RESUMO
Background: Mantle cell lymphoma (MCL) is an aggressive B-cell non-Hodgkin lymphoma (NHL). REGγ is important for tumor occurrence and development, but understanding of the specific role of REGγ in MCL is lacking. We aimed to identify REGγ effects on the proliferation and apoptosis of MCL cells and clarify the underlying mechanisms. Methods: JEKO-1 cells stably transfected with a doxycycline-inducible Tet-On system expressed high levels of REGγ. JEKO-1 cells stably expressing shRNA-REGγ to reduce REGγ levels were constructed. Cell proliferation, apoptosis, and p-NF-κB, NF-κB, IkB, REGγ, p-STAT3, STAT3, and PSMB5 levels in transfected cells and in transfected cells treated with Stattic, that is a nonpeptidic small molecule exhibited to selectively inhibit signal transducer and activator of transcription factor 3 through blocking the function of its SH2 domain, were analyzed using western blotting. Results: The proliferation of JEKO-1 cells was inhibited, and apoptosis was enhanced by increased expression of REGγ (P<0.01). REGγ inhibited MCL cell proliferation in a mouse tumor xenograft model by promoting apoptosis, increased the expression of the three IκB subunits and inhibited NF-κB signaling. Overexpressed REGγ inhibited STAT3 and downregulated PSMB5 expression in MCL cells. Stattic downregulated PSMB5 and nuclear factor-kappa B (NF-κB) expressions and upregulated IκBε expression in JEKO-1 cells. Conclusions: We found that REGγ regulated p-STAT3 expression by accelerating its half-life and downregulated the NF-κB signaling pathway to promote MCL cell apoptosis by negatively regulating STAT3-mediated PSMB5 expression and subsequently upregulating IκB expression.
RESUMO
OBJECTIVES: We sought to evaluate whether combining body mass index (BMI) and fasting blood glucose (FBG) can refine the predictive value of new-onset prediabetes/diabetes after acute pancreatitis (NODAP). METHODS: In this retrospective cohort study, we used Kaplan-Meier analysis to compare differences in the NODAP rate among 492 patients with different BMI or FBG levels, or with the combination of these 2 factors mentioned above. RESULTS: In all, 153 of 492 (31.1%) eligible patients finally developed NODAP. According to univariate and multivariate analyses, BMI (hazard ratio, 2.075; 95% confidence interval, 1.408-3.060; P < 0.001) and FBG (hazard ratio, 2.544; 95% confidence interval, 1.748-3.710; P < 0.001) were important predictors of the incidence of NODAP. Subsequently, we divided 492 eligible patients into 3 groups according to the median BMI and FBG values, and found that the NODAP rate in the high-risk group was significantly higher than that in the medium-risk group ( P = 0.018) or the low-risk group ( P < 0.001). CONCLUSIONS: Body mass index and FBG are independent predictors of NODAP. The combination of BMI and FBG can refine the prediction of NODAP and identify candidates for clinical prevention.
