M2 polarization of tumor-associated macrophages is dependent on integrin β3 via peroxisome proliferator-activated receptor-γ up-regulation in breast cancer
Macrophages are particularly abundant and play an important role throughout the tumor progression process, namely, tumor-associated macrophages (TAM) in the tumor microenvironment. TAM can be polarized to disparate functional phenotypes, the M1 and M2 macrophages. M1-like type macrophages are defined as pro-inflammatory cells involved in killing cancer cells, while M2-like type cells can specially promote tumor growth and metastasis, tissue remodeling and immunosuppression. In this study, we first found that integrin β3 was highly expressed on the surface of TAM, both in vivo and in vitro, that displayed the M2-like characteristics. Under intervention of CYC or triptolide, the integrin β3 inhibitors, the M2 polarization of TAM could be inhibited. Moreover, in the cell model of M2 polarization, either blockade or knockout/knockdown of integrin β3 could also suppress macrophage M2 polarization, which suggested that the M2 polarization was dependent on integrin β3. Using knockdown of peroxisome proliferator-activated receptor-γ (PPARγ), an M2 regulator, we found that expression and activation of PPARγ participated in M2 polarization that was mediated by integrin β3. Finally, to verify the activity of integrin β3 inhibitors on TAM in vivo, 4T1 tumor-bearing mice were treated with CYC or triptolide; in response, the M1/M2 ratio of TAM was up-regulated, while the infiltration of total lymphocytes into tumor tissue was not altered. In general, our study found a connection between integrin β3 and macrophage polarization, which provides a strategy for facilitating M2 to M1 repolarization and reconstructing the tumor immune microenvironment.Keywords: M2 polarization; breast cancer; integrin β3; peroxisome proliferator-activated receptor-γ; tumor-associated macrophages.
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基因水平:PCR Array、RT-PCR、PCR、单细胞测序
蛋白水平:MSD、Luminex、CBA、Elispot、Antibody Array、ELISA、Sengenics
细胞水平:细胞染色、细胞分选、细胞培养、细胞功能
组织水平:空间多组学、多重荧光免疫组化、免疫组化、免疫荧光
数据分析:流式数据分析、组化数据分析、多因子数据分析
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