What question did this study set out to answer?

To identify key pathogenic pathways, immune infiltration profiles, and diagnostic biomarkers in rheumatoid arthritis synovial tissue by integrating public transcriptomic datasets.

August 23, 2025Journal of Orthopaedics

Identification of key pathways and biomarkers in rheumatoid arthritis synovial tissue through comprehensive transcriptomic integration

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Authors

QCQifan Chen CZChusong Zhou HWHongyan Wu

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Overview

Transcriptomic analysis uncovers core pathways and nine biomarkers in rheumatoid arthritis synovial tissue, highlighting plasma cell infiltration and IGHG1 as key disease drivers.

Key Points

To identify key pathogenic pathways, immune infiltration profiles, and diagnostic biomarkers in rheumatoid arthritis synovial tissue by integrating public transcriptomic datasets.
Integrated microarray and RNA-seq datasets (GSE1919, GSE12021, GSE55235, GSE55457, GSE77298, and GSE89408) from the GEO database.
Performed differential gene expression analysis, GO/KEGG functional enrichment, gene set enrichment analysis (GSEA), and CIBERSORT immune cell infiltration profiling.
Applied an ensemble of 113 machine learning algorithms to discover and validate diagnostic synovial gene signatures.
Identified 9,204 differentially expressed genes in the GSE89408 training set enriched in cytokine-cytokine receptor interaction and chemokine signaling (NES >1, FDR <0.001).
Observed a significant expansion of plasma cells in rheumatoid arthritis synovium, whereas control synovium completely lacked plasma cell infiltration.
Established a 9-gene diagnostic model (AIM2, AKR1B10, CXCL10, CXCL13, IGLC1, IL2RG, LRRC15, SDC1, and IGHG1) demonstrating robust cross-dataset diagnostic performance.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/6a9dcbb79f7050eb61e72d83 https://doi.org/10.1016/j.jor.2025.08.049

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August 23, 2025Journal of Orthopaedics

Identification of key pathways and biomarkers in rheumatoid arthritis synovial tissue through comprehensive transcriptomic integration

View Full Paper

Ask AI

Bookmark

Authors

QCQifan Chen CZChusong Zhou HWHongyan Wu

Discussion

Member takes

Overview

Transcriptomic analysis uncovers core pathways and nine biomarkers in rheumatoid arthritis synovial tissue, highlighting plasma cell infiltration and IGHG1 as key disease drivers.

Key Points

To identify key pathogenic pathways, immune infiltration profiles, and diagnostic biomarkers in rheumatoid arthritis synovial tissue by integrating public transcriptomic datasets.
Integrated microarray and RNA-seq datasets (GSE1919, GSE12021, GSE55235, GSE55457, GSE77298, and GSE89408) from the GEO database.
Performed differential gene expression analysis, GO/KEGG functional enrichment, gene set enrichment analysis (GSEA), and CIBERSORT immune cell infiltration profiling.
Applied an ensemble of 113 machine learning algorithms to discover and validate diagnostic synovial gene signatures.
Identified 9,204 differentially expressed genes in the GSE89408 training set enriched in cytokine-cytokine receptor interaction and chemokine signaling (NES >1, FDR <0.001).
Observed a significant expansion of plasma cells in rheumatoid arthritis synovium, whereas control synovium completely lacked plasma cell infiltration.
Established a 9-gene diagnostic model (AIM2, AKR1B10, CXCL10, CXCL13, IGLC1, IL2RG, LRRC15, SDC1, and IGHG1) demonstrating robust cross-dataset diagnostic performance.

Cite This Study

Chen et al. (2025) studied this question.

synapsesocial.com/papers/6a9dcbb79f7050eb61e72d83 https://doi.org/10.1016/j.jor.2025.08.049

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