Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0029463 (osteosarcoma)
 16,637 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Transforming growth factor beta (TGF beta) treatment of rat osteoblast-rich calvarial cells or of the clonal osteogenic sarcoma cells, UMR 106-01, resulted in dose-dependent inhibition of plasminogen activator (PA) activity, and increased production of 3.2 kb mRNA and protein for PA inhibitor -1 (PAI-1). Although tissue-type PA (tPA) protein was not measured, TGF beta did not influence production of mRNA for tPA. Production of 2.3 kb mRNA for urokinase-type PA (uPA) was also increased by TGF beta in a dose-dependent manner. The effects of TGF beta on synthesis of mRNA for PAI-1 and uPA were maintained when protein synthesis was inhibited, and were abolished by inhibition of RNA synthesis. Although uPA had not been detected previously as a product of rat osteoblasts, treatment of lysates of osteoblast-like cells with plasmin yielded a band of PA activity on reverse fibrin autography, corresponding to a low Mr form of uPA. Untreated conditioned media from normal osteoblasts or UMR 106-01 cells contained no significant TGF beta activity, but activity could be detected in acidified medium. Treatment of conditioned media with plasmin resulted in activation of approximately 50% of the TGF beta detectable in acidified media. The results identify several effects of TGF beta on the PA-PA inhibitor system in osteoblasts. Net regulation of tPA activity through the stimulatory actions of several calciotropic hormones and the promotion of PAI-1 formation by TGF beta could determine the amount of osteoblast-derived TGF beta activated locally in bone. Stimulation of osteoblast production of mRNA for uPA could reflect effects on the synthesis of sc-uPA, a precursor for the active form of the enzyme.
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PMID:Transforming growth factor beta inhibits plasminogen activator (PA) activity and stimulates production of urokinase-type PA, PA inhibitor-1 mRNA, and protein in rat osteoblast-like cells. 183 80

A certain number of pediatric cancer patients still succumb to relapse following conventional treatment of their malignancies. One of the mechanisms of relapse is escape from immunity. Adoptive cellular immunotherapy with effector cells has the potential to overcome this escape. In adults, the CD3+ CD56+ cell, a cytokine-induced killer (CIK) cell, appears to be a promising effector cell type with the greatest cytotoxicity. This effector cell type may work in children as well. No similar studies with children have been published. We speculated that expanded CD3+ CD56+ cells obtained from pediatric cancer patients during remission would act similarly against various pediatric tumor cell lines; therefore, we undertook the present study to find support for our speculation. This study was undertaken to generate and expand CD3+ CD56+ CIK cells from normal peripheral blood mononuclear cells (PBL) obtained from 6 children with cancer (2 with acute lymphoblastic leukemia, 2 with large cell lymphoma, and 2 with osteosarcoma) in remission after intensive chemotherapy and to study the cytotoxic activities of these cells against chronic myeloid leukemia cell line K562 t(9;22), 4 pediatric tumor cell lines [infant acute lymphoblastic leukemia RS4 t(4;11), TEL/AML acute lymphoblastic leukemia REH t(12;21), alveolar rhabdomyosarcoma Rh-Cr t(2;13), and Ewing sarcoma EW-Le t(11;22)], and 2 pediatric glioblastoma multiforme cultured cell lines (G74 and G77). CIK cells were generated and expanded in culture medium to which interferon gamma, monoclonal antibody against CD3, and interleukin 2 were added at appropriate times. Cells were counted by flow cytometry. Net lactate dehydrogenase release from target cells incubated with CIK cells was used as an index of CIK cell cytotoxicity against various pediatric tumor cell lines. The results show that after 21 days in culture CD3+ CD56+ CIK cells derived from the 6 pediatric patients accounted for a median of 28.3% of the entire culture (range, 10.7%-36.4%). Before expansion no such cells were found in any of the 6 children. Median lytic activity rates of CIK cells were 45.5% to 64.5%, rates that contrasted drastically to the lytic activity rates of PBL, which were only 8% to 12%. The findings of the present study are encouraging. They provide information for developing adoptive immunotherapy for future clinical trials with pediatric cancer patients, particularly those patients with minimal residual disease after intensive chemotherapy or stem cell transplantation (especially nonmyeloablative transplantation procedures).
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PMID:Generation of CD3+ CD56+ cytokine-induced killer cells and their in vitro cytotoxicity against pediatric cancer cells. 1262 54

Many cancer cells depend on glutamine as they use the glutaminolysis pathway to generate building blocks and energy for anabolic purposes. As a result, glutamine transporters are essential for cancer growth and are potential targets for cancer chemotherapy with ASCT2 (SLC1A5) being investigated most intensively. Here we show that HeLa epithelial cervical cancer cells and 143B osteosarcoma cells express a set of glutamine transporters including SNAT1 (SLC38A1), SNAT2 (SLC38A2), SNAT4 (SLC38A4), LAT1 (SLC7A5), and ASCT2 (SLC1A5). Net glutamine uptake did not depend on ASCT2 but required expression of SNAT1 and SNAT2. Deletion of ASCT2 did not reduce cell growth but caused an amino acid starvation response and up-regulation of SNAT1 to replace ASCT2 functionally. Silencing of GCN2 in the ASCT2(-/-) background reduced cell growth, showing that a combined targeted approach would inhibit growth of glutamine-dependent cancer cells.
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PMID:Deletion of Amino Acid Transporter ASCT2 (SLC1A5) Reveals an Essential Role for Transporters SNAT1 (SLC38A1) and SNAT2 (SLC38A2) to Sustain Glutaminolysis in Cancer Cells. 2712 76

Automatic and accurate segmentation of osteosarcoma region in CT images can help doctor make a reasonable treatment plan, thus improving cure rate. In this paper, a multiple supervised residual network (MSRN) was proposed for osteosarcoma image segmentation. Three supervised side output modules were added to the residual network. The shallow side output module could extract image shape features, such as edge features and texture features. The deep side output module could extract semantic features. The side output module could compute the loss value between output probability map and ground truth and back-propagate the loss information. Then, the parameters of residual network could be modified by gradient descent method. This could guide the multi-scale feature learning of the network. The final segmentation results were obtained by fusing the results output by the three side output modules. A total of 1900 CT images from 15 osteosarcoma patients were used to train the network and a total of 405 CT images from another 8 osteosarcoma patients were used to test the network. Results indicated that MSRN enabled a dice similarity coefficient (DSC) of 89.22%, a sensitivity of 88.74% and a F1-measure of 0.9305, which were larger than those obtained by fully convolutional network (FCN) and U-net. Thus, MSRN for osteosarcoma segmentation could give more accurate results than FCN and U-Net.
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PMID:Multiple supervised residual network for osteosarcoma segmentation in CT images. 2936 40

We propose an image based cellular contractile force evaluation method using a machine learning technique. We use a special substrate that exhibits wrinkles when cells grab the substrate and contract, and the wrinkles can be used to visualize the force magnitude and direction. In order to extract wrinkles from the microscope images, we develop a new CNN (convolutional neural network) architecture SW-UNet (small-world U-Net), which is a CNN that reflects the concept of the small-world network. The SW-UNet shows better performance in wrinkle segmentation task compared to other methods: the error (Euclidean distance) of SW-UNet is 4.9 times smaller than the 2D-FFT (fast Fourier transform) based segmentation approach, and is 2.9 times smaller than U-Net. As a demonstration, here we compare the contractile force of U2OS (human osteosarcoma) cells and show that cells with a mutation in the KRAS oncogene show larger force compared to wild-type cells. Our new machine learning based algorithm provides us an efficient, automated and accurate method to evaluate the cell contractile force.
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PMID:Image based cellular contractile force evaluation with small-world network inspired CNN: SW-UNet. 3264 8