Identifying functional evolution processes of cancer according to regression residuals network

The occurrence and development of cancer is a complex biological process, which usually requires further research by constructing a network. The existing methods of constructing networks are usually based on similarity to construct co-expression networks for analysis, but this similarity-based method usually ignores the data whose expression level increases or decreases at the same time in different groups. Gene pairs may also be related to the evolution of cancer, and that may cause part of the information to be lost. Therefore, we propose a new method of constructing a network based on regression residuals. Then, the module is identified based on the regression residual network according to different stages of cancer. A module network between cancer stages is obtained through the similarity between the modules. Finally, based on the size and density of the subnets in the modular network, enjoy the screening, and finally selected 3 subnets for functional analysis and Veen diagram analysis, and found some biological functions and genes that have been confirmed to be related to cancer.