was associated with better overall survival. High SERPINA1 expression correlated with enhanced activation of immune-related pathways, such as T and B-cell signaling. Patients with high SERPINA1 levels showed higher immune and stromal scores, increased infiltration of CD8⁺ T cells, macrophages, and dendritic cells, and elevated expression of immune checkpoint molecules. A prognostic model based on SERPINA1-associated immune genes effectively stratified patient survival risk and was validated in multiple external datasets. Patients in the low-risk group had stronger immune activity and were more sensitive to various chemotherapy and targeted drugs, suggesting the model’s potential in guiding personalized treatment. CONCLUSION: SERPINA1 is closely linked to favorable prognosis and an active immune microenvironment in breast cancer. The SERPINA1-based prognostic model may be useful for survival prediction and personalized immunotherapy strategies.
Source: Discover Oncology
Authors: Xiaolin Xia, Jiaqi Liu, Jine Liu, Shuai Chen, Zhou Chen
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,690
OpenAlex ID: W7128026316