LAUSR

Objective: The objective of this paper is to identify ego networks and pathways in rheumatoid arthritis (RA) based on EgoNet algorithm and pathway enrichment analysis. Materials and Methods: The ego networks were identified based on the EgoNet algorithm which was comprised four steps: inputting gene expression data and protein-protein interaction data, identifying ego genes based on topological features of genes in background network, collecting ego networks by conducting snowball sampling for each ego gene, and estimating statistical significance of ego networks utilizing permutation test. To further explore the gene compositions of significant ego networks, pathway enrichment analysis was performed for each of them to investigate ego pathways in the progression of RA. Results: We detected 9 ego genes from the background network, such as CREBBP, SMAD2, and YY1. Starting with each ego gene and ending with prediction accuracy dropped, a total of 9 ego networks were identified. Statistical analysis identified two significant ego networks (ego-networks 2 and 4). Ego-network 2 with ego gene SNW1 and ego-network 4 whose ego gene was YY1 both included 10 genes. The results of pathway enrichment analysis showed that signaling by NOTCH (P = 1.11E-07) and oncogene-induced senescence (P = 3.48E-04) were the two ego pathways for RA. Conclusion: Ego networks and pathways identified in this work might be potential therapeutic markers for RA treatment and give a hand for further studies of this disease.