MATERIALS AND METHODS: In the present study, the anticancer effects and the mechanisms of action of 17βH-neriifolin (cardiac glycoside) were evaluated by terminal deoxynucleotidyl transferase dUTP nick-end labeling (TUNEL) assay and a proteomic approach in treated and non-treated SKOV-3 ovarian cancer cells.
RESULTS: 17βH-neriifolin was found to be active with IC50 values of 0.01 ± 0.001 in SKOV-3 ovarian cancer cell line, as evaluated by the sulforhodamine B (SRB) assay. RESULTS from TUNEL assay indicated that 17βH-neriifolin caused apoptosis in SKOV-3 cells in a dose-dependent manner. Based on differential analysis of treated and non-treated SKOV-3 two-dimensional electrophoresis (2-DE) profiles, four proteins, namely vimentin (VIM), pyruvate kinase, muscle (PKM), heterogeneous nuclear ribonucleoprotein A1 (HNRNPA1) and transgelin (TAGLN1) were identified to be involved in apoptosis. Other proteins including piggybac transposable element derived 5 (PGBD5), DENN/MADD domain containing 2D (DENND2D) and formin-like 1(FMNL) have also been identified to be associated in SKOV-3 cell death induced by 17βH-neriifolin.
CONCLUSION: These findings may provide new insights on the potential of 17βH-neriifolin's mechanism of action in killing ovarian cancer cells.
MATERIALS AND METHODS: The influence of co-culture of myofibroblasts and CRC cell lines is discussed using various in vitro assays including direct co-culture, transwell assays, Matrigel-based differentiation and cell invasion experiments.
RESULTS: The results from these in vitro assays clearly demonstrated various aspects of the crosstalk between myofibroblasts and CRC cell lines, which include cell growth, differentiation, migration and invasion.
CONCLUSION: The reported in vitro assays provide a basis for investigating the factors that control the myofibroblast-epithelial cell interactions in CRC in vivo.
PATIENTS AND METHODS: The institutional review board approved this prospective study. The brain MRI protocol, including sagittal T1-weighted, axial T2-weighted, coronal fluid-attenuated inversion recovery, and axial T1-weighted with contrast enhancement (T1WCE) sequences, was assessed in 26 patients divided into two groups: Medulloblastoma (n=22) and ependymoma (n=4). The quantified region of interest (ROI) values of tumors and their ratios to parenchyma were compared between the two groups. Multivariate logistic regression analysis was utilized to find significant factors influencing the differential diagnosis between the two groups. A generalized estimating equation (GEE) was used to create the predictive model for the discrimination of medulloblastoma from ependymoma.
RESULTS: Multivariate logistic regression analysis showed that the T2- and T1WCE-ROI values of tumors and the ratios of T1WCE-ROI values to parenchyma were the most significant factors influencing the diagnosis between these two groups. GEE produced the model: y=exn/(1+exn) with predictor xn=-8.773+0.012x1 - 0.032x2 - 13.228x3, where x1 was the T2-weighted signal intensity (SI) of tumor, x2 the T1WCE SI of tumor, and x3 the T1WCE SI ratio of tumor to parenchyma. The sensitivity, specificity, and area under the curve of the GEE model were 77.3%, 100%, and 92%, respectively.
CONCLUSION: The GEE predictive model can discriminate between medulloblastoma and ependymoma clinically. Further research should be performed to validate these findings.