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      Integrated Bioinformatics Analysis and Validation of the Prognostic Value of RBM10 Expression in Hepatocellular Carcinoma

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          Abstract

          Background

          RBM10ʹs function in hepatocellular carcinoma (HCC) has rarely been addressed. We intend to explore the prognostic significance and therapeutic meaning of RBM10 in HCC in this study.

          Methods

          Multiple common databases were integrated to analyze the expression status and prognostic meaning of RBM10 in HCC. The relationship between RBM10 mRNA level and clinical features was also assessed. Multiple enrichment analyses of the differentially expressed genes between RBM10 high- and low- transcription groups were constructed by using R software (version 4.0.2). A Search Tool for Retrieval of Interacting Genes database was used to construct the protein–protein interaction network between RBM10 and other proteins. A tumor immune estimation resource database was employed to identify the relationship between RBM10 expression and immune cell infiltrates. The prognostic value of RBM10 expression was validated in our HCC cohort by immunohistochemistry test.

          Results

          The transcription of RBM10 mRNA was positively correlated with tumor histologic grade (p < 0.001), T classification (p < 0.001), and tumor stage (p < 0.001). High transcription of RBM10 in HCC predicted a dismal overall survival (p = 0.0037) and recurrence-free survival (p < 0.001). Kyoto Encyclopedia of Genes and Genomes, Gene Ontology, and Gene Set Enrichment Analysis all revealed that RBM10 was involved in the regulation of cell cycle, DNA replication, and immune-related pathways. Tumor immune estimation analysis revealed that RBM10 transcription was positively related to multiple immune cell infiltrates and the expressions of PD-1 and PD-L1.

          Conclusion

          RBM10 was demonstrated to be a dismal prognostic factor and a potential biomarker for immune therapy in HCC in that it may be involved in the immune-related signaling pathways.

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          Most cited references33

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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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            GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses

            Abstract Tremendous amount of RNA sequencing data have been produced by large consortium projects such as TCGA and GTEx, creating new opportunities for data mining and deeper understanding of gene functions. While certain existing web servers are valuable and widely used, many expression analysis functions needed by experimental biologists are still not adequately addressed by these tools. We introduce GEPIA (Gene Expression Profiling Interactive Analysis), a web-based tool to deliver fast and customizable functionalities based on TCGA and GTEx data. GEPIA provides key interactive and customizable functions including differential expression analysis, profiling plotting, correlation analysis, patient survival analysis, similar gene detection and dimensionality reduction analysis. The comprehensive expression analyses with simple clicking through GEPIA greatly facilitate data mining in wide research areas, scientific discussion and the therapeutic discovery process. GEPIA fills in the gap between cancer genomics big data and the delivery of integrated information to end users, thus helping unleash the value of the current data resources. GEPIA is available at http://gepia.cancer-pku.cn/.
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              TIMER: A Web Server for Comprehensive Analysis of Tumor-Infiltrating Immune Cells.

              Recent clinical successes of cancer immunotherapy necessitate the investigation of the interaction between malignant cells and the host immune system. However, elucidation of complex tumor-immune interactions presents major computational and experimental challenges. Here, we present Tumor Immune Estimation Resource (TIMER; cistrome.shinyapps.io/timer) to comprehensively investigate molecular characterization of tumor-immune interactions. Levels of six tumor-infiltrating immune subsets are precalculated for 10,897 tumors from 32 cancer types. TIMER provides 6 major analytic modules that allow users to interactively explore the associations between immune infiltrates and a wide spectrum of factors, including gene expression, clinical outcomes, somatic mutations, and somatic copy number alterations. TIMER provides a user-friendly web interface for dynamic analysis and visualization of these associations, which will be of broad utilities to cancer researchers. Cancer Res; 77(21); e108-10. ©2017 AACR.
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                Author and article information

                Journal
                Cancer Manag Res
                Cancer Manag Res
                cmar
                Cancer Management and Research
                Dove
                1179-1322
                04 March 2022
                2022
                : 14
                : 969-980
                Affiliations
                [1 ]Department V of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University , Shanghai, 200438, People’s Republic of China
                [2 ]Department of Hepatobiliary Surgery, The 940th Hospital of CPLA Joint Logistics Support Force , Lanzhou, 730050, People’s Republic of China
                [3 ]Hongqiao International Institute of Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai, 200336, People’s Republic of China
                Author notes
                Correspondence: Ning Yang, Department V of Hepatic Surgery, Eastern Hepatobiliary Surgery Hospital, Second Military Medical University , Shanghai, 200438, People’s Republic of China, Tel +86 21 81877591, Fax +86 21 6556 6851, Email lancet00@163.com
                Guang-Zhi Jin, Hongqiao International Institute of Medicine, Tongren Hospital, Shanghai Jiao Tong University School of Medicine , Shanghai, 200336, People’s Republic of China, Email jgzhi@hotmail.com
                [*]

                These authors contributed equally to this work

                Article
                349884
                10.2147/CMAR.S349884
                8906710
                ea6b9f73-e5c9-4962-af9b-8c854b34b3be
                © 2022 Pang 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
                : 17 November 2021
                : 11 February 2022
                Page count
                Figures: 7, References: 33, Pages: 12
                Funding
                Funded by: the State Key Project for Liver Cancer;
                This study was supported by the State Key Project for Liver Cancer (2012ZX10002017-004, 2017ZX10203205-001-002). The funder was not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.
                Categories
                Original Research

                Oncology & Radiotherapy
                rbm10,hepatocellular carcinoma,prognosis,differentially expressed gene,integrated bioinformatics analysis

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