Transcriptomics and AI-driven approaches to the diagnosis and treatment of rheumatoid arthritis

Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disorder marked by joint swelling, pain, and progressive tissue destruction. Increasing evidence suggests that dysregulated RNA expression critically drives RA progression by perturbing immune, inflammatory, and stromal cell programs.

These aberrant transcriptional signatures offer valuable biomarkers for diagnosis, prognosis, and therapeutic stratification. Recent advances in transcriptomic technologies have transformed our understanding of RA biology.

Bulk RNA profiling has highlighted key dysregulated pathways and disease-associated molecular signatures. Single-cell transcriptomics has expanded this insight by defining extensive cellular heterogeneity and uncovering rare immune and stromal populations implicated in disease initiation, progression, and treatment response.

The emergence of spatial transcriptomics provides an additional dimension by preserving tissue architecture, enabling precise localisation of pathogenic cell states and mapping cell–cell interactions within inflamed joints and other affected tissues. Integration of transcriptomic datasets with advanced computational and machine learning (ML) methods has accelerated biomarker discovery.

Techniques such as Random Forest, XGBoost, support vector machines (SVM), artificial neural networks (ANNs), and Least Absolute Shrinkage and Selection Operator (LASSO) regression facilitate feature selection and prediction from high-dimensional data. Complementary network- and pathway-based tools, including Weighted Gene Co-expression Network Analysis (WGCNA) and Gene Set Variation Analysis (GSVA), uncover co-regulated modules and refine clinically relevant signatures.

Collectively, this review aims to provide an update on how the integration of transcriptomics, spatial technologies, and advanced algorithms offers powerful opportunities to identify novel biomarkers and pathogenic cell populations, thereby advancing precision medicine in RA.