Studying The Connections Between Obesity And Related Diseases Based On Network Analysis

With the completion of genome sequencing and the development of next generation sequencing technology,human have mastered a large number of biological data,moreover,gene data and protein-protein interaction network data constantly updated and enriched.It is very important for deciphering obesity-related disease mechanism to analyze the relationship between obesity and disease and to identify the key genes connecting obesity and disease based on the data,it also has practical significance in genomics and medicine.As known to all of us,obesity is associated with a variety of diseases and is a primary risk factor for many diseases,such as Type II diabetes,coronary artery disease and cardiovascular disease.However,it is hard to understand that how obesity plays an important role in the development of obesity-related diseases.What's more,there is not a clearly global view of the connections between obesity and obesity-related diseases.In order to solve this problem,we build three different network analysis algorithms,first one is named OBNet based on a procedure similar to gene set enrichment analysis and a random walk with restart procedure,second one is OBsp which is performed by using the shortest path algorithm,the last one is OBoverlap which is based on directing overlap method.Then we compared the methods and found OBNet which is based on expanded modularized network is the best;using the best method,we further analyze the molecular connections between obesity and obesity-related diseases as well as their potential functional connections.It also could provide insight in clinical medicine.Based on obesity gene and disease gene data,this paper presents a new network analysis method to study the global relationship between obesity and disease.The main work of this paper is as follows:1)This paper presents three different network analysis algorithms to analyze the relationship between obesity and diseases from a global perspective.By comparing the results of three different algorithms,OBNet method based on expanded modularized network is the best in identifying the connections between obesity and diseases.Based on OBNet-based on expanded modularized network,we could find several diseases which are significant related to obesity,and could identify pathways or subnetworks in which obesity-related diseases are significant enrichment with obesity.Finally,we analyzed obesity and two other special diseases and found the key driving genes for mediating the relationship between these two diseases and obesity.2)Based on breast cancer data,WGCNA algorithm could identified 29 modules,from which we abstracted the top 10 modules significantly associated with breast cancer;we also identified the top 10 subnetworks significantly associated with breast cancer by our OBNet method based on expanded modular network;comparing the 10 subnetworks with the top 10 WGCNA-derived modules,we found their significant overlap.It means that,our OBNet approach could identify some significant driver genes and modules associated with diseases even if the gene expression profile data is not available.