Rabbit Anti-Human GSK3 alpha/beta (phospho Ser9, Ser21) monoclonal antibody, clone L.968.2

Here we explored the antitumor-activity of novel-formulated-form of curcumin (phytosomal-encapsulated-curcumin) or in combination with 5-FU in breast cancer. The antiproliferative activity was assessed in 2D and 3-dimensional cell-culture-model. The migratory-behaviors of the cells were determined by migration assay. The expression levels of CyclinD1,GSK3a/b, P-AMPK, MMP9, and E-cadherin were studied by qRT-PCR and/or Western blotting. The anti-inflammatory of nano-curcumin was assessed, while antioxidant activity was evaluated by malondialdehyde (MDA), superoxide dismutase (SOD), catalase (CAT), and total thiols (T-SH). To understand dynamic behavior of genes, we reconstructed a Boolean network, while the robustness of this model was evaluated by Hamming distance. phytosomal-curcumin suppressed cell-growth followed by tumor-shrinkage in 3D model through perturbation of AMP-activated protein kinase. Curcumin reduced the invasiveness of MCF-7 through perturbation of E-cadherin. Moreover, phytosomal-curcumin inhibited the tumor growth in xerograph model. Histological staining of tumor tissues revealed vascular disruption and RBC extravasation, necrosis, tumor stroma, and inflammation. Co-treatment of curcumin and 5-FU reduced the lipid-peroxidation and increased MDA/SOD level. Of note, curcumin reduced cyclinD-expression in breast cancer cell treated with thrombin, and activates AMPK in a time-dependent manner. Also suppression of AMPK abrogated inhibitory effect of phytosomal-curcumin on thrombin-induced cyclin D1 over-expression, suggesting that AMPK is essential for anti-proliferative effect of this agent in breast cancer. Our finding demonstrated that phytosomal-curcumin antagonizes cell growth and migration, induced by thrombin through AMP-Kinase in breast cancer, supporting further-investigations on the therapeutic potential of this novel anticancer agent in treatment of breast cancer.

The development of cancer evolves gene mutations according to the somatic mutation theory. The identification and prediction of the cancer-associated genes is one of the most important aims in cancer research. We apply four centrality metrics (degree, betweenness, closeness and PageRank) to prioritize and predict the candidate cancer-associated genes in the human signaling network. We find that the genes with higher centrality scores are more likely to be cancer-associated. Taking the top 47 genes for each centrality measure, we get 89 central genes. Among these 89 central genes, 58 genes are known to be cancer-associated, 4 genes encode non-protein and 27 genes are inferred genes. For the 27 inferred genes, by literature mining we find that 21 genes have been confirmed to be cancer-associated and the other 6 genes (CAMP, GSK3A, MTG1, GNGT1, ISGF3G and DYT10) are strong candidates for cancer research. These results show that the four centrality metrics are effective in predicting candidate cancer-associated genes for further experimental analysis.