Bone marrow-derived mesenchymal stem cells promote colorectal cancer progression via CCR5
Mesenchymal stem cells (MSCs) are recruited from BM to the stroma of developing tumors, where they serve as critical components of the tumor microenvironment by secreting growth factors, cytokines, and chemokines. The role of MSCs in colorectal cancer (CRC) progression was controversial. In this study, we found that C-C chemokine receptor type 5 (CCR5) ligands (i.e., C-C motif chemokine ligand 3 (CCL3), CCL4, and CCL5) were highly produced from MSCs using a chemokine array screening with conditioned media from the cultured human MSCs. A relatively strong CCR5 expression could be detected within the cytoplasm of several CRC cell lines. Regarding the effect of MSC, we found that the xenografts in which CCR5-overexpressing HCT116 cells were inoculated into immunocompromised mice were highly promoted in vivo by a mixture with MSCs. Notably, the CCR5 inhibitor, maraviroc, significantly abolished the MSC-induced tumor growth in vivo. In human clinical specimens (n = 89), 20 cases (29%) were high for CCR5, whereas 69 cases (71%) were low. Statistical analyses indicated that CCR5 expression in primary CRC was associated with CRC patients' prognosis. Especially, stage III/IV patients with CCR5-high CRCs exhibited a significantly poorer prognosis than those with CCR5-low CRCs. Furthermore, we investigated the effects of preoperative serum CCR5 ligands on patients' prognosis (n = 114), and found that CRC patients with high serum levels of CCL3 and CCL4 exhibited a poorer prognosis compared to those with low levels of CCL3 and CCL4, while there was no association between CCL5 and prognosis. These results suggest that the inhibition of MSC-CRC interaction by a CCR5 inhibitor could provide the possibility of a novel therapeutic strategy for CRC, and that serum levels of CCL3 and CCL4 could be predictive biomarkers for the prognosis of CRC patients.
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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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