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      OncoTargets and Therapy (submit here)

      This international, peer-reviewed Open Access journal by Dove Medical Press focuses on the pathological basis of cancers, potential targets for therapy and treatment protocols to improve the management of cancer patients. Publishing high-quality, original research on molecular aspects of cancer, including the molecular diagnosis, since 2008. Sign up for email alerts here. 50,877 Monthly downloads/views I 4.345 Impact Factor I 7.0 CiteScore I 0.81 Source Normalized Impact per Paper (SNIP) I 0.811 Scimago Journal & Country Rank (SJR)

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      Establishment of a Risk Signature Based on m6A RNA Methylation Regulators That Predicts Poor Prognosis in Renal Cell Carcinoma

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          Abstract

          Purpose

          N6-methyladenosine (m6A) modifications represent one of the most common methylation modifications, and they are mediated by m6A RNA methylation regulators. However, their functions in renal cell carcinoma (RCC) are not completely understood. The aim of this study was to investigate the effects of the regulators in RCC.

          Materials and Methods

          The expression levels of the 13 main m6A RNA methylation regulators in RCC were detected and consensus clustering was performed to explore their relationships with RCC. Thereafter, a risk signature based on the regulators was established. This risk model was fully verified by conducting prognostic analyses using two datasets (The Cancer Genome Atlas [TCGA] and Gene Expression Omnibus [GEO] datasets) and a ROC curve analysis.

          Results

          Of the 13 main m6A regulators, six were significantly upregulated and four were significantly downregulated in 893 RCC cases compared to 128 normal controls in the TCGA database. Consensus clustering based on the regulators identified two clusters of RCC cases, which were significantly associated with a pathological characteristic (T status). Thus, these results indicated that m6A RNA methylation regulators were associated with RCC. Thereafter, a risk model involving two of the regulators (METTL14 and WTAP) was established. The alterations in the mRNA and protein expression levels of these two regulators were further confirmed based on Human Protein Atlas data and real-time PCR in RCC and normal cell lines. The results indicated that the risk model may serve as an independent prognostic marker of overall survival, and it was also associated with clinicopathological characteristics (T status, M status, pathological stage, and gender) in RCC.

          Conclusion

          Collectively, the results of this study indicated that the risk model (based on two m6A RNA methylation regulators) may serve as an independent prognostic indicator of RCC, which may aid further investigation into m6A RNA modification in RCC.

          Most cited references42

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          limma powers differential expression analyses for RNA-sequencing and microarray studies

          limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
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            The meaning and use of the area under a receiver operating characteristic (ROC) curve.

            A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
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              Comprehensive analysis of mRNA methylation reveals enrichment in 3' UTRs and near stop codons.

              Methylation of the N(6) position of adenosine (m(6)A) is a posttranscriptional modification of RNA with poorly understood prevalence and physiological relevance. The recent discovery that FTO, an obesity risk gene, encodes an m(6)A demethylase implicates m(6)A as an important regulator of physiological processes. Here, we present a method for transcriptome-wide m(6)A localization, which combines m(6)A-specific methylated RNA immunoprecipitation with next-generation sequencing (MeRIP-Seq). We use this method to identify mRNAs of 7,676 mammalian genes that contain m(6)A, indicating that m(6)A is a common base modification of mRNA. The m(6)A modification exhibits tissue-specific regulation and is markedly increased throughout brain development. We find that m(6)A sites are enriched near stop codons and in 3' UTRs, and we uncover an association between m(6)A residues and microRNA-binding sites within 3' UTRs. These findings provide a resource for identifying transcripts that are substrates for adenosine methylation and reveal insights into the epigenetic regulation of the mammalian transcriptome. Copyright © 2012 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Onco Targets Ther
                Onco Targets Ther
                ott
                ott
                OncoTargets and therapy
                Dove
                1178-6930
                14 January 2021
                2021
                : 14
                : 413-426
                Affiliations
                [1 ]Department of Colorectal Surgery and Oncology, Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, The Second Affiliated Hospital, Zhejiang University School of Medicine , Hangzhou, Zhejiang, People’s Republic of China
                [2 ]Cancer Institute, The Second Affiliated Hospital, Zhejiang University School of Medicine , Hangzhou, Zhejiang, People’s Republic of China
                Author notes
                Correspondence: Kefeng Ding Department of Colorectal Surgery and Oncology, Key Laboratory of Cancer Prevention and Intervention, Ministry of Education, The Second Affiliated Hospital, Zhejiang University School of Medicine , Hangzhou310009, Zhejiang, People’s Republic of ChinaTel/Fax +86-571-87784820 Email dingkefeng@zju.edu.cn
                [*]

                These authors contributed equally to this work

                Article
                288663
                10.2147/OTT.S288663
                7814279
                33488096
                bf0461e4-4714-4f4a-a09d-dcebc586dad5
                © 2021 Wei et al.

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                History
                : 25 October 2020
                : 23 December 2020
                Page count
                Figures: 8, Tables: 3, References: 42, Pages: 14
                Funding
                Funded by: the National Key R&D Program of China;
                Funded by: the Key Technology Research and Development Program of Zhejiang Province;
                Funded by: the National Natural Science Foundation of China;
                This study was funded by the National Key R&D Program of China (2017YFC0908200), the Key Technology Research and Development Program of Zhejiang Province (2017C03017), and the National Natural Science Foundation of China (81772545).
                Categories
                Original Research

                Oncology & Radiotherapy
                renal cell carcinoma,m6a methylation,tcga,prognostic signature
                Oncology & Radiotherapy
                renal cell carcinoma, m6a methylation, tcga, prognostic signature

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