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      Identification of hub biomarkers and immune cell infiltration in polymyositis and dermatomyositis

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          Abstract

          Objective: Polymyositis (PM) and dermatomyositis (DM) are heterogeneous disorders. However, the etiology of PM/DM development has not been thoroughly clarified.

          Methods: Gene expression data of PM/DM were obtained from Gene Expression Omnibus. We used robust rank aggregation (RRA) to identify differentially expressed genes (DEGs). Gene Ontology functional enrichment and pathway analyses were used to investigate potential functions of the DEGs. Weighted gene co-expression network analysis (WGCNA) was used to establish a gene co-expression network. CIBERSORT was utilized to analyze the pattern of immune cell infiltration in PM/DM. Protein–protein interaction (PPI) network, Venn, and association analyses between core genes and muscle injury were performed to identify hub genes. Receiver operating characteristic analyses were executed to investigate the value of hub genes in the diagnosis of PM/DM, and the results were verified using the microarray dataset GSE48280.

          Results: Five datasets were included. The RRA integrated analysis identified 82 significant DEGs. Functional enrichment analysis revealed that immune function and the interferon signaling pathway were enriched in PM/DM. WGCNA outcomes identified MEblue and MEturquoise as key target modules in PM/DM. Immune cell infiltration analysis revealed greater macrophage infiltration and lower regulatory T-cell infiltration in PM/DM patients than in healthy controls. PPI network, Venn, and association analyses of muscle injury identified five putative hub genes: TRIM22, IFI6, IFITM1, IFI35, and IRF9.

          Conclusions: Our bioinformatics analysis identified new genetic biomarkers of the pathogenesis of PM/DM. We demonstrated that immune cell infiltration plays a pivotal part in the occurrence of PM/DM.

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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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            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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              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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                Author and article information

                Journal
                Aging (Albany NY)
                Aging
                Aging (Albany NY)
                Impact Journals
                1945-4589
                31 May 2022
                24 May 2022
                : 14
                : 10
                : 4530-4555
                Affiliations
                [1 ]Department of Clinical Laboratory, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, Beijing, China
                [2 ]Department of Clinical Laboratory, Beijing Anzhen Hospital, Capital Medical University, Beijing, China
                Author notes
                Correspondence to: Yongzhe Li; email: yongzhelipumch@126.com, https://orcid.org/0000-0002-8267-0985
                Article
                204098 204098
                10.18632/aging.204098
                9186768
                35609018
                7cc5f065-c45e-4a36-9530-ef54f3d661f2
                Copyright: © 2022 Chen et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 23 December 2021
                : 12 April 2022
                Categories
                Research Paper

                Cell biology
                idiopathic inflammatory myopathy,macrophage infiltration,interferon signaling,autoimmune disease,gene set expression analysis

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