BackgroundExosomes play an important role in cell-cell communication by transferring genetic materials such as long non-coding RNAs (lncRNAs) between cancer cells and tumor-associated macrophages (TAMs) in the tumor microenvironment (TME). Recent studies revealed that lncRNA ELFN1-AS1 could function as an oncogene in many human cancers. However, the role of extracellular lncRNA ELFN1-AS1 in cell-to-cell communication of osteosarcoma (OS) has not been fully investigated.MethodsFunctional studies, including CCK-8, EdU staining and transwell assay were performed to investigate the role of ELFN1-AS1 in the progression of OS. 143B via xenograft mouse model was established to assess the role of ELFN1-AS1 in vivo. In addition, transmission electron microscopy (TEM) and real-time quantitative PCR (RT-qPCR) assay were used to verify the existence of exosomal ELFN1-AS1.ResultsThe level of ELFN1-AS1 was markedly upregulated in patients with advanced OS and in OS cells. In addition, overexpression of ELFN1-AS1 significantly promoted the proliferation, migration and invasion of OS cells, while knockdown of ELFN1-AS1 exhibited the opposite effects. Meanwhile, ELFN1-AS1 could be transferred from OS cells to macrophages via exosomes. Exosomal ELFN1-AS1 from 143B cells was able to promote macrophage M2 polarization, and M2 macrophage in return facilitated OS progression. Mechanistically, overexpression of ELFN1-AS1 upregulated CREB1 level via sponging miR-138-5p and miR-1291 in macrophage via.ConclusionOS cell-derived exosomal ELFN1-AS1 was able to induce macrophage M2 polarization via sponging miR-138-5p and miR-1291, and M2 macrophage notably facilitated the progression of OS. These data suggested that ELFN1-AS1 might serve as a potential therapeutic target for osteosarcoma.

No abstract

Convergence speed and steady-state source separation performance are crucial for enable engineering applications of blind source separation methods. The modification of the loss function of the blind source separation algorithm and optimization of the algorithm to improve its performance from the perspective of neural networks (NNs) is a novel concept. In this paper, a blind source separation method, combining the maximum likelihood estimation criterion and an NN with a bias term, is proposed. The method adds L2 regularization terms for weights and biases to the loss function to improve the steady-state performance and designs a novel optimization algorithm with a dual acceleration strategy to improve the convergence speed of the algorithm. The dual acceleration strategy of the proposed optimization algorithm smooths and speeds up the originally steep, slow gradient descent in the parameter space. Compared with competing algorithms, this strategy improves the convergence speed of the algorithm by four times and the steady-state performance index by 96%. In addition, to verify the source separation performance of the algorithm more comprehensively, the simulation data with prior knowledge and the measured data without prior knowledge are used to verify the separation performance. Both simulation results and validation results based on measured data indicate that the new algorithm not only has better convergence and steady-state performance than conventional algorithms, but it is also more suitable for engineering applications.

Background Osteosarcoma is an aggressive malignant tumor which has attracted worldwide attention. MEX3A may be associated with tumors while has not yet seen its coverage on osteosarcoma. Herein, this study was to investigate the correlation between MEX3A and the progression of osteosarcoma. Methods Firstly, we determined that expression of MEX3A was significantly higher in osteosarcoma tissues than that in marginal bone by immunohistochemical staining. Additionally, MEX3A expression was downregulated by the RNAi‐mediated knockdown. The functions of MEX3A knockdown on proliferation, apoptosis, cell cycle, migration was assessed by MTT assay, flow cytometry, wound-healing assay and Transwell assay, respectively. Knockdown of MEX3A resulted in suppressing cell proliferation, increasing cell apoptosis, inducing the G2 phase cell cycle arrest, and attenuating cellular migration. Furthermore, mouse xenograft model confirmed inhibitory effects of MEX3A knockdown on osteosarcoma formation. Results The preliminary exploration on the molecular mechanism of MEX3A in osteosarcoma cells showed that the induction of apoptosis needs the participation of a series of apoptosis- associated factors, such as upregulation of Caspase 3, Caspase 8 and HSP60, downregulation of HSP27 and XIAP. Conclusions In summary, these findings predicated that therapy directed at decreasing MEX3A expression is a potential osteosarcoma treatment.