Recent studies suggested that cancer stem-like cells contribute to tumor vasculogenesis by differentiating into endothelial cells. However, such process is governed by still undefined mechanism. At varying differentiation levels, three representative colon cancer cells were cultured in endothelial-inducing conditioned medium: human colon cancer cells HCT116 (HCT116) (poorly differentiated), SW480 (moderately differentiated), and HT29 (well differentiated). We tested for expression of endothelial markers (cluster of differentiation (CD) 31, CD34, and vascular endothelial (VE)-cadherin and their ability to form tube-like structures in 3D culture. We also observed VEGF secretion and expressions of endothelial markers and VEGFRs in HCT116 cells under hypoxia to simulate physiological conditions. In in vitro and in xenotransplantation experiments, VE growth factor receptor 2 (VEGFR2) antagonist SKLB1002 was used to test effect of VEGFR2 in endothelial differentiation of HCT116 cells. Expression levels of VEGFR2 and VE-cadherin were assessed by immunohistochemistry of human colon cancer tissues to evaluate clinicopathological significance of VEGFR2. After culturing in endothelial-inducing conditioned medium, poorly differentiated HCT116 cells expressed endothelial markers and formed tube-like structure in vitro. HCT116 cells secreted more endogenous VEGF and expressed higher VEGFR2 under hypoxia. SKLB1002 impaired endothelial differentiation in vitro and xenotransplantation experiments, suggesting a VEGFR2-dependent mechanism. Increased expression of VEGFR2 correlated with differentiation, metastasis/recurrence, and poor prognosis in 203 human colon cancer samples. Positive correlation was observed between VEGFR2 and VE-cadherin expression. VEGFR2 regulates endothelial differentiation of colon cancer cell and may be potential platform for anti-angiogenesis cancer therapy.

Objective: This study aimed to evaluate the performance of the deep convolutional neural network (DCNN) to discriminate between benign, borderline, and malignant serous ovarian tumors (SOTs) on ultrasound(US) images.Material and Methods: This retrospective study included 279 pathology-confirmed SOTs US images from 265 patients from March 2013 to December 2016. Two- and three-class classification task based on US images were proposed to classify benign, borderline, and malignant SOTs using a DCNN. The 2-class classification task was divided into two subtasks: benign vs. borderline & malignant (task A), borderline vs. malignant (task B). Five DCNN architectures, namely VGG16, GoogLeNet, ResNet34, MobileNet, and DenseNet, were trained and model performance before and after transfer learning was tested. Model performance was analyzed using accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (AUC).Results: The best overall performance was for the ResNet34 model, which also achieved the better performance after transfer learning. When classifying benign and non-benign tumors, the AUC was 0.96, the sensitivity was 0.91, and the specificity was 0.91. When predicting malignancy and borderline tumors, the AUC was 0.91, the sensitivity was 0.98, and the specificity was 0.74. The model had an overall accuracy of 0.75 for in directly classifying the three categories of benign, malignant and borderline SOTs, and a sensitivity of 0.89 for malignant, which was better than the overall diagnostic accuracy of 0.67 and sensitivity of 0.75 for malignant of the senior ultrasonographer.Conclusion: DCNN model analysis of US images can provide complementary clinical diagnostic information and is thus a promising technique for effective differentiation of benign, borderline, and malignant SOTs.

Our previous study provided evidence that non-canonical Wnt signaling is involved in regulating vasculogenic mimicry (VM) formation. However, the functions of canonical Wnt signaling in VM formation have not yet been explored. In this study, we found the presence of VM was related to colon cancer histological differentiation (p < 0.001), the clinical stage (p < 0.001), and presence of metastasis and recurrence (p < 0.001). VM-positive colon cancer samples showed increased Wnt3a expression (p < 0.001) and β-catenin nuclear expression (p < 0.001) compared with the VM-negative samples. In vitro, over-regulated Wnt3a expression in HT29 colon cancer cells promoted the capacity to form tube-like structures in the three-dimensional (3-D) culture together with increased expression of endothelial phenotype-associated proteins such as VEGFR2 and VE-cadherin. The mouse xenograft model showed that Wnt3a-overexpressing cells grew into larger tumor masses and formed more VM than the control cells. In addition, the Wnt/β-catenin signaling antagonist Dickkopf-1(Dkk1) can reverse the capacity to form tube-like structures and can decrease the expressions of VEGFR2 and VE-cadherin in Wnt3a-overexpressing cells. Taken together, our results suggest that Wnt/β-catenin signaling is involved in VM formation in colon cancer and might contribute to the development of more accurate treatment modalities aimed at VM.