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Open Access Highly Accessed Methodology article

Network-based differential gene expression analysis suggests cell cycle related genes regulated by E2F1 underlie the molecular difference between smoker and non-smoker lung adenocarcinoma

Chao Wu1, Jun Zhu2* and Xuegong Zhang1

Author Affiliations

1 MOE Key Laboratory of Bioinformatics and Bioinformatics Division, TNLIST and Department of Automation, Tsinghua University, Beijing 100084, PR China

2 Department of Genetics and Genomic Sciences, Icahn Institute of Genomics and Multiscale Biology, Mount Sinai School of Medicine, New York, NY, USA

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BMC Bioinformatics 2013, 14:365  doi:10.1186/1471-2105-14-365

Published: 17 December 2013

Abstract

Background

Differential gene expression (DGE) analysis is commonly used to reveal the deregulated molecular mechanisms of complex diseases. However, traditional DGE analysis (e.g., the t test or the rank sum test) tests each gene independently without considering interactions between them. Top-ranked differentially regulated genes prioritized by the analysis may not directly relate to the coherent molecular changes underlying complex diseases. Joint analyses of co-expression and DGE have been applied to reveal the deregulated molecular modules underlying complex diseases. Most of these methods consist of separate steps: first to identify gene-gene relationships under the studied phenotype then to integrate them with gene expression changes for prioritizing signature genes, or vice versa. It is warrant a method that can simultaneously consider gene-gene co-expression strength and corresponding expression level changes so that both types of information can be leveraged optimally.

Results

In this paper, we develop a gene module based method for differential gene expression analysis, named network-based differential gene expression (nDGE) analysis, a one-step integrative process for prioritizing deregulated genes and grouping them into gene modules. We demonstrate that nDGE outperforms existing methods in prioritizing deregulated genes and discovering deregulated gene modules using simulated data sets. When tested on a series of smoker and non-smoker lung adenocarcinoma data sets, we show that top differentially regulated genes identified by the rank sum test in different sets are not consistent while top ranked genes defined by nDGE in different data sets significantly overlap. nDGE results suggest that a differentially regulated gene module, which is enriched for cell cycle related genes and E2F1 targeted genes, plays a role in the molecular differences between smoker and non-smoker lung adenocarcinoma.

Conclusions

In this paper, we develop nDGE to prioritize deregulated genes and group them into gene modules by simultaneously considering gene expression level changes and gene-gene co-regulations. When applied to both simulated and empirical data, nDGE outperforms the traditional DGE method. More specifically, when applied to smoker and non-smoker lung cancer sets, nDGE results illustrate the molecular differences between smoker and non-smoker lung cancer.