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      Tenascin-C promotes epithelial-to-mesenchymal transition and the mTOR signaling pathway in nasopharyngeal carcinoma

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          Abstract

          Tenascin-C (TNC) is a large extracellular matrix glycoprotein that promotes cell adhesion and tissue remodeling, and is involved in the transduction of cellular signaling pathways. The present study aimed to investigate the role of TNC and determine its effect in nasopharyngeal carcinoma (NPC). TNC gene transcription and expression were analyzed using the NPC dataset and immunohistochemistry analysis of NPC tissues. Weighted gene co-expression network and gene enrichment analyses were performed to determine the potential molecular mechanisms underlying the effects of TNC in NPC. TNC expression was suppressed in NPC cells, and the effects were determined both in vitro and in vivo. The results demonstrated that TNC gene transcription and expression were high in NPC tissues compared with normal tissues. Notably, TNC knockdown inhibited NPC cell proliferation, migration and invasion. In addition, TNC knockdown inhibited tumor growth in mice. In vitro, TNC knockdown inhibited epithelial-to-mesenchymal transition (EMT) and decreased activity of the PI3K/AKT/mTOR signaling pathway in NPC cells. Taken together, these results suggest that TNC promotes cell proliferation, EMT and activity of the PI3K/AKT/mTOR signaling pathway in NPC cells, and thus functions as an oncogene.

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          Global Cancer Statistics 2018: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries

          This article provides a status report on the global burden of cancer worldwide using the GLOBOCAN 2018 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer, with a focus on geographic variability across 20 world regions. There will be an estimated 18.1 million new cancer cases (17.0 million excluding nonmelanoma skin cancer) and 9.6 million cancer deaths (9.5 million excluding nonmelanoma skin cancer) in 2018. In both sexes combined, lung cancer is the most commonly diagnosed cancer (11.6% of the total cases) and the leading cause of cancer death (18.4% of the total cancer deaths), closely followed by female breast cancer (11.6%), prostate cancer (7.1%), and colorectal cancer (6.1%) for incidence and colorectal cancer (9.2%), stomach cancer (8.2%), and liver cancer (8.2%) for mortality. Lung cancer is the most frequent cancer and the leading cause of cancer death among males, followed by prostate and colorectal cancer (for incidence) and liver and stomach cancer (for mortality). Among females, breast cancer is the most commonly diagnosed cancer and the leading cause of cancer death, followed by colorectal and lung cancer (for incidence), and vice versa (for mortality); cervical cancer ranks fourth for both incidence and mortality. The most frequently diagnosed cancer and the leading cause of cancer death, however, substantially vary across countries and within each country depending on the degree of economic development and associated social and life style factors. It is noteworthy that high-quality cancer registry data, the basis for planning and implementing evidence-based cancer control programs, are not available in most low- and middle-income countries. The Global Initiative for Cancer Registry Development is an international partnership that supports better estimation, as well as the collection and use of local data, to prioritize and evaluate national cancer control efforts. CA: A Cancer Journal for Clinicians 2018;0:1-31. © 2018 American Cancer Society.
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            Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

            The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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              WGCNA: an R package for weighted correlation network analysis

              Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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                Author and article information

                Journal
                Oncol Lett
                Oncol Lett
                OL
                Oncology Letters
                D.A. Spandidos
                1792-1074
                1792-1082
                July 2021
                29 May 2021
                29 May 2021
                : 22
                : 1
                : 570
                Affiliations
                Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, Wuhan, Hubei 430060, P.R. China
                Author notes
                Correspondence to: Professor Zezhang Tao, Department of Otolaryngology-Head and Neck Surgery, Renmin Hospital of Wuhan University, 238 Jie-Fang Road, Wuhan, Hubei 430060, P.R. China, E-mail: taozezhang@ 123456126.com
                Article
                OL-0-0-12831
                10.3892/ol.2021.12831
                8185706
                34113398
                031b5dd1-3435-4f0d-8a7e-7514f35d2a3f
                Copyright: © Cheng et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

                History
                : 05 December 2020
                : 22 April 2021
                Funding
                Funded by: National Natural Science Foundation of China, open-funder-registry 10.13039/501100001809;
                Award ID: 81372880
                Funded by: Guidance fund of the Renmin Hospital of Wuhan University
                Award ID: RMYD2018Z12
                The present study was supported by the National Natural Science Foundation of China (grant nos. 81372880) and the Guidance fund of the Renmin Hospital of Wuhan University (grant no. RMYD2018Z12).
                Categories
                Articles

                Oncology & Radiotherapy
                tenascin-c,weighted gene co-expression network analysis,epithelial-to-mesenchymal transition,nasopharyngeal carcinoma,gene set enrichment analysis

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