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Metastasis is the most significant process affecting the clinical management of cancer patients and occurs in multiple sequential steps. However, the molecular pathways underlying each step still remain obscure. Recent research has shown that there is a microRNA (miRNA) network that functions as a regulator of tumor metastasis. In this paper, we review the role of miRNAs in tumor metastasis, including control of epithelial-mesenchymal transition, regulation of metastasis-associated genes and epigenetic alterations. More information on miRNAs will promote a better understanding of the molecular mechanism of metastasis.

The Src homology 2 domain-containing protein tyrosine phosphatase 2 (Shp2) is generally considered to be an oncogene owing to its ability in enhancing the malignancy of multiple types of tumor cells; however, its role in modulating tumor immunity remains largely elusive. Here, we reported that myeloid-restricted ablation of Shp2 suppressed melanoma growth. Mechanistically, loss of Shp2 potentiates macrophage production of CXCL9 in response to IFN-γ and tumor cell-derived cytokines, thereby facilitating the tumor infiltration of IFN-γ-producing T cells that could in turn support CXCL9 production within tumor microenvironment. Collectively, our findings highlight a causative role of myeloid Shp2 in dampening T cell-mediated antitumor immunity by restraining the macrophage/CXCL9-T cell/IFN-γ feedback loop. Thus, targeting macrophage Shp2 may help to create a Th1-dominant tumor immune microenvironment.

MicroRNAs (miRNAs) have been proposed as critical regulatory molecules in the epithelial-mesenchymal transition (EMT) program. However, the roles of mature miRNA biogenesis during EMT process needs to be defined. Here we determined that increased expression of XRN2 induced EMT and promoted metastasis in vitro and in vivo. Furthermore, we uncovered that XRN2 functions as pro-metastatic gene, which accelerates miR-10a maturation by binding pre-miR-10a in a DICER-independent manner. These findings suggest that XRN2 is a novel regulator of EMT that contributes to the metastatic processes in lung cancer through a novel miRNA regulatory mechanism.

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The conventional cost–tolerance model is constructed by linear or non-linear regression analysis based upon empirical data from all frequently used production processes. These approaches suffer relatively large model-fitting errors and also fail to consider the varying manufacturing environment due to the simplicity of the mathematical models used. In the present study, a novel, multi-parameter cost–tolerance model is developed based on a fuzzy neural network (FNN) that has tolerance and a cost influence coefficient as inputs and manufacturing cost as output. First, the cost influence coefficient is introduced based on analysis of the manufacturing environment. Then, a multi-input, FNN-based cost–tolerance model is constructed. Analysis of the model is conducted and comparison is made with existing models. Analytical results show that the proposed model yields better performance in controlling the average fitting error and is flexible to varying manufacturing environment. It can provide more reliable results and reduce the chances of error in the tolerance design.

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