Abstract
BackgroundGene 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.MethodsIn 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.ResultsWe 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.ConclusionsThe results demonstrate that our method is very effective in excavating gene co-expression genes of multi-cancers.
Highlights
Gene co-expression network is a favorable method to reveal the nature of disease
The similarity measures based on gene expression profiles during the process of network construction are mainly obtained by calculating the linear or non-linear correlation coefficient of different gene expression profiles
Weighted Gene Co-expression Network Analysis (WGCNA) adds an index to the Pearson Correlation Coefficient (PCC) so that the distribution of correlation coefficient values gradually accords with the scale-free distribution
Summary
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. There are still a limited number of current node measurement methods and node mining strategies for multi-cancers network construction. The similarity measures based on gene expression profiles during the process of network construction are mainly obtained by calculating the linear or non-linear correlation coefficient of different gene expression profiles. Measurements of non-linear correlations include, for example, Mutual Information (MI) and so on. WGCNA is a method of mining module information from sequencing data. WGCNA is the current effective method with higher recognition. It is based on the module method, lack of directional mining for the node. Butte and Kohane firstly used MI as a measure of correlation between the genes and constructed an MI genes-related network [3]. There are no specific standards for the association measure, and some scholars have compared above two types of measures and found that each has its own advantages and disadvantages [5]
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