Identify Disease-related Gene Function Modules Based On Protein Interaction Network

Studies have shown that complex diseases are rarely caused by mutations in individual gene products.The pathogenesis is often due to mutations in genes with important functions.These mutations extend to the entire network through molecular regulatory network,resulting in complex and diverse disease phenotype.Based on gene expression data and protein interaction network(PPIN)data,this paper identifies gene function modules related to lung carcinoma and breast cancer,aiming to explore the molecular mechanisms of complex disease pathogenesis.We firstly analyzes the differential expression of disease gene expression data and integrates it with PPIN data,further uses the Heinz search algorithm to identify disease-related gene function modules,and performs function(GO term)enrichment analysis and pathway(KEGG)enrichment analysis on the genes in the module.Disease-related interaction sub-networks were obtained from protein interaction network analysis.The disease hub nodes(hubs)genes were obtained according to the protein interaction sub-network,and the biological processes involved in the genes in the subnetwork were obtained by enrichment analysis.And we also obtained the biological pathways which play important roles in the occurrence and development of disease candidate gene markers.