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      Comprehensive analyses indicated the association between m6A related long non‐coding RNAs and various pathways in glioma

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          Abstract

          Background

          Glioma is one of the most malignant brain tumors and diseases. N6‐methyladenosine modification (m6A) is the most abundant and prevalent internal chemical modification of mRNA and long non‐coding RNAs (lncRNAs) in eukaryotes. Nevertheless, the correlated pathways and clinical utilization of m6A‐related lncRNAs have not been fully evaluated in glioma.

          Methods

          Public RNA‐sequencing and clinical annotation data were retrieved from TCGA, CGGA and GEO database. Differential expression analysis and univariate Cox regression analysis were performed to identify the m6A‐related and differentially expressed lncRNAs with prognostic function (m6A‐DELPF). The consensus clustering was performed to identify the expression pattern of m6A‐DELPF. LASSO Cox regression analysis was performed to construct the lncRNA‐based signature. The CIBERSORT and ESTIMATE algorithms were performed to analyze immune infiltration and tumor microenvironment, respectively. Immunotherapy sensitivity analysis was performed using data from TCIA. The small molecule drugs prediction analysis was performed using The Connectivity Map (CMap) database and STITCH database. A competing endogenous RNAs (ceRNA) network was constructed based on miRcode, miRDB, miRTarBase, TargetScan database.

          Results

          Two clusters (cluster1 and cluster2) were identified after unsupervised cluster analysis based on m6A‐DELPF. Additionally, a 15‐gene prognostic signature namely m6A‐DELPFS was constructed. Analyses of epithelial‐mesenchymal‐transition score, tumor microenvironment, immune infiltration, clinical characterization analysis, and putative drug prediction were performed to confirm the clinical utility and efficacy of m6A‐DELPFS. The potential mechanisms including tumor immune microenvironment of m6A‐DELPF influence the initiation and progression of glioma. A clinically accessible nomogram was also constructed based on the m6A‐DELPF and other survival‐relevant clinical parameters. Two miRNAs and 114 mRNAs were identified as the downstream of seven m6A‐related lncRNAs in a ceRNA network.

          Conclusion

          Our present research confirmed the clinical value of m6A related lncRNAs and their high correlation with tumor immunity, tumor microenvironment, tumor mutation burden and drug sensitivity in glioma.

          Abstract

          Our present research confirmed the clinical value of m6A related lncRNAs and their high correlation with tumor immunity, tumor microenvironment, tumor mutation burden and drug sensitivity in glioma.

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          Most cited references81

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              Is Open Access

              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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                Author and article information

                Contributors
                zhangmengqi8912@163.com
                Journal
                Cancer Med
                Cancer Med
                10.1002/(ISSN)2045-7634
                CAM4
                Cancer Medicine
                John Wiley and Sons Inc. (Hoboken )
                2045-7634
                06 June 2022
                January 2023
                : 12
                : 1 ( doiID: 10.1002/cam4.v12.1 )
                : 760-788
                Affiliations
                [ 1 ] Department of Neurology, Xiangya Hospital Central South University Changsha China
                [ 2 ] Department of Neurosurgery, Xiangya Hospital Central South University Changsha China
                [ 3 ] National Clinical Research Center for Geriatric Disorders, Xiangya Hospital Central South University Changsha China
                Author notes
                [*] [* ] Correspondence

                Mengqi Zhang, Department of Neurology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha 410008, China.

                Email: zhangmengqi8912@ 123456163.com

                Author information
                https://orcid.org/0000-0003-2090-6320
                Article
                CAM44913 CAM4-2021-10-4574.R2
                10.1002/cam4.4913
                9844638
                35668574
                cd55a6fa-9e4f-434a-9e68-7a55a3f9df05
                © 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 23 April 2022
                : 31 October 2021
                : 25 May 2022
                Page count
                Figures: 14, Tables: 0, Pages: 29, Words: 13679
                Funding
                Funded by: Key Research and Development Program of Hunan Province of China , doi 10.13039/501100019091;
                Award ID: 2020SK2063
                Funded by: Natural Science Foundations for Excellent Young Scholars of Hunan Province
                Award ID: 2021JJ20095
                Funded by: Natural Science Foundations of Hunan Province
                Award ID: 2020JJ4134
                Funded by: Research Project on Education and Teaching Innovation of Central South University
                Award ID: 2021jy145
                Funded by: National Natural Science Foundation of China , doi 10.13039/501100001809;
                Award ID: 81501025
                Categories
                Research Article
                Research Articles
                Bioinformatics
                Custom metadata
                2.0
                January 2023
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.2.3 mode:remove_FC converted:17.01.2023

                Oncology & Radiotherapy
                glioma,immunotherapy sensitivity,m6a related lncrna,prognosis prediction,tumor microenvironment,tumor mutation burden

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