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      Explore the shared molecular mechanism between dermatomyositis and nasopharyngeal cancer by bioinformatic analysis

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          Abstract

          Background

          Dermatomyositis (DM) is prone to nasopharyngeal carcinoma (NPC), but the mechanism is unclear. This study aimed to explore the potential pathogenesis of DM and NPC.

          Methods

          The datasets GSE46239, GSE142807, GSE12452, and GSE53819 were downloaded from the GEO dataset. The disease co-expression module was obtained by R-package WGCNA. We built PPI networks for the key modules. ClueGO was used to analyze functional enrichment for the key modules. DEG analysis was performed with the R-package "limma". R-package “pROC” was applied to assess the diagnostic performance of hub genes. MiRNA-mRNA networks were constructed using MiRTarBase and miRWalk databases.

          Results

          The key modules that positively correlated with NPC and DM were found. Its intersecting genes were enriched in the negative regulation of viral gene replication pathway. Similarly, overlapping down-regulated DEGs in DM and NPC were also enriched in negatively regulated viral gene replication. Finally, we identified 10 hub genes that primarily regulate viral biological processes and type I interferon responses. Four key genes (GBP1, IFIH1, IFIT3, BST2) showed strong diagnostic performance, with AUC>0.8. In both DM and NPC, the expression of key genes was correlated with macrophage infiltration level. Based on hub genes’ miRNA-mRNA network, hsa-miR-146a plays a vital role in DM-associated NPC.

          Conclusions

          Our research discovered pivot genes between DM and NPC. Viral gene replication and response to type I interferon may be the crucial bridge between DM and NPC. By regulating hub genes, MiR-146a will provide new strategies for diagnosis and treatment in DM complicated by NPC patients. For individuals with persistent viral replication in DM, screening for nasopharyngeal cancer is necessary.

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          Most cited references57

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          Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

          Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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            WGCNA: an R package for weighted correlation network analysis

            Background Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. Results The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. Conclusion The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at .
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              Robust enumeration of cell subsets from tissue expression profiles

              We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen, and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content, and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu).
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: ResourcesRole: SoftwareRole: Writing – original draft
                Role: ConceptualizationRole: Data curationRole: InvestigationRole: MethodologyRole: Visualization
                Role: ConceptualizationRole: InvestigationRole: Methodology
                Role: SupervisionRole: Validation
                Role: SupervisionRole: Validation
                Role: ResourcesRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                16 May 2024
                2024
                : 19
                : 5
                : e0296034
                Affiliations
                [1 ] Department of rheumatology and immunology, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, China
                [2 ] School of Medicine, Key Laboratory of Viral Pathogenesis & Infection Prevention and Control, Guangzhou Key Laboratory of Germ-free Animals and Microbiota Application, Jinan University, Guangzhou, China
                Concordia University, CANADA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                ‡ XZ and JS are contributed equally to this work and co-author.

                Author information
                https://orcid.org/0000-0002-0432-3638
                Article
                PONE-D-23-24175
                10.1371/journal.pone.0296034
                11098312
                38753689
                0823d549-8b78-4097-9ad8-bb0058e0853d
                © 2024 Zhong et al

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

                History
                : 31 July 2023
                : 4 December 2023
                Page count
                Figures: 10, Tables: 0, Pages: 19
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100013064, Key Research and Development Program of Jiangxi Province;
                Award ID: No. 20192BBGL70024
                Award Recipient :
                This study was supported by a Key Research and Development Program of Jiangxi Province grant awarded to XD [20192BBGL70024].
                Categories
                Research Article
                Medicine and Health Sciences
                Oncology
                Cancers and Neoplasms
                Carcinoma
                Nasopharyngeal Carcinoma
                Biology and Life Sciences
                Biochemistry
                Proteins
                Interferons
                Biology and Life Sciences
                Microbiology
                Virology
                Viral Replication
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Blood Cells
                White Blood Cells
                Macrophages
                Biology and Life Sciences
                Cell Biology
                Cellular Types
                Animal Cells
                Immune Cells
                White Blood Cells
                Macrophages
                Biology and Life Sciences
                Immunology
                Immune Cells
                White Blood Cells
                Macrophages
                Medicine and Health Sciences
                Immunology
                Immune Cells
                White Blood Cells
                Macrophages
                Biology and Life Sciences
                Genetics
                Gene Expression
                Gene Regulation
                Biology and Life Sciences
                Microbiology
                Virology
                Viral Transmission and Infection
                Biology and life sciences
                Biochemistry
                Nucleic acids
                RNA
                Non-coding RNA
                Natural antisense transcripts
                MicroRNAs
                Biology and life sciences
                Genetics
                Gene expression
                Gene regulation
                MicroRNAs
                Biology and Life Sciences
                Genetics
                Gene Expression
                Custom metadata
                All relevant data are within the paper and its Supporting Information files.

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