Purpose Curcumin has been shown to have potent anti-cancer activities like inhibition of cell proliferation, induction of apoptosis, and suppression of angiogenesis. Transforming growth factor-β (TGF-β) signaling plays a complex role in tumor suppression and promotion depending on the tumor type and stage. However, the effect of curcumin on TGF-β signaling in cancer cells and the role of TGF-β signaling in curcumin-induced anticancer activities have not been determined. Here, we investigate the role of curcumin on TGF-β signaling, and whether TGF-β signaling is involved in the antitumor activities of curcumin. Methods Human non-small cell lung cancer (NSCLC) cell lines, ACC-LC-176 (without TGF-β signaling), H358, and A549 (with TGF-β signaling) were treated with curcumin to determine cell growth, apoptosis, and tumorigenicity. Antitumor activities of curcumin were determined using these cell lines and an in vivo mouse model. We also tested the effect of curcumin on TGF-β/Smad signaling by western blotting and by luciferase assays. Results Curcumin inhibited cell growth and induced apoptosis of all three NSCLC cell lines in vitro and in vivo. It significantly reduced subcutaneous tumor growth by these three cell lines irrespective of TGF-β signaling status. Curcumin inhibited TGF-β-induced Smad2/3 phosphorylation and transcription in H358 and A549 cells, but not in ACC-LC-176 cells. Conclusions Curcumin reduces tumorigenicity of human lung cancer cells in vitro and in vivo by inhibiting cell proliferation and promoting apoptosis. These results suggest that TGF-β signaling is not directly involved in curcumin-mediated growth inhibition, induction of apoptosis, and inhibition of tumorigenicity.

In large scale scientific computing, proper planning and management of computational resources lead to higher system utilizations and increased scientific productivity. Scientists are increasingly leveraging the use of business process management techniques and workflow management tools to balance the needs of the scientific analyses with the availability of computational resources. However, the advancements in productivity from execution of workflows in a large scale computing environments are often thwarted by runtime resource failures. This paper presents our initial work toward autonomic model based fault analysis in workflow based environments.