Evidence map›Paper›PMID 41066424›Full record

ArticlePloS one2025

Identification of shared key genes in Rheumatoid Arthritis and COVID-19 and their relevance as diagnostic biomarkers and with immune infiltration: New insights from bioinformatics analysis.

Wei Ya Lan, Shan Shan Cai, QianWei Lu, Fang Tang

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Wei Ya LanGraduate School, Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China.ORCID https://orcid.org/0009-0005-2700-6423
Shan Shan CaiGraduate School, Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China.
QianWei LuDepartment of Joint and Orthopedic Surgery, Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China.ORCID https://orcid.org/0009-0003-4705-7020
Fang TangDepartment of Rheumatology and Immunology, Second Affiliated Hospital of Guizhou University of Traditional Chinese Medicine, Guiyang, Guizhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The interaction mechanism between Coronavirus Disease (COVID-19) and rheumatoid arthritis (RA) remains inadequately understood. Consequently, this study sought to elucidate the potential mechanisms underlying the comorbidity between RA and COVID-19, as well as to identify key genes, diagnostic markers, and associated immune cells. Differential analysis of the training set, derived from the GEO database, identified differentially expressed genes (DEGs) in the RA and COVID-19 gene chip and sequencing datasets. Weighted Gene Co-expression Network Analysis (WGCNA) identified key modular genes, while protein-protein interaction (PPI) network analysis revealed hub genes, which were validated by the validation set. Receiver Operating Characteristic (ROC) curves were used to assess clinical relevance. Cytoscape-based transcription factor (TF)-mRNA and microRNA (miRNA)-mRNA regulatory networks were used to identify potential therapeutic targets, and immune cell infiltration was evaluated using the CIBERSORT algorithm. Differential expression analysis identified 2,778 DEGs in RA and 12,733 in COVID-19, with WGCNA identifying 18 shared genes, suggesting possible common molecular mechanisms. Validation analysis confirmed LGMN and NRGN as key genes associated with RA and COVID-19 comorbidity, highlighting their diagnostic significance. Network analysis identified related miRNAs and TFs, and enrichment analysis revealed the critical signaling pathways. Immune cell infiltration in patients with RA and COVID-19 was assessed using the CIBERSORT algorithm. This study preliminarily explored the shared pathogenic mechanisms between RA and COVID-19, identifying LGMN and NRGN as potential biomarkers for both diseases. Notably, NRGN may play a significant role as a common biomarker involved in the immune response in both disease states. These findings may open new avenues for the diagnosis and treatment of RA and COVID-19.

Indexed as

Arthritis, RheumatoidCOVID-19BiomarkersComputational BiologyGene Expression ProfilingGene Regulatory NetworksHumansMicroRNAsProtein Interaction MapsSARS-CoV-2BiomarkersMicroRNAs

Identifiers

PMID41066424
PMCPMC12510541

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.