Deciphering the molecular landscape of rheumatoid arthritis offers new insights into the stratified treatment for the condition

TL;DR

This study identifies three distinct molecular subtypes of rheumatoid arthritis, offering new opportunities for personalized treatment strategies.

Contribution

A novel subtyping scheme for RA based on gene expression profiles and validated with machine learning is introduced.

Findings

Three RA subtypes were identified: NE-driving, IFN-driving, and CD8+ T-cells-driving.

Each subtype is characterized by distinct immune pathways and cell types.

The subtyping scheme was validated using XGBoost and shows potential for stratified therapy.

Abstract

For Rheumatoid Arthritis (RA), a long-term chronic illness, it is essential to identify and describe patient subtypes with comparable goal status and molecular biomarkers. This study aims to develop and validate a new subtyping scheme that integrates genome-scale transcriptomic profiles of RA peripheral blood genes, providing a fresh perspective for stratified treatments.

We utilized independent microarray datasets of RA peripheral blood mononuclear cells (PBMCs). Up-regulated differentially expressed genes (DEGs) were subjected to functional enrichment analysis. Unsupervised cluster analysis was then employed to identify RA peripheral blood gene expression-driven subtypes. We defined three distinct clustering subtypes based on the identified 404 up-regulated DEGs.

Subtype A, named NE-driving, was enriched in pathways related to neutrophil activation and responses to bacteria. Subtype B, termed interferon-driving (IFN-driving), exhibited abundant B cells and showed increased expression of transcripts involved in IFN signaling and defense responses to viruses. In Subtype C, an enrichment of CD8+ T-cells was found, ultimately defining it as CD8+ T-cells-driving. The RA subtyping scheme was validated using the XGBoost machine learning algorithm. We also evaluated the therapeutic outcomes of biological disease-modifying anti-rheumatic drugs.

The findings provide valuable insights for deep stratification, enabling the design of molecular diagnosis and serving as a reference for stratified therapy in RA patients in the future.

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