In the area of large-scale graph data representation and semi-supervised learning, deep graph-based convolutional neural networks have been widely applied. However, typical graph convolutional network (GCN) aggregates information of neighbor nodes based on binary neighborhood similarity (adjacency matrix). It treats all neighbor nodes of one node equally, which does not suppress the influence of dissimilar neighbor nodes. In this paper, we investigate GCN based on similarity matrix instead of adjacency matrix of graph nodes. Gaussian heat kernel similarity in Euclidean space is first adopted, which is named EGCN. Then biologically inspired manifold similarity is trained in reproducing kernel Hilbert space (RKHS), based on which a manifold GCN (named MGCN) is proposed for graph data representation and semi-supervised learning with four different kernel types. The proposed method is evaluated with extensive experiments on four benchmark document citation network datasets. The objective function of manifold similarity learning converges very quickly on different datasets using various kernel functions. Compared with state-of-the-art methods, our method is very competitive in terms of graph node recognition accuracy. In particular, the recognition rates of MGCN (Gaussian kernel) and MGCN (Polynomial Kernel) outperform that of typical GCN about 3.8% on Cora dataset, 3.5% on Citeseer dataset, 1.3% on Pubmed dataset and 4% on Cora_ML dataset, respectively. Although the proposed MGCN is relatively simple and easy to implement, it can discover local manifold structure by manifold similarity learning and suppress the influence of dissimilar neighbor nodes, which shows the effectiveness of the proposed MGCN.
Sorafenib provides survival benefits in patients with advanced renal cell carcinoma (RCC), but its use is hampered by acquired drug resistance. It is important to fully clarify the molecular mechanisms of sorafenib resistance, which can help to avoid, delay or reverse drug resistance. Extracellular vesicles (EVs) can mediate intercellular communication by delivering effector molecules between cells. Here, we studied whether EVs are involved in sorafenib resistance of RCC and its possible molecular mechanisms. Using differential centrifugation, EVs were isolated from established sorafenib-resistant RCC cells (786-0 and ACHN), and EVs derived from sorafenib-resistant cells were uptaken by sensitive parental RCC cells and thus promoted drug resistance. Elevated exogenous miR-31-5p within EVs effectively downregulated MutL homolog 1 (MLH1) expression and thus promoted sorafenib resistance in vitro. Mice experiments also confirmed that miR-31-5p could mediate drug sensitivity in vivo. In addition, low expression of MLH1 was observed in sorafenib-resistant RCC cells and upregulation of MLH1 expression restored the sensitivity of resistant cell lines to sorafenib. Finally, miR-31-5p level in circulating EVs of RCC patients with progressive disease (PD) during sorafenib therapy was higher when compared to that in the pretherapy status. In conclusion, EVs shuttled miR-31-5p can transfer resistance information from sorafenib-resistant cells to sensitive cells by directly targeting MLH1, and thus magnify the drug resistance information to the whole tumor. Furthermore, miR-31-5p and MLH1 could be promising predictive biomarkers and therapeutic targets to prevent sorafenib resistance. 相似文献
ABSTRACT Tryptophan (Trp) is not only a nutrient enhancer but also has systemic effects. Trp metabolites signaling through the well-known aryl hydrocarbon receptor (AhR) constitute the interface of microbiome-gut-brain axis. However, the pathway through which Trp metabolites affect central nervous system (CNS) function have not been fully elucidated. AhR participates in a broad variety of physiological and pathological processes that also highly relevant to intestinal homeostasis and CNS diseases. Via the AhR-dependent mechanism, Trp metabolites connect bidirectional signaling between the gut microbiome and the brain, mediated via immune, metabolic, and neural (vagal) signaling mechanisms, with downstream effects on behavior and CNS function. These findings shed light on the complex Trp regulation of microbiome-gut-brain axis and add another facet to our understanding that dietary Trp is expected to be a promising noninvasive approach for alleviating systemic diseases. 相似文献
Pancreatic cancer is a lethal disease characterized by early metastasis, local invasion, and resistance to conventional therapies. To understand its etiology and eventually make prevention of it possible and effective, appropriate carcinogenesis models will certainly help us understand the effects of environmental and genetic elements on pancreatic carcinogenesis. The development of new treatment strategies to control cancer metastasis is of immediate urgency. Fulfillment of this task relies on our knowledge of the cellular and molecular biology of pancreatic cancer metastasis and the availability of biologically and clinically relevant model systems. Many of the existing pancreatic cancer carcinogenesis and metastasis animal models are described in this review. The advantages and disadvantages of each model and their clinical implications are discussed, and special attention is focused on experimental therapeutic strategies targeting pancreatic cancer metastasis. 相似文献