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      A novel prognostic N 7-methylguanosine-related long non-coding RNA signature in clear cell renal cell carcinoma

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          Abstract

          Clear cell renal cell carcinoma (ccRCC) is regulated by methylation modifications and long noncoding RNAs (lncRNAs). However, knowledge of N 7-methylguanosine (m 7G)-related lncRNAs that predict ccRCC prognosis remains insufficient. A prognostic multi-lncRNA signature was created using LASSO regression to examine the differential expression of m7G-related lncRNAs in ccRCC. Furthermore, we performed Kaplan–Meier analysis and area under the curve (AUC) analysis for diagnosis. In all, a model based on five lncRNAs was developed. Principal component analysis (PCA) indicated that the risk model precisely separated the patients into different groups. The IC 50 value for drug sensitivity divided patients into two risk groups. High-risk group of patients was more susceptible to A.443654, A.770041, ABT.888, AMG.706, and AZ628. Moreover, a lower tumor mutation burden combined with low-risk scores was associated with a better prognosis of ccRCC. Quantitative real-time polymerase chain reaction (qRT-PCR) exhibited that the expression levels of LINC01507, AC093278.2 were very high in all five ccRCC cell lines, AC084876.1 was upregulated in all ccRCC cell lines except 786-O, and the levels of AL118508.1 and DUXAP8 were upregulated in the Caki-1 cell line. This risk model may be promising for the clinical prediction of prognosis and immunotherapeutic responses in patients with ccRCC.

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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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            KEGG: kyoto encyclopedia of genes and genomes.

            M Kanehisa (2000)
            KEGG (Kyoto Encyclopedia of Genes and Genomes) is a knowledge base for systematic analysis of gene functions, linking genomic information with higher order functional information. The genomic information is stored in the GENES database, which is a collection of gene catalogs for all the completely sequenced genomes and some partial genomes with up-to-date annotation of gene functions. The higher order functional information is stored in the PATHWAY database, which contains graphical representations of cellular processes, such as metabolism, membrane transport, signal transduction and cell cycle. The PATHWAY database is supplemented by a set of ortholog group tables for the information about conserved subpathways (pathway motifs), which are often encoded by positionally coupled genes on the chromosome and which are especially useful in predicting gene functions. A third database in KEGG is LIGAND for the information about chemical compounds, enzyme molecules and enzymatic reactions. KEGG provides Java graphics tools for browsing genome maps, comparing two genome maps and manipulating expression maps, as well as computational tools for sequence comparison, graph comparison and path computation. The KEGG databases are daily updated and made freely available (http://www. genome.ad.jp/kegg/).
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              KEGG: new perspectives on genomes, pathways, diseases and drugs

              KEGG (http://www.kegg.jp/ or http://www.genome.jp/kegg/) is an encyclopedia of genes and genomes. Assigning functional meanings to genes and genomes both at the molecular and higher levels is the primary objective of the KEGG database project. Molecular-level functions are stored in the KO (KEGG Orthology) database, where each KO is defined as a functional ortholog of genes and proteins. Higher-level functions are represented by networks of molecular interactions, reactions and relations in the forms of KEGG pathway maps, BRITE hierarchies and KEGG modules. In the past the KO database was developed for the purpose of defining nodes of molecular networks, but now the content has been expanded and the quality improved irrespective of whether or not the KOs appear in the three molecular network databases. The newly introduced addendum category of the GENES database is a collection of individual proteins whose functions are experimentally characterized and from which an increasing number of KOs are defined. Furthermore, the DISEASE and DRUG databases have been improved by systematic analysis of drug labels for better integration of diseases and drugs with the KEGG molecular networks. KEGG is moving towards becoming a comprehensive knowledge base for both functional interpretation and practical application of genomic information.
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                Author and article information

                Contributors
                zhixunbai@zmu.edu.cn
                mswu0909@zmu.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                27 October 2023
                27 October 2023
                2023
                : 13
                : 18454
                Affiliations
                [1 ]School of Stomatology, Zunyi Medical University, ( https://ror.org/00g5b0g93) Zunyi, 563000 Guizhou China
                [2 ]Department of Clinical, Zunyi Medical and Pharmaceutical College, Zunyi, 563000 Guizhou China
                [3 ]Department of Medical Genetics, Zunyi Medical University, ( https://ror.org/00g5b0g93) Zunyi, 563000 Guizhou China
                [4 ]Department of Pathology, Affiliated Hospital of Zunyi Medical University, ( https://ror.org/00g5b0g93) Zunyi, 563000 Guizhou China
                [5 ]GRID grid.413390.c, ISNI 0000 0004 1757 6938, Department of Nephrology, , the Second Affiliated Hospital of Zunyi Medical University, ; Zunyi, 563000 Guizhou China
                Author information
                http://orcid.org/0000-0003-4352-0658
                http://orcid.org/0000-0002-0173-102X
                Article
                45287
                10.1038/s41598-023-45287-w
                10611723
                37891201
                fa9b0bca-5532-454e-a418-85c35dcd3105
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 3 February 2023
                : 18 October 2023
                Funding
                Funded by: The Special Project of Innovation and Exploration in Zunyi Medical University
                Award ID: 2020-024
                Award Recipient :
                Funded by: Natural Science and Technology Foundation of Guizhou Province
                Award ID: QiankeheZhicheng [2022] YiBan182
                Award Recipient :
                Funded by: The Project of Guizhou Provincial Health Commission
                Award ID: gzwkj2021-138
                Award Recipient :
                Funded by: The Technological Project of Zunyi Science and Technology Bureau
                Award ID: HZ-2021- No. 218
                Award ID: HZ-2021-No. 298
                Award Recipient :
                Funded by: National Natural Science Foundation of China
                Award ID: 82260106
                Award Recipient :
                Funded by: The Zunyi “15851 Talent Elite” Project
                Award ID: 81760508
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2023

                Uncategorized
                cancer,computational biology and bioinformatics,genetics,immunology
                Uncategorized
                cancer, computational biology and bioinformatics, genetics, immunology

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