Reduced expression of E-cadherin was observed in renal cell carcinoma (RCC). However, its potential clinical value and correlation with WNT/β-catenin signaling in RCC progression was still unclear. Immunohistochemical staining was performed in RCC tissue microarray to examine the expression status and prognosis value of E-cadherin and β-catenin. The potential role of E-cadherin in β-catenin translocation was analyzed with immunobloting assays. A significant negative correlation was observed between E-cadherin and β-catenin expression in RCC tissues. E-cadherin inhibits β-catenin translocation from membrane to cytoplasm in RCC tissues, which was an important step for WNT/β-catenin signaling. Reduced E-cadherin expression was associated with poor prognosis. More importantly, E-cadherin-/β-catenin+ was an independent detrimental factor for survival estimation of RCC patients. Reduced E-cadherin expression in RCC promoted cancer progression via WNT/β-catenin signaling pathway activation. E-cadherin/β-catenin provides a valuable prognosis marker for RCC, which may be an effective target for RCC therapy.

Human forkhead box P3 (FOXP3)+ cluster of differentiation (CD)25+CD4+ regulatory T cells (Tregs) are a type of T cell that express CD4, CD25 and FOXP3, which are critical for maintaining immune homeostasis. The present study aimed to determine the mechanisms underlying Treg function. The GSE11292 dataset was downloaded from the Gene Expression Omnibus, which included data from Treg cells at 19 time points (0–360 min) with an equal interval of 20 min, and corresponding repeated samples. However, data for Treg cells at time point 120 min were missing. Using the Mfuzz package, the key genes were identified by clustering analysis. Subsequently, regulatory networks and protein-protein interaction (PPI) networks were constructed and merged into integrated networks using Cytoscape software. Using Database for Annotation, Visualization and Integrated Discover software, enrichment analyses were performed for the genes involved in the PPI networks. Cluster 1 (including 292 genes), cluster 2 (including 111 genes), cluster 3 (including 194 genes) and cluster 4 (including 103 genes) were obtained from the clustering analysis. GAPDH (degree, 40) in cluster 1, Janus kinase 2 (JAK2) (degree, 10) and signal transducer and activator of transcription 5A (STAT5A) (degree, 9) in cluster 3, and tumor necrosis factor (TNF) (degree, 26) and interleukin 2 (IL2) (degree, 22) in cluster 4 had higher degrees in the PPI networks. In addition, it was indicated that several genes may have a role in Treg function by targeting other genes [e.g. microRNA (miR)-146b-3p→TNF, miR-146b-5p→TNF, miR-142-5p→TNF and tripartite motif containing 28 (TRIM28)→GAPDH]. Enrichment analyses indicated that IL2 and TNF were enriched in the immune response and T cell receptor signaling pathway. In conclusion, GAPDH targeted by TRIM28, TNF targeted by miR-146b-3p, miR-146b-5p and miR-142-5p, in addition to JAK2, IL2, and STAT5A may serve important roles in Treg function.

In this paper, we study the communication performance of applying unmanned aerial vehicles (UAVs) combined with intelligent reflective surfaces (IRS) in a high speed railway (HSR) scenario. This study investigates the design and performance of (multiple-input-multiple-output) MIMO systems with UAV and IRS assistance technology in high-mobility scenarios. Direct links between base stations (BS) and trains are often obstructed in suburban environments, especially in mountainous areas. We mount the IRS on the UAVs so that it can assist in the communication between the trains and the BS. With the help of the UAV-IRS, straight-line links can be established effectively, which greatly improves communication for train passengers. This paper considers the employment of large-scale antenna arrays at both the BS and train ends. Train passengers communicate with UAVs via antennas assembled on the roof of the train as gateways, which in turn communicate with the BS. We consider two types of antenna layouts on the train: all antennas are located in the center of the train named Co-located antennas (CA) layout and uniformly distributed along the train called distributed antennas (DA) layout. We can obtain the analytical up-link capacity by averaging over all locations in a cell for the above two layouts by considering the radio frequency consumption. Overall, the CA layout is found to be a better option for trains when attempting to maximize cell mean value of capacity, and DA layout achieves a more uniformly distribution of capacity over the entire cell. Ultimately, the best solution will depend on the specific requirements and constraints of the selected deployment scenario.