# 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.

## Key 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.