BackgroundThe microRNA miR-101 is downregulated in several cancers, including bladder cancer. However, miR-101’s role in the invasion, metastasis, and chemosensitivity of bladder cancer cells remains unclear. This study was conducted to determine miR-101’s role on the lymphangiogenic molecule vascular endothelial growth factor C (VEGF-C) and their effects upon bladder cancer cell migration, invasion, and chemosensitivity to cisplatin.MethodsTwo bladder cancer cell lines (T24 and 5637) and the tool cell line 293T were employed here. Bladder cancer cells were transfected with either a miR-101 overexpression vector or a scrambled-sequence lentivirus, both of which exhibited a high transfection efficiency. Non-transfection was used as a mock negative control. Wound healing and Transwell assays were performed to measure cell migration and invasiveness. A luciferase reporter assay was performed to validate miR-101’s interaction with VEGF-C’s 3′ untranslated region followed by RT-PCR and Western blot confirmation. An MTS assay was used to evaluate the cisplatin sensitivity of the cell lines.ResultsmiR-101 overexpression significantly inhibited the migration and invasiveness while significantly enhancing cisplatin sensitivity. miR-101 negatively regulated VEGF-C protein expression, and VEGF-C overexpression rescued the effects of miR-101 overexpression, indicating that miR-101 negatively regulates VEGF-C protein expression post-transcriptionally. miR-101 and VEGF-C interference independently enhanced cisplatin cytotoxicity in bladder cancer cells.ConclusionsmiR-101 suppresses VEGF-C expression, inhibits cell migration and invasion, and increases cisplatin sensitivity in bladder cancer cells. This study provides new insight into miR-101’s role in bladder cancer and shows miR-101’s promise as a potential molecular target for bladder cancer.
Deep learning-based approaches have made considerable progress in image classification tasks, but most of the approaches lack interpretability, especially in revealing the decisive information causing the categorization of images. This paper seeks to answer the question of what clues encode the discriminative visual information between image categories and can help improve the classification performance. To this end, an attention-based clue extraction network (ACENet) is introduced to mine the decisive local visual information for image classification. ACENet constructs a clue-attention mechanism, that is globallocal attention, between the image and visual clue proposals extracted from it and then introduces a contrastive loss defined over the achieved discrete attention distribution to increase the discriminability of clue proposals. The loss encourages considerable attention to be devoted to discriminative clue proposals, that is those similar within the same category and dissimilar across categories. The experimental results for the Negative Web Image (NWI) dataset and the public ImageNet2012 dataset demonstrate that ACENet can extract true clues to improve the image classification performance and outperforms the baselines and the state-of-the-art methods.
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