Identification of Novel Pathways in Rheumatoid Arthritis Through Gene Expression Signatures and Gene Ontology Enrichment

• Israa M Shamkh

  Chief Computational Chemistry Department EBO Bio Solution Company, London, EC1V2NX, United Kingdom

  Author

• Ahmed Hassen Shntaif

  Assist Professor of Chemistry Department of Chemistry, University of Babylon, Alhilla, Iraq

  Author

• Ihosvany Camps

  Full Professor. Department of Physics, Federal University of Alfenas, Alfenas, MG, Brazil

  Author

Inflammation of the synovial joints is an indication of rheumatoid arthritis (RA), a chronic inflammatory illness. The pathogenesis of RA is known to be mostly influenced by pro-inflammatory cytokines like TNF-α and IL-1β, but the molecular processes behind the disease's development are still not fully understood. In this work, we used publically accessible RNA-sequencing datasets from The Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) project to find new pathways and genes with differential expression associated with RA. Over 500 genes with a factor change criterion of 2 and p-value <0.05 were found to be differentially expressed between RA and control samples using bioinformatic analysis of synovial tissue samples from 20 RA patients and 20 healthy controls included in the GTEx and TCGA datasets. In RA samples, a number of well-known RA hub genes, including TNF, IL1B, IL6, and NFKB1, showed substantial overexpression. Enhancement of immune response, cytokine signalling, cell adhesion, and chemokine activity pathways was found by gene ontology analysis of elevated genes. Our investigation yielded the novel hub genes CCL2, CXCL13, and MMP9. Our discovery of differentially expressed genes and signalling pathways not previously linked to the etiology of the illness offers fresh insights into the molecular landscape of RA. The discovery of new hub genes involved in RA advances our knowledge of the disease's pathophysiology and might lead to the discovery of fresh treatment options. Improved RA diagnosis and individualized treatment plans may result from more validation of these putative genes and pathways.

• PDF

• Computational Science Section

This work is licensed under a Creative Commons Attribution 4.0 International License.