Abstract. Expression of the long non-coding RNA taurine-upregulated gene 1 (TUG1) is associated with various aggressive tumors. The present study aimed to investigate the biological function of TUG1 in regulating apoptosis, proliferation, invasion and cell cycle distribution in human glioma U251 cells. Lentivirus-mediated TUG1-specific microRNA was transfected into U251 cells to abrogate the expression of TUG1. Flow cytometry analysis was used to examine the cell cycle distribution and apoptosis of U251 cells. Cellular proliferation was examined using Cell Counting Kit-8 (CCK-8) assays and invasion was examined by Transwell assays. The apoptotic rate of cells in the TUG1-knockdown group was significantly higher than in the negative control (NC) group (11.58 vs. 9.14%, P<0.01). CCK-8 assay data demonstrated that the proliferative ability of cells within the TUG1-knockdown group was lower compared with that of the NC group. A Transwell invasion assay was performed, which revealed that the number of invaded cells from the TUG1-knockdown group was the less compared with that of the NC group. In addition, the G 0 /G 1 phase population was significantly increased within the treated group (44.85 vs. 38.45%, P<0.01), as measured by flow cytometry. The present study demonstrated that the downregulation of TUG1 may inhibit proliferation and invasion, and promote glioma U251 cell apoptosis. In addition, knockdown of TUG1 may have an effect on cell cycle arrest.The data presented in the current study indicated that TUG1 may be a novel therapeutic target for glioma.
Abstract. Mutations in the aryl hydrocarbon receptor-interacting protein (AIP) gene have previously been associated with a predisposition to pituitary adenomas. However, to the best of our knowledge, mutations in AIP that relate specifically to sporadic non-functioning pituitary adenomas (NFPAs) have yet to be reported. Therefore, the present study aimed to identify single nucleotide polymorphisms (SNPs) in the AIP gene that may be associated with NFPAs. Peripheral blood samples and the entire coding sequence of the AIP gene from 56 patients with NFPAs and 56 controls were analyzed in triplicate. Of the 56 patients with NFPAs, 9 patients (16.1%) were identified as harboring five different SNPs, although no germline mutations in the AIP gene were detected in any of the patients. Three different SNPs (7051C>T, 8012G>C and 8020G>C) were identified in exons 4 and 6 in 3 different patients (each in 1 patient). Two different SNPs (7318C>A and 7886A>G) were identified in exons 5 and 6, respectively, in 6 different patients (each in 3 patients). No SNPs or germline mutations in the AIP gene were identified in the controls. The results of the present study suggested that mutations in the AIP gene might not have an important role in the tumorigenesis of NFPAs. However, further studies are required in order to investigate potential molecular and genetic mechanisms that may underlie the involvement of AIP in NFPA.
In today's cyber world, worms pose a great threat to the global network infrastructure. In this paper, we propose a worm detection system based on deep learning. It includes two main modules: one worm detection module based on a convolutional neural network (CNN) and one automatic worm signature generation module based on a deep neural network (DNN). In the CNN-based worm detection module, we propose three kinds of data preprocessing methods: frequency processing, frequency weighted processing, and difference processing, and use CNN to train the model for worm detection. In the DNN-based worm signature generation module, there are two phrase:DNN is firstly utilized for training the model with worm payloads and their corresponding signatures as input in the training phrase. After worm payloads are fed into the trained DNN model in the test phrase, worm signatures are generated by our proposed Signature Beam Search algorithm. In the experiment, we firstly analyzed the impact of different data preprocessing methods and the number of convolution-pooling layers in the CNN model on the worm detection performance. Then we analyzed the effects of different signatures in the DNN algorithm on the automatic generation of worm signatures. Experiments show that the generated signatures have a good detection performance.
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