Research Article

Mapping the Knowledge Landscape of Muscle Disorders in Rheumatoid Arthritis: A Bibliometric and Bioinformatics Study

DOI:

10.3791/70373

⸱

April 21st, 2026

 ,  ,  , 
1Department of TCM Rheumatology, China-Japan Friendship Hospital, 2School of Acupuncture-Moxibustion and Tuina, Beijing University of Chinese Medicine, 3Department of Integrated TCM and Western Medicine for Pulmonary Diseases, China-Japan Friendship Hospital

In This Article

Summary

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This study employs bibliometric analysis to delineate the current research landscape of rheumatoid arthritis-associated muscle disorders and, further, uses bioinformatics approaches to investigate shared genes and enriched pathways between rheumatoid arthritis and myositis.

Abstract

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This study conducted a bibliometric analysis of publications related to the comorbidity between rheumatoid arthritis (RA) and Muscle Disorders, sourced from the Web of Science Core Collection database between January 1, 2015, and December 31, 2024. By examining key characteristics of the published articles, the study aimed to identify global research hotspots in this field. Utilizing tools such as VOSviewer, CiteSpace, Scimago Graphica, and Excel, a visual analysis of the research landscape was performed. The analysis revealed that the annual publication output in this domain exhibited a cyclical fluctuation. The United States led in total publications, followed by China. Publishing institutions formed regional collaborative networks with significant radiating effects. Karolinska Institutet and Karolinska University Hospital in Sweden ranked first and second, with 50 and 30 publications, respectively. Regarding specific research topics, the keyword cluster analysis identified 12 thematic clusters, including myositis, tumor necrosis factor, and metabolism. Given that myositis is a research hotspot within the comorbidity of RA and Muscle Disorders, we further employed GeneCards to analyze the correlation between RA and myositis in terms of gene expression and pathways, presenting the results graphically. GeneCards screening identified 359 shared genes. Protein-protein interaction network analysis revealed that genes such as TNF, IL6, and IFNG occupy central positions. KEGG enrichment analysis indicated that the TNF, JAK-STAT, and IL-17 signaling pathways play critical roles.

Introduction

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Rheumatoid Arthritis (RA) is a chronic autoimmune inflammatory disorder characterized by symmetric joint involvement, with a global prevalence of approximately 0.5–1%1,2. The core pathological mechanism of RA involves aberrant activation of the immune system, leading to persistent synovial membrane inflammation and a cascade of articular pathological changes3,4,5. Although synovium represents the primary target of RA, the disease is often accompanied by various systemic manifestations, including cardiovascular diseases, osteoporosis, and muscle-related disorders6,7,8,9. Among these, RA-related muscular alterations, such as muscle atrophy, reduced muscle strength, and myositis, have gradually emerged as a research hotspot in recent years. These complications not only exacerbate functional impairment and diminish quality of life but also influence disease prognosis and treatment response10,11,12,13. Although muscle disorders are clinically prevalent among patients with RA, the specific hotspot conditions remain unclear, and the underlying comorbid molecular mechanisms have yet to be systematically elucidated. Moreover, the global research trends and knowledge structure in this field warrant further systematic investigation14.

Over the past decade, significant progress has been made in research on RA-related muscle disorders, driven by advances in biologic therapies, targeted treatments, and rehabilitation medicine15,16,17. However, a comprehensive analysis of the shifting research focus, collaborative networks, and evolution of core themes within the academic community is still lacking. Bibliometrics, a quantitative method for analyzing academic literature, can reveal research trends, contributions from countries and institutions, keyword hotspots, and interdisciplinary dynamics within a specific field18. Visualization techniques, such as co-occurrence networks and clustering analysis, further offer intuitive insights into the evolution of knowledge structures. Bioinformatics analysis enables in-depth mining of biological data to reveal potential disease pathogenesis and key regulatory pathways at the molecular level. The integration of these two approaches facilitates the construction of a comprehensive research framework, spanning from macroscopic knowledge maps to microscopic molecular networks, thereby providing novel multidimensional perspectives for understanding the pathogenesis of RA-associated muscle disorders and identifying potential intervention targets.

Based on the Web of Science database, this study employs bibliometric tools including VOSviewer, CiteSpace, Scimago Graphica, and Excel to systematically analyze annual publication volume, national and institutional contributions, keyword co-occurrence, and emerging thematic trends in the field of RA associated with muscle-related disorders. Additionally, the GeneCards database was employed to analyze shared genes and enriched pathways associated with both myositis and RA. The findings aim to provide new insights into mechanistic exploration, clinical intervention, and interdisciplinary collaboration regarding muscle-related disorders associated with RA.

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Protocol

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The overall workflow of this study is illustrated in Figure 1, outlining the sequential steps of literature search, bibliometric analysis, identification of shared genes, construction of the protein-protein interaction network, and pathway enrichment analysis.

Literature search strategy
The literature search was conducted on June 22, 2025, on the Web of Science (WOS) Core Collection database19. The search was limited to the Science Citation Index Expanded (SCI-Expanded) and Social Sciences Citation Index (SSCI), covering publications from January 1, 2015, to December 31, 2024. The specific search terms and the corresponding number of results are provided in Table 1. After the initial search, document types such as meeting abstracts, conference papers, editorial materials, book chapters, and retracted publications were excluded. Subsequently, only records in the English language and of the "Article" or "Review Article" types were retained for further analysis.

Bibliometric analysis
Bibliometric analysis and visualization require the support of specific software tools, details of which are provided in Table of Materials. This study aims to construct a knowledge map of the interdisciplinary research field bridging myositis and rheumatoid arthritis. The analytical scope encompasses publication trend analysis over the past decade, patterns of international collaboration and collaborative networks, academic influence among institutions/journals/authors, core keywords reflecting research hotspots, and citation network analysis.

Annual publication count data were first exported from the Web of Science database and used to generate statistical charts illustrating publication trends. The retrieved literature data was then imported into VOSviewer software for analysis: within the "Co-authorship" module, options for "Authors," "Organizations," and "Countries" were sequentially selected to generate author collaboration networks, institutional collaboration networks, and country collaboration networks respectively; within the "Citation" module, the "Sources" option was selected to create journal collaboration networks; within the "Co-citation" module, "Cited Sources" and "Cited Authors" were chosen to obtain cited journal collaboration networks and cited author collaboration networks. To more intuitively visualize international collaborative relationships, the country collaboration network was further aesthetically optimized using Scimago Graphica software to enhance visual clarity and interpretability20.

Analysis of keyword hotspots and cross-disciplinary thematic terms was conducted using CiteSpace software21. The Pathfinder algorithm was selected for network pruning, with the time span set from January 2015 to December 2024, divided into monthly time slices. The node type was configured as "Keyword," yielding a keyword-based cluster map of research hotspots, representative thematic terms for each cluster, and the top 20 most frequently occurring hotspot keywords. Citation network analysis followed a similar procedural workflow, requiring only the modification of the node type to "Cited Reference" to accomplish the corresponding analysis.

Identification of shared genes
Based on bibliometric analysis, the keyword "myositis" showed significant prominence in the interdisciplinary research domain of muscle pathology and rheumatoid arthritis. Therefore, this study selected myositis and rheumatoid arthritis as research subjects to conduct overlapping gene analysis. The specific procedure was as follows: First, target genes associated with myositis and rheumatoid arthritis were retrieved from the GeneCards database22. To ensure the comprehensiveness of the analysis, this study did not set a relevance score threshold and included all reported genes associated with the diseases, thereby constructing two corresponding gene sets. By comparing these two sets, overlapping genes were identified. These overlapping genes were ultimately defined as "RA-Myositis comorbidity-related genes" and served as the core dataset for subsequent analyses.

Protein-protein interaction (PPI) network construction
Using the "Multiple proteins" query function in the STRING database, we submitted the "RA-myositis comorbidity-related genes" list for analysis23. The organism was set to "Homo sapiens" with a minimum required interaction score threshold of ≥0.700 (medium confidence). The resulting protein-protein interaction network data were then imported into Cytoscape for visualization. By customizing node shapes and colors through the STYLE panel, a clearly visualized protein-protein interaction network diagram was generated.

Pathway enrichment analysis
The specific workflow for pathway enrichment analysis proceeded as follows. First, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was performed on the shared gene set using the clusterProfiler package in R software24. Statistical significance was assessed by the hypergeometric test, and p-values were adjusted for multiple testing using the Benjamini-Hochberg method to control the false discovery rate. Pathways with an adjusted p-value < 0.05 were considered significantly enriched and were retained for further analysis. Pathways related to specific human diseases or general metabolic processes were then manually excluded to focus the analysis on core signaling mechanisms. The filtered pathways and their associated genes were subsequently imported into Cytoscape software to construct an interaction network25. The "yFiles Organic Layout" algorithm was applied to generate a clear hierarchical structure. Visual attributes were further optimized in the STYLE panel: node color intensity was mapped to enrichment significance, node size was weighted by the number of core genes in each pathway, and edge thickness was weighted by the Jaccard similarity coefficient between pathways to reflect the degree of gene overlap. Ultimately, a KEGG pathway interaction network was obtained for comprehensive analysis.

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Results

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Annual number of publications
A search of the WOS database revealed that a total of 1,352 research papers on RA and muscle-related studies have been published in the past decade. After screening to exclude document types including meeting abstracts, conference papers, editorial materials, book chapters, and retracted publications, only Articles and Review Articles were retained, resulting in 1,274 publications. Further limitations on English-language publications ultimately yielded 1,249 documents, c...

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Discussion

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Bibliometric analysis reveals a distinct phased evolution of research focus in this field: while early studies primarily concentrated on foundational exploration of joint inflammation mechanisms, the focus has progressively shifted toward comprehensive research directions encompassing the establishment of clinical assessment systems, elucidation of molecular pathways, and development of targeted intervention strategies for systemic comorbidities26,27,<...

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Disclosures

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The authors have no conflicts of interest to declare.

Acknowledgements

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This study was supported by the National Natural Science Foundation of China (Grant No.81704050).

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
CiteSpaceDrexel Universityhttps://citespace.podia.comKeyword clustering, burst detection, and reference co-citation analysis
clusterProfilerR package / Bioconductorhttps://bioconductor.org/packages/clusterProfilerKEGG pathway enrichment analysis
CytoscapeNational Institute of General Medical Sciences (NIGMS)https://cytoscape.orgVisualization of PPI networks and KEGG pathway interaction networks
ExcelMicrosoft Corporationhttps://www.microsoft.com/excelData organization, statistical charts, and annual publication trend analysis
GeneCardsWeizmann Institute of Sciencehttps://www.genecards.orgRetrieval of target genes for rheumatoid arthritis and myositis
R SoftwareThe R Foundationhttps://www.r-project.orgStatistical computing, data analysis, and generation of enrichment plots
Scimago GraphicaScimago Labhttps://www.graphica.appVisualization and aesthetic optimization of country collaboration networks
STRINGSwiss Institute of Bioinformatics (SIB) et al.https://string-db.orgProtein-protein interaction (PPI) network construction
VOSviewerLeiden Universityhttps://www.vosviewer.comConstruction of collaboration networks (authors, institutions, countries) and journal co-citation analysis

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Bibliometric AnalysisMyositisTNF SignalingJAK STAT PathwayIL 17 PathwayProtein Interaction NetworkGene Expression

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