Hypoxia is an indispensable factor for cancer progression and is closely associated with the Warburg effect. Circular RNAs (CircRNA) have garnered considerable attention in molecular malignancy therapy as they are potentially important modulators. However, the roles of circRNAs and hypoxia in osteosarcoma (OS) progression have not yet been elucidated. This study reveals the hypoxia-sensitive circRNA, Hsa_circ_0000566, that plays a crucial role in OS progression and energy metabolism under hypoxic stress. Hsa_circ_0000566 is regulated by hypoxia-inducible factor-1α (HIF-1α) and directly binds to it as well as to the Von Hippel-Lindau (VHL) E3 ubiquitin ligase protein. Consequentially, binding between VHL and HIF-1α is impeded. Furthermore, Hsa_circ_0000566 contributes to OS progression by binding to HIF-1α (while competing with VHL) and by confers protection against HIF-1α against VHL-mediated ubiquitin degradation. These findings demonstrate the existence of a positive feedback loop formed by HIF-1α and Hsa_circ_0000566 and the key role they play in OS glycolysis. Taken together, these data indicate the significance of Hsa_circ_0000566 in the Warburg effect and suggest that Hsa_circ_0000566 could be a potential therapeutic target to combat OS progression.
Since abnormal expression of long noncoding RNAs (lncRNAs) is often closely related to various human diseases, identification of disease-associated lncRNAs is helpful for exploring the complex pathogenesis. Most of recent methods concentrate on exploiting multiple kinds of data related to lncRNAs and diseases for predicting candidate disease-related lncRNAs. These methods, however, failed to deeply integrate the topology information from the meta-paths that are composed of lncRNA, disease and microRNA (miRNA) nodes. We proposed a new method based on fully connected autoencoders and convolutional neural networks, called ACLDA, for inferring potential disease-related lncRNA candidates. A heterogeneous graph that consists of lncRNA, disease and miRNA nodes were firstly constructed to integrate similarities, associations and interactions among them. Fully connected autoencoder-based module was established to extract the low-dimensional features of lncRNA, disease and miRNA nodes in the heterogeneous graph. We designed the attention mechanisms at the node feature level and at the meta-path level to learn more informative features and meta-paths. A module based on convolutional neural networks was constructed to encode the local topologies of lncRNA and disease nodes from multiple meta-path perspectives. The comprehensive experimental results demonstrated ACLDA achieves superior performance than several state-of-the-art prediction methods. Case studies on breast, lung and colon cancers demonstrated that ACLDA is able to discover the potential disease-related lncRNAs.
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