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      METTL3-mediated m 6A modification of ZBTB4 mRNA is involved in the smoking-induced EMT in cancer of the lung

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          Abstract

          N6-methyladenosine (m 6A) is an epigenetic modification associated with various tumors, but its role in tumorigenesis remains unexplored. Here, as confirmed by methylated RNA immunoprecipitation sequencing (meRIP-seq) and RNA sequencing (RNA-seq) analyses, exposure of human bronchial epithelial (HBE) cells to cigarette smoke extract (CSE) caused an m 6A modification in the 3′ UTR of ZBTB4, a transcriptional repressor. For these cells, CSE also elevated methyltransferase-like 3 (METTL3) levels, which increased the m 6A modification of ZBTB4. RIP-qPCR illustrated that ZBTB4 was the intent gene of YTHDF2 and that levels of ZBTB4 were decreased in an YTHDF2-dependent mechanism. The lower levels of ZBTB4 were associated with upregulation of EZH2, which enhanced H3K27me3 combining with E-cadherin promoter, causing lower E-cadherin levels and induction of the epithelial-mesenchymal transition (EMT). Further, in the lungs of mice, downregulation of METTL3 alleviated the cigarette smoke (CS)-induced EMT. Further, the expression of METTL3 was high in the lung tissues of smokers and inversely correlated with ZBTB4. Overall, our results show that the METTL3-mediated m 6A modification of ZBTB4 via EZH2 is involved in the CS-induced EMT and in lung cancer. These results indicate that m 6A modifications are a potential therapeutic target of lung damage induced by CS.

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          Abstract

          N6-methyladenosine (m 6A) is involved in various tumors, but its role in tumorigenesis remains unexplored. Here, the authors reveal that RNA m 6A modifications are important players during cigarette smoke (CS)-caused lung cancer, which indicates that m 6A modifications are a potential therapeutic target of lung damage induced by CS.

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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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              STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

              Abstract Proteins and their functional interactions form the backbone of the cellular machinery. Their connectivity network needs to be considered for the full understanding of biological phenomena, but the available information on protein–protein associations is incomplete and exhibits varying levels of annotation granularity and reliability. The STRING database aims to collect, score and integrate all publicly available sources of protein–protein interaction information, and to complement these with computational predictions. Its goal is to achieve a comprehensive and objective global network, including direct (physical) as well as indirect (functional) interactions. The latest version of STRING (11.0) more than doubles the number of organisms it covers, to 5090. The most important new feature is an option to upload entire, genome-wide datasets as input, allowing users to visualize subsets as interaction networks and to perform gene-set enrichment analysis on the entire input. For the enrichment analysis, STRING implements well-known classification systems such as Gene Ontology and KEGG, but also offers additional, new classification systems based on high-throughput text-mining as well as on a hierarchical clustering of the association network itself. The STRING resource is available online at https://string-db.org/.
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                Author and article information

                Contributors
                Journal
                Mol Ther Nucleic Acids
                Mol Ther Nucleic Acids
                Molecular Therapy. Nucleic Acids
                American Society of Gene & Cell Therapy
                2162-2531
                10 December 2020
                05 March 2021
                10 December 2020
                : 23
                : 487-500
                Affiliations
                [1 ]Center for Global Health, The Key Laboratory of Modern Toxicology, Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, People’s Republic of China
                [2 ]China International Cooperation Center for Environment and Human Health, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Jiangsu Collaborative Innovation Center for Cancer Personalized Medicine, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, People’s Republic of China
                [3 ]Department of Respiratory and Critical Care Medicine, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi 214023, Jiangsu, People’s Republic of China
                Author notes
                []Corresponding author: Qizhan Liu, Center for Global Health, The Key Laboratory of Modern Toxicology, Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing 211166, Jiangsu, People’s Republic of China. drqzliu@ 123456hotmail.com
                [∗∗ ]Corresponding author: Tao Bian, Department of Respiratory and Critical Care Medicine, Wuxi People’s Hospital Affiliated to Nanjing Medical University, Wuxi 214023, Jiangsu, People’s Republic of China. biantaophd@ 123456126.com
                [4]

                These authors contributed equally

                Article
                S2162-2531(20)30382-6
                10.1016/j.omtn.2020.12.001
                7806951
                33510938
                eb6386ee-0b0b-4ca5-ac44-6cdc1e8df1a1
                © 2020 The Author(s)

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 14 September 2020
                : 6 December 2020
                Categories
                Original Article

                Molecular medicine
                n6-methyladenosine,cigarette smoke,epithelial-mesenchymal transition,lung cancer

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