Identification of common genes and biomarkers between Dermatomyositis and rheumatoid arthritis through integrated bioinformatics

TL;DR

This study identifies shared genes and immune features between dermatomyositis and rheumatoid arthritis, offering potential biomarkers and insights into their comorbidity.

Contribution

The study introduces an integrated bioinformatics and machine-learning approach to uncover shared molecular mechanisms and biomarkers between dermatomyositis and rheumatoid arthritis.

Findings

Four core genes (JUNB, NRGN, HCP5, RARRES3) were identified as potential biomarkers with diagnostic performance in external datasets.

Shared pathways include IL-17, Toll-like receptor, and chemokine signaling, with immune microenvironment changes involving macrophage polarization and T-cell subpopulations.

Single-cell analysis localized core gene expression to immune cell types and highlighted inflammatory ligand-receptor interactions.

Abstract

Dermatomyositis (DM) and rheumatoid arthritis (RA) share immuno-inflammatory features, yet mechanisms underlying their comorbidity remain unclear. We aimed to define shared molecular mechanisms across gene regulatory networks and the immune microenvironment using integrated multi-omics and machine-learning analyses.

Microarray datasets for RA (GSE55235, GSE55457, GSE12021) and DM were retrieved from GEO. RA datasets were merged and batch-corrected with ComBat. Differentially expressed genes (DEGs) were identified using limma; key modules were derived by weighted gene co-expression network analysis (WGCNA). Intersected DEGs–module genes underwent GO/KEGG enrichment. Core genes were prioritised by LASSO regression and random-forest modelling and evaluated in external cohorts. Immune landscape was estimated with CIBERSORT and immune subpopulations profiled by single-sample GSEA. Single-cell RNA-seq (GSE159117) mapped cell-type–specific expression of core genes and inferred ligand–receptor networks.

We identified 780 DEGs in RA and 739 in DM. Intersecting DEGs with WGCNA modules yielded 47 candidates enriched for IL-17, Toll-like receptor and chemokine signalling (all P < 0.05). Four core genes (JUNB, NRGN, HCP5, RARRES3) were prioritised; HCP5 and RARRES3 showed significant differential expression and diagnostic performance in external datasets (AUC 0.634–0.846). CIBERSORT indicated enrichment of activated CD4+ memory T cells and a shift in macrophage polarisation with increased M2 signatures in both diseases. Core genes were dynamically associated with M1/M2 polarisation and T-cell subpopulations (P < 0.05). Single-cell analysis localised core gene expression to NK cells, monocytes and T/B cells, and highlighted inflammatory ligand–receptor interactions.

Integrative, ML-assisted transcriptomics reveals convergent RA–DM programmes centred on IL-17, TLR and chemokine pathways with remodelling of the immune microenvironment. HCP5 and RARRES3 emerge as reproducible, externally supported candidates with diagnostic potential and plausible links to macrophage polarisation and T-cell states. These findings nominate testable biomarkers and pathways for validation and provide a rationale for pathway-guided, cross-disease studies of RA–DM comorbidity.

Figures