Extra-pancreatic metastasis is a difficult problem for surgical intervention in pancreatic cancer. CXC chemokine receptor 4 (CXCR4) was considered to have an important role in this process. We hypothesized it may contribute to the pancreatic cancer progression through influencing canonical Wnt pathway. The purpose of this study was to examine the functional role of CXCR4 in the progression of pancreatic cancers and explore the possible mechanism. To this end, the relation between CXCR4 and clinical characteristics was analysed. shRNA against CXCR4 was applied to disrupt the SDF-1/CXCR4 signal transduction pathways in pancreatic cancer cell lines. Our results showed that overall survival in the case of patients positive for CXCR4 expression was significantly lower than that in the case of patients negative for CXCR4 expression. Notably, in vitro studies we observed that the abrogation of CXCR4 could obviously influence the pancreatic cancer cell phenotype including cell proliferation, colony formation, cell invasion and also inhibit the TOPflash activity. In addition, Wnt target genes and mesenchymal markers such as Vimentin and Slug were also inhibited in CXCR4 knockdown cells. Collectively, these data reported here demonstrate CXCR4 could modulate the canonical Wnt pathway and perhaps be a promising therapeutic target for pancreatic cancer progression.

The rate of apoptosis in PC-2 cells was higher after treatment with butoxamine than propranolol, suggesting that propranolol induces apoptosis in PC-2 cells via the beta2-adrenoceptors principally. Our data could be useful for developing beta-adrenoceptor antagonists for inducing apoptosis in pancreatic cancer cells.

The expression of alpha1- and alpha2-adrenoreceptors is different in PC-2 and PC-3 cell lines, which might be indicative of their different functions. The alpha2-adrenoceptor antagonist, yohimbine, can inhibit the proliferation of both cell lines and induce their apoptosis, suggesting that yohimbine can be used as an anticancer drug for apoptosis of PC-2 and PC-3 cells.

No abstract

IntroductionSpatially varying coefficient models are a generalization of standard linear regression models allowing regression coefficients to change with spatial location. This type of model has been a useful tool in analyzing the spatial nonstationarity of a regression relationship based on estimated coefficient functions. Two competing methods for calibrating spatially varying coefficient models are the Bayesian approach (eg see