Background/Aims: To investigate the downregulation of microRNA (miR)-10b in clear cell renal cell carcinoma (ccRCC) and its mechanistic involvement in tumourigenesis. Methods: The relative expression of miR-10b in ccRCC samples were determined by real-time PCR. Exogenous expression and knockdown of endogenous miR-10b were performed by transfection with indicated plasmids into 786-0 cells. Cell proliferation was evaluated using Cell Counting Kit-8 assay. Cell apoptosis was analyzed by Annexin V/propidium iodide staining. MAPK pathway activation was detected by western blotting with indicated antibodies. Results: We confirmed the downregulation of miR-10b in ccRCC tumour. The forced expression of miR-10b inhibited cell proliferation in 786-0 cells. Moreover, miR-10b stimulated apoptosis in 786-0 cells, which was abrogated by specific miR-10b inhibitor. We further elucidated that the apoptosis induction was mediated by the JNK pathway activation. We consolidated this observation by combinational treatment with JNK specific inhibitor, which was shown to completely impede miR-10b elicited apoptosis. Conclusion: We suggested a tumour suppressor function of miR-10b in tumourigenesis of ccRCC via proliferation suppression and apoptosis induction, and the latter was mediated by the JNK pathway activation.

Cross-modal hashing has received increasing research attentions due to its less storage and efficient retrieval. However, most existing cross-modal hashing methods focus only on exploring multi-modal information, while underestimate the significance of local and Euclidean structure information on the hashing learning procedure. In this paper, we propose a supervised discrete-based cross-modal hashing method, named Scalable Discriminative Discrete Hashing (SDDH), for cross-modal retrieval, where 1) the discrete hash codes are directly obtained by multi-modal features and semantic labels so that the quantization errors are dramatically reduced, and 2) the discrete hash codes simultaneously preserve the heterogeneous similarity and manifold information in the original space by employing matrix factoring with orthogonal and balanced constraints. Moreover, an efficient optimization is introduced to tackle the discrete solution, which makes the SDDH scalable to large-scale cross-modal retrieval. Empirical results on three widely-used benchmark databases clearly demonstrate the effectiveness and efficiency of the proposed method in comparison with state-of-the-arts.

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