Background/Purpose:   Genome-wide analysis of RA has independently evaluated DNA sequence variation, differential RNA expression, and differential DNA methylation. Each approach alone implicates promising pathways and networks in the pathogenesis of disease. Prioritizing high value pathways from a large set of candidates can be difficult. To address this limitation, we performed an integrative analysis of genome wide association study (GWAS) as well as DNA methylation and RNA transcriptomics for cultured fibroblast-like synoviocytes (FLS). We then identified genes that found in at least two of these sets and evaluated whether they are enriched in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways.   

Methods: Data were from three types of genome-wide RA assays: (i) for sequence variation we used the NCBI GWAS database and extracted all gene that mapped to SNPs that had been implicated in RA susceptibility (www.genome.gov/gwasstudies/); (ii) for gene expression we used public microarray datasets of RA, OA, and normal (NL) FLS lines (Gene Expression Omnibus Database (GSE 29746)); (iii) for DNA methylation we used a set of differentially methylated genes that we previously identified in 11 RA, 11 OA, and 6 NL FLS (Genome Medicine 5:40, 2013). We then overlapped the gene sets and identified those with two or three forms of evidence, termed the multi-evidence genes (MEGs). The significance of MEGs in KEGG human pathways was determined and resulting p-values represented the fraction of randomly selected background gene sets that were at least as enriched in genes found in the tested pathway as the DMG set. A q-value was calculated to correct for multiple comparisons and a threshold of 0.05 determined significance.

Results:   357 MEGs in RA FLS were identified for KEGG analysis. 14 out of 271 KEGG pathways were significantly enriched with MEGs. Perhaps most interesting, evaluation of MEGs identified a 3.88-fold enriched in the KEGG “rheumatoid arthritis” pathway (q = 1.4e-02) with 7 out 89 genes: ANGPT1, CSF2, CTLA4, HLA-DQA1, HLA-DQA2, HLA-DRA and HLA-DRB1. Involvement of this pathway strongly suggests that the MEGs are highly relevant to RA. At least four additional immunological pathways relevant to RA were also significantly enriched in the MEGs set. For example, the KEGG ‘Cell adhesion molecules’ pathway is 4.59-fold enriched (q = 2.00E-04) with 12 out of 129 genes in the MEGs set. The KEGG ‘Cytokine-cytokine receptor interaction’ pathway is 2.98-fold enriched (q =1.63e-03) with 15 out of 248 genes labeled in the MEGs set. The KEGG ‘Antigen processing and presentation’ pathway is 4.42-fold enriched (q =1.53e-02) with 6 out of 67 genes labeled as DMGs in the MEGs set. The KEGG ‘Jak-STAT signaling pathway’ pathway is 2.84-fold enriched (q =4.10e-02) with 8 out of 139 genes labeled in the MEGs set.

Conclusion:   Pathway analysis demonstrates non-random identification of RA related genes through the integrative analysis of multiple genome-wide datasets. The identified genes and pathways, such as those involved with cytokines and Jak-STAT, are likely dysregulated in the disease and reflect the pathogenesis of disease. Therefore, genes identified in the pathways identified through integrative analysis could be novel targets for treatment of RA.

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ACR Meeting Abstracts - https://acrabstracts.org/abstract/key-rheumatoid-arthritis-associated-pathogenic-pathways-revealed-by-integrative-analysis-of-ra-omics-datasets/