A new method for mining information of co-expression network based on multi-cancers integrated data

Abstract

Abstract Background Gene co-expression network is a favorable method to reveal the nature of disease. With the development of cancer, the way to build gene co-expression networks based on cancer data has been become a hot spot. However, there are still a limited number of current node measurement methods and node mining strategies for multi-cancers network construction. Methods In this paper, we introduce a new method for mining information of co-expression network based on multi-cancers integrated data, named PMN. We construct the network by combining the different types of relevant measures (linear and nonlinear rules) for different nodes based on integrated gene expression data of multi-cancers from The Cancer Genome Atlas (TCGA). For mining genes, we combine different properties (local and global characteristics) of the nodes. Results We uncover more suspicious abnormally expressed genes and shared pathways of different cancers. And we have also found some proven genes and pathways; of course, there are some suspicious factors and molecules that need clinical validation. Conclusions The results demonstrate that our method is very effective in excavating gene co-expression genes of multi-cancers.

Keywords

• Gene co-expression network
• TCGA
• Abnormally expressed genes
• Mutual information
• Pearson correlation coefficient