A network approach predicts NFKBIA and BIRC3 as pathogenic genes in childhood asthma.

This study aimed to predict pathogenic genes of childhood asthma based on molecular interaction networks and gene expression data. Known pathogenic genes identified from the human protein-protein interaction network were denoted as seed genes, and were included in network A. We extracted sub-network B (pathogenic network), which consisted of genes that interacted with at least two seed genes. We assigned a weight to select the pathogenic genes from this network according to its interactions and co-expressions with the seed genes. We also conducted ClusterONE analysis for the pathogenic network, and determined the statistical significance of the predicted clusters through a significance score (SS). Lastly, we investigated the biological pathways of the seed and candidate genes based on information obtained from the KEGG database. In network A, we identified 172 interactions and 125 genes that interacted with seed genes. In the pathogenic network, we found 51 genes and 102 interactions. The top 10 candidate genes with high weight scores were recorded. The SS of the predicted clusters demonstrated that 3 gene clusters were statistically relevant for childhood asthma. Pathway analysis showed that the seed genes and the top 10 candidate genes were significantly enriched in the same three biological processes. NFKBIA and BIRC3, which were involved in all three biological processes, may therefore be pathogenic genes. Using the network approach, we predicted that the pathogenic genes NFKBIA and BIRC3 are associated with childhood asthma. This information can provide guidelines for future experimental verification of childhood asthma.