BPIFB1 promotes metastasis of hormone receptor-positive breast cancer via inducing macrophage M2-like polarization.

Metastasis is an important factor affecting the prognosis of hormone receptor-positive breast cancer (BC). However, the molecular basis for migration and invasion of tumor cells remains poorly understood. Here, we identify that bactericidal/permeability-increasing-fold-containing family B member 1 (BPIFB1), which plays an important role in innate immunity, is significantly elevated in breast cancer and associated with lymph node metastasis. High expression of BPIFB1 and its coding mRNA are significantly associated with poor prognosis of hormone receptor-positive BC. Using enrichment analysis and constructing immune infiltration evaluation, we predict the potential ability of BPIFB1 to promote macrophage M2 polarization. Finally, we demonstrate that BPIFB1 promotes the metastasis of hormone receptor-positive BC by stimulating the M2-like polarization of macrophages via the establishment of BC tumor cells/THP1 co-culture system, qPCR, Transwell assay, and animal experiments. To our knowledge, this is the first report on the role of BPIFB1 as a tumor promoter by activating the macrophage M2 polarization in hormone receptor-positive breast carcinoma. Together, these results provide novel insights into the mechanism of BPIFB1 in BC.

Construction of an immunogenic cell death-based risk score prognosis model in breast cancer.

Immunogenic cell death (ICD) is a form of regulated cell death that elicits immune response. Common inducers of ICD include cancer chemotherapy and radiation therapy. A better understanding of ICD might contribute to modify the current regimens of anti-cancer therapy, especially immunotherapy. This study aimed to identify ICD-related prognostic gene signatures in breast cancer (BC). An ICD-based gene prognostic signature was developed using Lasso-cox regression and Kaplan-Meier survival analysis based on datasets acquired from the Cancer Genome Atlas and Gene Expression Omnibus. A nomogram model was developed to predict the prognosis of BC patients. Gene Set Enrichment Analysis (GESA) and Gene Set Variation Analysis (GSVA) were used to explore the differentially expressed signaling pathways in high and low-risk groups. CIBERSORT and ESTIMATE algorithms were performed to investigate the difference of immune status in tumor microenvironment of different risk groups. Six genes (CALR, CLEC9A, BAX, TLR4, CXCR3, and PIK3CA) were selected for construction and validation of the prognosis model of BC based on public data. GSEA and GSVA analysis found that immune-related gene sets were enriched in low-risk group. Moreover, immune cell infiltration analysis showed that the immune features of the high-risk group were characterized by higher infiltration of tumor-associated macrophages and a lower proportion of CD8+ T cells, suggesting an immune evasive tumor microenvironment. We constructed and validated an ICD-based gene signature for predicting prognosis of breast cancer patients. Our model provides a tool with good discrimination and calibration abilities to predict the prognosis of BC, especially triple-negative breast cancer (TNBC).