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Integrative analysis of transcriptomic profiles reveals shared gene signatures of Rheumatoid Arthritis and Systemic Lupus Erythromatosus


 Background

Autoimmune diseases develop when a person’s immune system starts developing immune response against its own healthy cells, tissues, or any other cell constituents. Rheumatoid Arthritis (RA) and Systemic Lupus Erythromatosus (SLE) are the two most common systemic inflammatory autoimmune diseases, sharing various clinical as well as pathological signatures. Although multiple studies have been conducted to date, very little is known about molecular pathogenesis and overlapping molecular signatures of the two diseases. Motivated to explore the common molecular disease features, we conducted a meta-analysis of the publicly available microarray gene expression datasets of RA and SLE.
Methods

Common and unique gene signatures of RA and SLE were identified based on analysis of microarray gene-expression datasets. Hub genes were identified by performing network analysis of protein-protein interaction (PPI) networks of the identified genes. Gene ontology functional enrichment and integrative pathway analysis was also performed to understand the underlying molecular mechanisms in the diseases.
Results

Intriguingly, out of the identified signature genes, 9 are upregulated and 24 are downregulated. Many of the common gene signatures identified in this study provide clues to the shared pathological mechanisms of RA and SLE. Amongst the identified signatures, MMP8, NFIL3, B4GALT5, HIST1H1C, NMT2, PTGDS and DUSP14, are the robust gene signatures shared by all the RA and SLE datasets. Functional analysis revealed that the common signatures are involved in the pathways such as mTOR signaling pathways, virus infection-related pathways, bone remodeling, activation of matrix metalloproteinase pathway, immune and inflammatory response-related pathways.
Conclusions

The common gene signatures and related pathways identified in this study substantiate the shared pathological mechanism involved in both diseases. Furthermore, our analysis of multi-cohort and multiple microarray datasets allow discovery of novel leads for clinical diagnosis and potential novel drug targets.