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      The Polygenic Map of Keloid Fibroblasts Reveals Fibrosis-Associated Gene Alterations in Inflammation and Immune Responses

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          Abstract

          Due to many inconsistencies in differentially expressed genes (DEGs) related to genomic expression changes during keloid formation and a lack of satisfactory prevention and treatment methods for this disease, the critical biomarkers related to inflammation and the immune response affecting keloid formation should be systematically clarified. Normal skin/keloid scar tissue-derived fibroblast genome expression data sets were obtained from the Gene Expression Omnibus (GEO) and ArrayExpress databases. Hub genes have a high degree of connectivity and gene function aggregation in the integration network. The hub DEGs were screened by gene-related protein–protein interactions (PPIs), and their biological processes and signaling pathways were annotated to identify critical biomarkers. Finally, eighty-one hub DEGs were selected for further analysis, and some noteworthy signaling pathways and genes were found to be closely related to keloid fibrosis. For example, IL17RA is involved in IL-17 signal transduction, TIMP2 and MMP14 activate extracellular matrix metalloproteinases, and TNC, ITGB2, and ITGA4 interact with cell surface integrins. Furthermore, changes in local immune cell activity in keloid tissue were detected by DEG expression, immune cell infiltration, and mass CyTOF analyses. The results showed that CD4+ T cells, CD8+ T cells and NK cells were abnormal in keloid tissue compared with normal skin tissue. These findings not only support the key roles of fibrosis-related pathways, immune cells and critical genes in the pathogenesis of keloids but also expand our understanding of targets that may be useful for the treatment of fibrotic diseases.

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          Most cited references52

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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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            GSVA: gene set variation analysis for microarray and RNA-Seq data

            Background Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets. Results To address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments. Conclusions GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.
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              The Molecular Signatures Database (MSigDB) hallmark gene set collection.

              The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.
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                Author and article information

                Contributors
                Journal
                Front Immunol
                Front Immunol
                Front. Immunol.
                Frontiers in Immunology
                Frontiers Media S.A.
                1664-3224
                10 January 2022
                2021
                : 12
                : 810290
                Affiliations
                [1] 1 Department of Dermatology, The Affiliated Qingdao Municipal Hospital of Qingdao University , Qingdao, China
                [2] 2 Department of Dermatology, Qilu Hospital, Shandong University , Jinan, China
                [3] 3 Department of Dermatology, Shandong Provincial Qianfoshan Hospital, The First Hospital Affiliated with Shandong First Medical University , Jinan, China
                Author notes

                Edited by: Ferhat Ay, La Jolla Institute for Immunology (LJI), United States

                Reviewed by: Ferdinand Nangole, University of Nair, Kenya; Jillian M. Richmond, University of Massachusetts Medical School, United States

                *Correspondence: Tongxin Shi, shitongxin2006@ 123456163.com

                This article was submitted to Systems Immunology, a section of the journal Frontiers in Immunology

                †These authors share first authorship

                Article
                10.3389/fimmu.2021.810290
                8785650
                35082796
                03a2a1c6-7e80-45e8-b1cc-84c7e8177925
                Copyright © 2022 Li, Li, Qu, Li, Tang, Zhou, Yu, Wang, Xin and Shi

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 06 November 2021
                : 15 December 2021
                Page count
                Figures: 5, Tables: 2, Equations: 0, References: 53, Pages: 12, Words: 4978
                Categories
                Immunology
                Original Research

                Immunology
                fibroblasts,keloid,molecular alterations,genome-wide expression,immune response,inflammation

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