Accumulating evidence demonstrates that aberrant miRNAs contribute to gastric cancer (GC) development and progression. However, the roles of various miRNAs in GC remain to be determined. In the present study, we confirmed that a reduced miR-379 expression was present in GC tissues and cell lines. Our clinical analysis revealed that the downregulated miR-379 expression was significantly correlated with poor prognostic features including lymph node metastasis and advanced TNM stage. Moreover, we confirmed that miR-379 was a novel independent prognostic marker for predicting 5-year survival of GC patients. The ectopic overexpression of miR-379 inhibited cell migration, invasion and EMT progress, while downregulated miR-379 reversed the effect. In addition, miR-379 regulated the focal adhesion kinase (FAK) by directly binding to its 3'-UTR, resulting in suppression of AKT signaling. In clinical samples of GC, miR-379 inversely correlated with FAK, which was upregulated in GC. Alteration of FAK expression or activating AKT signaling at least partially abolished the migration, invasion and EMT progress effects of miR-379 on GC cells. In conclusion, our results indicated that miR-379 functioned as a tumor suppressor gene in regulating the EMT and metastasis of GC via targeting FAK/AKT signaling, and may represent a novel potential therapeutic target and prognostic marker for GC.
The differential privacy preservation model based on spatial dataset meshing has been widely concerned, but the distribution characteristics of the dataset and user's query granularity are often ignored or not fully considered in the partitioning of the dataset. Aiming at deficiencies in existing mesh-based algorithms, a standard deviation circle radius adaptive grid decomposition (SDCAG) algorithm is proposed. Firstly, the standard deviation circle radius is introduced to quantitatively represent the distribution characteristics of datasets in order to calculate privacy preservation requirements. Secondly, filtering and bucketing are used to reduce the noise error. Finally, the improved query precision is implemented based on the post-processing. Experiments on the NYC dataset, the Beijing dataset, and the Checkin dataset show that the SDCAG algorithm is superior to similar algorithms in terms of query performance.
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