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TrkB is a neurotrophic tyrosine kinase receptor (Trk). To investigate its role in anoikis suppression in human ovarian cancer, we used reverse transcription-polymerase chain reaction and real-time polymerase chain reaction, immunohistochemistry, and western blotting to compare the expression levels of TrkB and its ligand brain-derived neurotrophic factor between (i) 20 epithelial ovarian cancers, their multicellular spheroids in ascites or great omentum metastatic lesions, and eight borderline or benign ovarian tumors, as well as four normal ovarian tissues; and (ii) three ovarian cancer cell lines cultured under different conditions: monolayer adhesive culture (adhesive cells), anchorage-independent culture (cell spheroids), and trypsinized cell spheroids placed in monolayer adhesive dishes (cell spheroids replaced). TrkB and brain-derived neurotrophic factor were overexpressed in epithelial ovarian cancers, and full-length TrkB was more often overexpressed in high-grade carcinomas and multicellular spheroids in ascites. Expression of TrkB mRNA was higher in OVCAR-3 cell spheroids than in adhesive cells. The expression of full-length TrkB protein was highest in OVCAR-3 cell spheroids, but its precursor was expressed highly in OVCAR-3 cells under all three culture conditions. The relationship between TrkB overexpression and phosphatidylinositol 3'-kinase (PI3K)-AKT pathway activation in OVCAR-3 cells was studied by western blotting and RNA interference. The PI3K-AKT pathway was highly activated in anoikis-survived cells and was inhibited when TrkB was silenced by small interfering RNA. Finally, the chemosensitivity and invasiveness of OVCAR-3 cells were examined by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium, fluorescence-activated cell sorting, Matrigel invasion assay, and in vivo studies. Adhesive cells showed higher chemosensitivity and lower invasion ability than anoikis-survived cells. Our study suggests that TrkB might mediate anoikis suppression by activating the PI3K-AKT pathway in ovarian cancer cells.

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Paclitaxel is clinically used as a first-line chemotherapeutic regimen for several cancer types, including head and neck cancers. However, acquired drug resistance results in the failure of therapy, metastasis and relapse. The drug efflux mediated by ATP-binding cassette (ABC) transporters and the survival signals activated by forkhead box (FOX) molecules are critical in the development of paclitaxel drug resistance. Whether FOX molecules promote paclitaxel resistance through drug efflux remains unknown. In this study, we developed several types of paclitaxel-resistant (TR) nasopharyngeal carcinoma (NPC) cells. These TR NPC cells acquired cancer stem cell (CSC) phenotypes and underwent epithelial to mesenchymal transition (EMT), and developed multidrug resistance. TR cells exhibited stronger drug efflux than parental NPC cells, leading to the reduction of intracellular drug concentrations and drug insensitivity. After screening the gene expression of ABC transporters and FOX molecules, we found that FOXM1 and ABCC5 were consistently overexpressed in the TR NPC cells and in patient tumor tissues. Further studies demonstrated that FOXM1 regulated abcc5 gene transcription by binding to the FHK consensus motifs at the promoter. The depletion of FOXM1 or ABCC5 with siRNA significantly blocked drug efflux and increased the intracellular concentrations of paclitaxel, thereby promoting paclitaxel-induced cell death. Siomycin A, a FOXM1 inhibitor, significantly enhanced in vitro cell killing by paclitaxel in drug-resistant NPC cells. This study is the first to identify the roles of FOXM1 in drug efflux and paclitaxel resistance by regulating the gene transcription of abcc5, one of the ABC transporters. Small molecular inhibitors of FOXM1 or ABCC5 have the potential to overcome paclitaxel chemoresistance in NPC patients.

Our results suggested that pre-treatment with wogonin could attenuate the TLR4-mediated inflammatory response and maintain intestinal barrier function in LPS-induced Caco-2 cells, thus might be a potential therapy for treating IBD.

BackgroundThe poly ADP ribose polymerase (PARP) inhibitor olaparib has been approved for treating prostate cancer (PCa) with BRCA mutations, and veliparib, another PARP inhibitor, is being tested in clinical trials. However, veliparib only showed a moderate anticancer effect, and combination therapy is required for PCa patients. Histone deacetylase (HDAC) inhibitors have been tested to improve the anticancer efficacy of PARP inhibitors for PCa cells, but the exact mechanisms are still elusive.MethodsSeveral types of PCa cells and prostate epithelial cell line RWPE-1 were treated with veliparib or SAHA alone or in combination. Cell viability or clonogenicity was tested with violet crystal assay; cell apoptosis was detected with Annexin V-FITC/PI staining and flow cytometry, and the cleaved PARP was tested with western blot; DNA damage was evaluated by staining the cells with γH2AX antibody, and the DNA damage foci were observed with a fluorescent microscopy, and the level of γH2AX was tested with western blot; the protein levels of UHRF1 and BRCA1 were measured with western blot or cell immunofluorescent staining, and the interaction of UHRF1 and BRCA1 proteins was detected with co-immunoprecipitation when cells were treated with drugs. The antitumor effect of combinational therapy was validated in DU145 xenograft models.ResultsPCa cells showed different sensitivity to veliparib or SAHA. Co-administration of both drugs synergistically decreased cell viability and clonogenicity, and synergistically induced cell apoptosis and DNA damage, while had no detectable toxicity to normal prostate epithelial cells. Mechanistically, veliparib or SAHA alone reduced BRCA1 or UHRF1 protein levels, co-treatment with veliparib and SAHA synergistically reduced BRCA1 protein levels by targeting the UHRF1/BRCA1 protein complex, the depletion of UHRF1 resulted in the degradation of BRCA1 protein, while the elevation of UHRF1 impaired co-treatment-reduced BRCA1 protein levels. Co-administration of both drugs synergistically decreased the growth of xenografts.ConclusionsOur studies revealed that the synergistic lethality of HDAC and PARP inhibitors resulted from promoting DNA damage and inhibiting HR DNA damage repair pathways, in particular targeting the UHRF1/BRCA1 protein complex. The synergistic lethality of veliparib and SAHA shows great potential for future PCa clinical trials.Electronic supplementary materialThe online version of this article (10.1186/s13046-018-0810-7) contains supplementary material, which is available to authorized users.

Traffic problems have seriously affected people's life quality and urban development, and forecasting the short-term traffic congestion is of great importance to both individuals and governments. However, understanding and modeling the traffic conditions can be extremely difficult, and our observations from real traffic data reveal that (1) similar traffic congestion patterns exist in the neighboring time slots and on consecutive workdays; (2) the levels of traffic congestion have clear multiscale properties. To capture these characteristics, we propose a novel method named PCNN based on deep Convolutional Neural Network, modeling Periodic traffic data for short-term traffic congestion prediction. PCNN has two pivotal procedures: time series folding and multi-grained learning. It first temporally folds the time series and constructs a two-dimensional matrix as the network input, such that both the real-time traffic conditions and past traffic patterns are well considered; then with a series of convolutions over the input matrix, it is able to model the local temporal dependency and multiscale traffic patterns. In particular, the global trend of congestion can be addressed at the macroscale; whereas more details and variations of the congestion can be captured at the microscale. Experimental results on a realworld urban traffic dataset confirm that folding time series data into a two-dimensional matrix is effective and PCNN outperforms the baselines significantly for the task of short-term congestion prediction.