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      Comprehensive Analysis of the Expression and Prognostic Significance of the CENP Family in Breast Cancer

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          Abstract

          Background

          Centromere proteins (CENPs) are a set of protein-coding genes involved in the transient assembly of the kinetochore which occurs during mitosis. This study intended to clarify the expression patterns, prognosis and potential mechanisms of CENPs in breast cancer (BC).

          Methods

          Coexpedia was used to screen GEO datasets and PubMed articles related to CENPs and BC. CENPs expressions, prognosis and alteration were analyzed by Oncomine, Ualcan and Kaplan Meier plotter and cBioPortal. The correlation and interaction of CENPs was performed by Breast Cancer Gene-Expression Miner, GeneMANIA and STRING portal. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted to clarify the functional roles of CENPs. CENPF, E, U, A, N, I, K, W, M, L were selected for further analysis.

          Results

          All CENPs were highly expressed in BC compared to normal tissue. High expression of CENPF, E, U, A, N, I, W, M, L and CENPF, E, U, A, N, I, M correlated with worse relapse free survival (RFS) and worse overall survival (OS), respectively. All of 10 CENPs indicated positive correlations and complex interactions between each other at mRNA expression and protein level. CENPs were enriched GO terms mainly in centromere complex assembly and KEGG terms in progesterone-mediated oocyte maturation, cell cycle and oocyte meiosis.

          Conclusion

          The 10 CENPs could be diagnostic biomarkers and all of them except CENPK can be used as prognosis biomarkers in BC. CENPs play an oncogenic role and may be the potential therapy targets of treatment for BC patients.

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          Most cited references51

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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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            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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              Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal.

              The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.
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                Author and article information

                Journal
                Int J Gen Med
                Int J Gen Med
                ijgm
                International Journal of General Medicine
                Dove
                1178-7074
                29 March 2022
                2022
                : 15
                : 3471-3482
                Affiliations
                [1 ]Breast Cancer Center, The Fourth Hospital of Hebei Medical University , Shijiazhuang, 050000, Hebei, People’s Republic of China
                Author notes
                Correspondence: Yunjiang Liu, Tel +86-13703297890, Email lyj818326@outlook.com
                Author information
                http://orcid.org/0000-0001-7202-2004
                Article
                354200
                10.2147/IJGM.S354200
                8976518
                35378917
                6270d394-dff0-46ae-83d6-eba8b413a1ab
                © 2022 Liu and Liu.

                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
                : 23 December 2021
                : 23 March 2022
                Page count
                Figures: 7, References: 51, Pages: 12
                Funding
                Funded by: Natural Science Foundation of Hebei Province, open-funder-registry 10.13039/501100003787;
                Funded by: Key Research and Development Foundation of Science and Technology Department of Hebei Province;
                This work was supported by Natural Science Foundation of Hebei Province (No. H2020206210); Key Research and Development Foundation of Science and Technology Department of Hebei Province (No. 192777125D).
                Categories
                Original Research

                Medicine
                centromere proteins,breast cancer,diagnosis,prognosis,bioinformatics analysis
                Medicine
                centromere proteins, breast cancer, diagnosis, prognosis, bioinformatics analysis

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