TNFalpha has recently emerged as a regulator linking inflammation to cancer pathogenesis, but the detailed cellular and molecular mechanisms underlying this link remain to be elucidated. The tuberous sclerosis 1 (TSC1)/TSC2 tumor suppressor complex serves as a repressor of the mTOR pathway, and disruption of TSC1/TSC2 complex function may contribute to tumorigenesis. Here we show that IKKbeta, a major downstream kinase in the TNFalpha signaling pathway, physically interacts with and phosphorylates TSC1 at Ser487 and Ser511, resulting in suppression of TSC1. The IKKbeta-mediated TSC1 suppression activates the mTOR pathway, enhances angiogenesis, and results in tumor development. We further find that expression of activated IKKbeta is associated with TSC1 Ser511 phosphorylation and VEGF production in multiple tumor types and correlates with poor clinical outcome of breast cancer patients. Our findings identify a pathway that is critical for inflammation-mediated tumor angiogenesis and may provide a target for clinical intervention in human cancer.

Enhancer of Zeste homolog 2 (EZH2) is a methyltransferase that plays an important role in many biological processes through its ability to trimethylate lysine 27 in histone H3. Here, we show that Akt phosphorylates EZH2 at serine 21 and suppresses its methyltransferase activity by impeding EZH2 binding to histone H3, which results in a decrease of lysine 27 trimethylation and derepression of silenced genes. Our results imply that Akt regulates the methylation activity, through phosphorylation of EZH2, which may contribute to oncogenesis.

Objective: In this study, we exploited a VGG-16 deep convolutional neural network (DCNN) model to differentiate papillary thyroid carcinoma (PTC) from benign thyroid nodules using cytological images.Methods: A pathology-proven dataset was built from 279 cytological images of thyroid nodules. The images were cropped into fragmented images and divided into a training dataset and a test dataset. VGG-16 and Inception-v3 DCNNs were trained and tested to make differential diagnoses. The characteristics of tumor cell nucleus were quantified as contours, perimeter, area and mean of pixel intensity and compared using independent Student's t-tests.Results: In the test group, the accuracy rates of the VGG-16 model and Inception-v3 on fragmented images were 97.66% and 92.75%, respectively, and the accuracy rates of VGG-16 and Inception-v3 in patients were 95% and 87.5%, respectively. The contours, perimeter, area and mean of pixel intensity of PTC in fragmented images were more than the benign nodules, which were 61.01±17.10 vs 47.00±24.08, p=0.000, 134.99±21.42 vs 62.40±29.15, p=0.000, 1770.89±627.22 vs 1157.27±722.23, p=0.013, 165.84±26.33 vs 132.94±28.73, p=0.000), respectively.Conclusion: In summary, after training with a large dataset, the DCNN VGG-16 model showed great potential in facilitating PTC diagnosis from cytological images. The contours, perimeter, area and mean of pixel intensity of PTC in fragmented images were more than the benign nodules.