- Methodology article
- Open Access
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
© Wu et al.; licensee BioMed Central Ltd. 2013
- Received: 16 July 2013
- Accepted: 12 December 2013
- Published: 17 December 2013
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.
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.
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.
- Gene Module
- E2F1 Target Gene
- Smoker Sample
- Cell Cycle Related Gene
- Regulate Gene Module
High throughput technologies enable people to monitor the transcriptome of complex diseases. It’s a great opportunity as well as a big challenge for us to reveal the deregulated molecular mechanisms of complex diseases from transcriptomic data. Over the decade, differential gene expression (DGE) analysis has been widely used to discover differentially regulated genes and deregulated molecular mechanisms . However, changes in multiple genes coupled with interactions among themselves and between them and other genes interfere normal biological functions of cell and cause diseases . Traditional DGE analysis such as the t test or the rank sum test doesn’t always perform well on identifying deregulated genes and deregulated molecular mechanisms because it processes each gene independently without considering gene-gene relationships [3, 4]. Genes most significantly differentially regulated might not directly relate to diseases. More suitable tools need to be developed for identifying the deregulated molecular mechanisms from transcriptomic data.
Functional genomics studies reveal that genes and their products are well governed in cell: they are elaborately assembled and disassembled by regulatory forces beyond genetic code . Revealing gene-gene relationships among differentially regulated genes and identifying causal relationships or gene modules in them will lead to a better understanding of deregulated molecular mechanisms and discovery of potential causal factors. Many efforts have been devoted to integrate gene-gene relationships and gene expression level changes to prioritize signature genes, such as some gene prioritization methods and gene module based methods [3, 6-13]. However, most of the methods involve multiple separate steps in defining gene expression changes and gene-gene relationships related to disease without maximally leveraging all information available simultaneously.
In this work, we extend our previously developed Networked Gene Prioritizer (NGP) method  and develop a gene module based method for differential gene expression analysis, named network-based differential gene expression (nDGE) analysis to prioritize deregulated genes and group them into gene modules. NGP leverages a protein-protein interaction network and differential expressed genes in a network neighborhood to prioritize genes. Improvement of nDGE comparing to NGP and other existing methods is that it uses a one-step integrative process to simultaneously define gene-gene relationships and gene expression level changes associated with diseases while most existing methods involve two separated steps to define them. The resulted advantage is that no hard cutoff parameters are needed in nDGE to determine neither gene-gene relationships nor gene expression changes associated with disease while hard cutoff parameters are needed in most existing methods and might lead the methods sensitive to the selection of parameters. No hard cutoff parameters are needed in NGP, either. However, NGP’s ability to prioritize all the genes on chips is limited because it relies on protein-protein interaction network which only covers a fraction of whole proteome. In addition, NGP might not be able to accurately prioritize some genes because of limitations of its underlie assumption that a physical interaction implies a co-expression relationship.
We first compare nDGE with a traditional DGE, NGP and two gene module based methods using simulated data sets. The DGE is carried out by the rank sum test. One version of gene module based methods in our comparison is to construct co-expression network using only differentially expressed genes and detecting modules within it. The other version is to apply co-expression analysis using all genes in data sets, then to extract co-expression gene modules and apply gene set enrichment analysis to identify the differentially expressed gene modules. We demonstrate that nDGE outperforms the compared methods on accuracy of prioritizing deregulated genes and identifying deregulated gene modules with a large range of parameter of co-expression measurement.
Then, we apply nDGE to a series of smoker and non-smoker lung adenocarcinoma data sets to explore the molecular mechanisms and regulators that drive differences between smoker and non-smoker lung adenocarcinoma. Lung cancer is the most common cancer in terms of both incidence and mortality. In 2008, there were 1.61 million new cases, and 1.38 million deaths due to lung cancer . Tobacco smoke is the most common cause of lung cancer. But non-smokers account for 10-15% of lung cancer cases . This percentage is even higher in Asian women . Although studies have suggested that lung cancers arising in non-smokers have a distinct natural history, profile of oncogenic mutations, and response to targeted therapy comparing to smokers , lung cancer in smokers and non-smokers is treated similarly to date. Identifying the molecular mechanisms and capturing the regulatory factors that explain the differences between smoker and non-smoker lung cancer can extend our understanding of smoking and non-smoking related lung cancer and will provide benefits for the treatment of lung cancer. We apply nDGE and the rank sum test on multiple smoker and non-smoker lung cancer data sets. 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. A differentially regulated gene module identified by nDGE, which is enriched for cell cycle genes and E2F1 targeted genes, plays a role attributing to the differences between smoker and non-smoker lung adenocarcinoma. Existing data support that E2F1 regulates cell cycle genes that lead to the molecular differences associated with different response to chemotherapies between smoker and non-smoker lung adenocarcinoma. In conclusion, our nDGE results provide a better understanding of smoker and non-smoker lung cancers which can lead to better early lung cancer detection and personalized treatment of smoker and non-smoker lung cancer.
Information about the empirical data used in this work
Agilent Homo sapiens 21.6 K custom array
Illumina HumanHT-12 V3.0
Illumina HumanWG-6 v3.0
Methodology of nDGE
Step 1- For each candidate probe set, all probe sets on the chips are sorted by their PCCs with the candidate probe set. We identify its co-regulated probe sets by their values of PCC. We assume that the probe sets are co-regulated if they are highly co-expressed in a subtype of samples (at PCC > 0.707).
Step 2 - We calculate probe sets’ differential expression between the compared samples as Z score of the rank sum test statistic assuming the rank sum test statistic is normally distributed. We calculate activity score (AS) of candidate probe set as following: at first, we walk in the co-regulated probe sets (the probe sets before the green line in Figure 2) step by step; in each step we generate two gene sets-A i and B i (the colored bar in Figure 2), A i contains the top-i probe sets, B i contains the other probe sets; next, we test whether the differential expression of genes in A i and B i is different by the rank sum test. AS of the candidate probe set is formulated as the product of the Z score of rank sum test statistic and a correction factor:
Step 3 - For each probe set, their co-regulated neighbors that contribute to AS are defined as its differentially expressed neighbors (DE neighbors).
Statistical significance of AS is estimated by permutation tests. We keep the size of co-regulated neighbors of a candidate probe set the same but randomly select its neighbors from probes on the chip. If the true AS of a candidate probe set is negative, then we calculate the “negative AS” which is the minimum of , where , A i is the aforementioned neighbors set, Z j is the Z score of probe sets’ differential expression by the rank sum test, , , , N is the number of probe sets on the chip, if parameter a = 0, else a =1.
If AS of a candidate probe set is positive, then we calculate the “positive AS” which is the maximum of .
We repeat the aforementioned process 100,000 times to generate negative (or positive) AS background distributions. At last, p value is estimated as the frequency of AS background smaller (or larger) than the true AS.
Two prioritization lists are returned in nDGE, one is for treatment samples and the other is for control samples. Different co-expression and differential gene expression patterns might exist in the different types of samples which suggest different regulatory programs might be deregulated in the different subtypes of disease. For this reason, we independently prioritize genes in treatment and control samples and draw conclusions in each subtype of samples.
Identification of differentially regulated gene modules
nDGE prioritizes deregulated genes and groups them into gene modules. At first, deregulated genes are extracted according to their p value; then, DE neighbor relationships between these genes are extracted; next, interactions indicating genes are DE neighbors of each other are retained to construct a gene-gene network; at last, if the network is densely connected we employ a spectrum clustering algorithm  followed by a coherence-based module detection algorithm  to identify gene modules, else we define gene modules as connected components in sparse network.
Comparison of nDGE with two gene module based methods
A straightforward approach to identify differentially regulated gene modules is to determine co-expression gene modules first and then to inspect differential expression of genes in the modules or vice versa. We name the approach as two-steps approach. A problem of two-steps approach is that gene modules are determined by the parameter of co-expression measurement without leveraging the information of differential gene expression. The selection of the parameter for co-expression modules will affect the differentially regulated gene module results. Here we develop nDGE which simultaneously considers gene-gene co-expression and differential gene expression to identify differentially regulated gene modules. Thus, the variation of co-expression parameter has few impacts on the results.
We compare nDGE with two two-steps methods. One approach, noted as method 1, is to construct co-expression network of differentially expressed genes and detecting modules within it. An alternative approach is first to apply co-expression analysis on all the genes to identify co-expression gene modules then to apply GSEA  to identify modules enriched for differentially expressed genes. We apply the leading edge analysis (LEA) to GSEA result to further refine the identified gene modules. We note the method as method 2.
We compare nDGE with method 1 and 2 on simulated data sets 4-6. Performances of the methods on revealing deregulated gene modules are measured by whether they can identify the candidate genes and their differentially expressed neighbors in the simulated data sets. In each data set, the number of true co-regulated neighbors is 50. The “detected” co-regulated neighbors are set as 50, 100, 150, 200, 250, 300, 350, 400, 450 and 500. In data set 4, any neighbors identified by the methods are regarded as false positives. In data sets 5 and 6, the overlapped genes and unique genes of the detected neighbors by the methods with the truly differentially expressed neighbors are counted and their ratio is defined as a true positive rate.
No hard cutoff parameter is needed in nDGE and method 2 to determine differential gene expression while a cutoff parameter is needed in method 1. In order to fairly compare the three methods on the variation of co-expression parameter, we set top 1-100 genes as the differentially expressed genes in method 1, which is close to the true differentially expressed neighbors in data sets 5 and 6. In method 2, the p parameter of GSEA is set 0 and co-regulated neighbors are identified by LEA analysis of GSEA.
Function and regulator analyses for differentially regulated gene modules
Functional annotation analysis is carried out to investigate the potential biological functions of modules identified by nDGE. The tool we used is “Functional Annotation Clustering” analysis of DAVID , the parameter is default except enrichment thresholds is set as 0.0001 and Bonferroni corrected p < 0.01. Only Gene Ontology terms are showed in the results. Regulator analysis is implemented to discover potential regulators of modules. The tool we used is “Transcription Factor Target Analysis” of WebGestalt (WEB-based GEne SeT AnaLysis Toolkit)  and the parameter is set as Bonferroni corrected p < 0.01.
We first compare nDGE with traditional DGE which is carried out by the rank sum test, NGP and two gene module based methods using simulated data sets. nDGE outperforms NGP on accurately prioritizing deregulated genes (see Additional file 1 for the detail). Here we focus on comparing nDGE with other existing methods. Then we apply nDGE to a series of smoker and non-smoker lung adenocarcinoma data sets to reveal the molecular differences between smoker and non-smoker lung cancer.
nDGE outperforms the traditional DGE on gene prioritization
Differential gene expression (DGE) analysis is widely used to reveal the deregulated molecular mechanisms underlying complex diseases from transcriptomic data. However, traditional DGE analysis (e.g., the t test or the rank sum test) tests each gene independently without considering interactions between them. Developing a method that performs better than currently available DGE methods is one of motivations of nDGE. nDGE and the rank sum test are applied on simulated data set 3. Z score of the rank sum test statistic is taken as the measurement of differential gene expression. The Spearman Correlation Coefficient (SCC) between Z score and AS of candidate genes without co-expressed neighbors is calculated. The SCC is always 1 in 100 simulation experiments. Thus, nDGE returns the same prioritization list as the rank sum test when candidate genes have no co-expressed neighbors. The ranks of candidate genes that have differentially expressed co-regulated neighbors are counted. The 50 candidate genes that are co-expressed and differentially expressed are always ranked at 1th to 50th in the prioritization list in 100 simulation experiments. The candidate genes whose neighbors are differentially expressed are top-ranked in nDGE. Comparing to the rank sum test analysis, nDGE sets a higher rank for genes that are co-expressed and differentially expressed than genes that are only differentially expressed. We think this feature enables nDGE to more accurately capture coherently deregulated genes because co-expression relationships among genes reflect co-regulation relationships among them [12, 23]. Top-prioritized genes by nDGE are likely to involve in deregulated regulatory programs of the studied disease.
nDGE outperforms two gene module based methods on discovering deregulated gene modules
Differentially regulated gene modules in transcriptome level may shed light on the dysregulated molecular mechanisms underlying complex diseases. A straightforward approach to identify these gene modules is to determine co-expression gene modules first and then to inspect differential expression of genes in the modules or vice versa. For example, in GSEA software , a set of co-expression gene modules centered on cancer-related genes have been defined in MigSDB and used to test whether they are differentially expressed in query data sets. In WGCNA software , gene significance measurement is based on biological significance of the identified co-expression gene modules such as the correlation with clinical traits. The problem of two-steps approach is that the information of gene-gene co-expression and differential gene expression is not maximally leveraged. Thus the variation of the parameter of co-expression measurement will influence the differentially regulated gene module results. We compare nDGE with two simple and straightforward two-steps methods which are named methods 1 and 2 (see “Methods” for the details).
In all tests, nDGE has high sensitivity in revealing deregulated co-regulated genes and high specificity without detecting many false positives across a large range of co-expression parameters. Altogether, simulation results suggest that nDGE robustly performs better than currently available methods in term of detecting coherently differentially regulated genes.
Molecular differences between smoker and non-smoker lung adenocarcinoma
Lung cancers arising in non-smokers and smokers are different diseases. But they are treated similarly to date. Discovering the molecular mechanisms that lead to the differences between smoker and non-smoker lung cancer will extend our understanding of lung cancer and provide benefits for risk evaluation for early lung cancer detection and personalized treatment of different lung cancers. nDGE is applied to multiple smoker and non-smoker lung adenocarcinoma data sets (listed in Table 1). It is applied to non-smoker samples in Smoker2, Smoker5 and Smoker6 data sets because sizes of non-smoker samples in the other data sets are small, limiting the power of nDGE to infer reliable co-expression relationships. We make notation conventions on some conceptual designations: probe sets whose DE neighbors are of higher expression levels in smoker samples are regarded as “upregulated”; probe sets whose DE neighbors have lower expression levels in smoker samples are noted as “downregulated”. We assign genes’ AS by AS of their probe sets that have the largest absolute AS. The rank sum test is also applied in the data sets and its prioritization results are taken as reference for comparison. Z score of the rank sum test statistic is taken as the statistic. Genes with higher expression levels in smoker samples are regarded as upregulated genes; the ones with lower expression levels in smoker samples are downregulated genes. Genes’ differential expression level is assigned by Z score of their probe sets whom have the largest absolute Z score.
Top-ranked genes by the rank sum test and nDGE are different
Overlap ratios of the top-ranked genes by the rank sum test and nDGE
Consistency of top ranked gene sets derived from different data sets
Percentages of modules occupying their networks and the other modules in smoker samples
Genes in the module
% of the network*
% of the top 50 gene module
% of the top 100 gene module
% of the top 500 gene module
% of the top 1000 gene module
Percentages of modules occupying their networks and the other modules in non-smoker samples
Genes in the module
% in the network*
% in the top 50 gene module
% in the top 100 gene module
% in the top 500 gene module
% in the top 1000 gene module
The analyses suggest that E2F1 might regulate cell cycle related genes in smoker and non-smoker samples and play a role contributing to the molecular differences between smoker and non-smoker lung adenocarcinoma. Forty-seven genes are in the overlap between the 77-gene module identified in the top 100 gene network in smoker samples and the 63-gene module identified in the top 100 gene network in non-smoker samples. E2F1 might partially explain the molecular differences between smoker and non-smoker samples by up-regulating the genes in smoker samples and down-regulating them in non-smoker samples.
A potential molecular mechanism
It is shown that lung cancer patients who have never smoked respond better to chemotherapy than smoker lung cancer patients . Down regulation of E2F1 enhances the sensitivity of chemotherapy of cancer cell [30-32]. Our nDGE result demonstrates that the gene module regulated by E2F1 is up-regulated in smoker samples and down-regulated in non-smoker samples. E2F1 might have higher activity in smoker samples than in non-smoker samples. Altogether, these results suggest that the gene module regulated by E2F1 might explain the different response to chemotherapies between smoker and non-smoker lung cancers.
Lung cancer is the most common cause of cancer-related death in men and women worldwide . Although it has been suggested that lung cancer arising in non-smokers and smokers have distinct natural history, profile of oncogenic mutations, and response to targeted therapy , lung cancer in smokers and non-smokers is treated similarly to date. In our work, the integrative analysis of a series of smoker and non-smoker lung adenocarcinoma data sets shows that E2F1 might regulate a gene module enriched for cell cycle related genes, in turn, partially explain the molecular differences between smoker and non-smoker lung adenocarcinoma and different response to chemotherapies between smoker and non-smoker lung adenocarcinoma. The result leads to a better understanding of smoking and non-smoking related lung cancer and may provide benefits for risk evaluation for early lung cancer detection and personalized treatment of different lung cancers.
In this work, we develop a gene module based differential gene expression analysis, named network-based differential gene expression (nDGE) to prioritize differentially regulated genes and group them into gene modules. The key improvement of nDGE comparing to currently available methods is that nDGE uses a one-step integrative process to simultaneously identify gene-gene relationships and gene expression level changes associated with diseases while most existing methods involve two separated steps to define them. The resulted advantage is that no hard cutoff parameters are required in nDGE to determine gene-gene relationships and gene expression level changes associated with disease.
DGE analysis has been widely used in transcriptomic analysis of complex diseases. However, the traditional DGE analysis such as the rank sum test or the t test doesn’t always perform well in discovering differentially regulated genes and deregulated molecular mechanisms because the most significantly differentially expressed genes may not directly associate with the deregulated regulatory programs of the studied disease. The rank sum test analysis on the smoker and non-smoker lung adenocarcinoma data sets explicates it. The coherently deregulated genes together might better reflect the deregulated regulatory programs of complex disease. nDGE is developed aiming to identify these coherently deregulated genes.
In this work, we develop nDGE to prioritize deregulated genes and group them into gene modules. When applied to both simulated and empirical data, nDGE outperforms currently available methods in term of detecting coherently deregulated genes. More specifically, when applied to smoker and non-smoker lung cancer sets, nDGE results elucidate the molecular differences between smoker and non-smoker lung cancers that lead to different response to chemotherapies. We hope the result will lead to a better understanding of smoking and non-smoking related lung cancer and provide benefits for risk evaluation for early lung cancer detection and personalized treatment of different lung cancers.
We thank Prof. Sam M. Hanash for initiating the smoker and non-smoker lung cancers comparison and guiding the biological analysis. This work is partially supported by the National Basic Research Program of China (2012CB316504), the National High-Tech Research and Development Program of China (2012AA020401), NSFC grant 91010016, NSFC grant 61273228, NCI grants CA170722 and CA173772, a postdoc fellowship funded by Sage Bionetworks (Seattle, WA) and a fund from Canary Foundation (Palo Alto, CA).
- Murray D, Doran P, MacMathuna P, Moss AC: In silico gene expression analysis-an overview. Mol Cancer. 2007, 6: 50-10.1186/1476-4598-6-50.PubMed CentralView ArticlePubMedGoogle Scholar
- Kanehisa M, Goto S: KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000, 28 (1): 27-30. 10.1093/nar/28.1.27.PubMed CentralView ArticlePubMedGoogle Scholar
- de la Fuente A: From ‘differential expression’ to ‘differential networking’ - identification of dysfunctional regulatory networks in diseases. Trends Genet. 2010, 26 (7): 326-333. 10.1016/j.tig.2010.05.001.View ArticlePubMedGoogle Scholar
- Hudson NJ, Dalrymple BP, Reverter A: Beyond differential expression: the quest for causal mutations and effector molecules. BMC Genomics. 2012, 13: 356-10.1186/1471-2164-13-356.PubMed CentralView ArticlePubMedGoogle Scholar
- Hartwell LH, Hopfield JJ, Leibler S, Murray AW: From molecular to modular cell biology. Nature. 1999, 402 (6761 Suppl): C47-C52.View ArticlePubMedGoogle Scholar
- Nitsch D, Tranchevent LC, Goncalves JP, Vogt JK, Madeira SC, Moreau Y: PINTA: a web server for network-based gene prioritization from expression data. Nucleic Acids Res. 2011, 39 (Web Server issue): W334-W338.PubMed CentralView ArticlePubMedGoogle Scholar
- Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, Faria D, Bull S, Pawson T, Morris Q, Wrana JL: Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nat Biotechnol. 2009, 27 (2): 199-204. 10.1038/nbt.1522.View ArticlePubMedGoogle Scholar
- Reverter A, Hudson NJ, Nagaraj SH, Perez-Enciso M, Dalrymple BP: Regulatory impact factors: unraveling the transcriptional regulation of complex traits from expression data. Bioinformatics. 2010, 26 (7): 896-904. 10.1093/bioinformatics/btq051.View ArticlePubMedGoogle Scholar
- Moreau Y, Tranchevent LC: Computational tools for prioritizing candidate genes: boosting disease gene discovery. Nat Rev Genet. 2012, 13 (8): 523-536. 10.1038/nrg3253.View ArticlePubMedGoogle Scholar
- Wu C, Zhu J, Zhang X: Integrating gene expression and protein-protein interaction network to prioritize cancer-associated genes. BMC Bioinform. 2012, 13: 182-10.1186/1471-2105-13-182.View ArticleGoogle Scholar
- Langfelder P, Horvath S: WGCNA: an R package for weighted correlation network analysis. BMC Bioinform. 2008, 9: 559-10.1186/1471-2105-9-559.View ArticleGoogle Scholar
- Segal E, Shapira M, Regev A, Pe’er D, Botstein D, Koller D, Friedman N: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Nat Genet. 2003, 34 (2): 166-176. 10.1038/ng1165.View ArticlePubMedGoogle Scholar
- Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, et al: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci UsA. 2005, 102 (43): 15545-15550. 10.1073/pnas.0506580102.PubMed CentralView ArticlePubMedGoogle Scholar
- Ferlay J, Shin HR, Bray F, Forman D, Mathers C, Parkin DM: Estimates of worldwide burden of cancer in 2008: GLOBOCAN 2008. Int J Cancer. 2010, 127 (12): 2893-2917. 10.1002/ijc.25516.View ArticlePubMedGoogle Scholar
- Thun MJ, Hannan LM, Adams-Campbell LL, Boffetta P, Buring JE, Feskanich D, Flanders WD, Jee SH, Katanoda K, Kolonel LN, et al: Lung cancer occurrence in never-smokers: an analysis of 13 cohorts and 22 cancer registry studies. PLoS Med. 2008, 5 (9): e185-10.1371/journal.pmed.0050185.PubMed CentralView ArticlePubMedGoogle Scholar
- Couraud S, Zalcman G, Milleron B, Morin F, Souquet PJ: Lung cancer in never smokers-a review. Eur J Cancer. 2012, 48 (9): 1299-1311. 10.1016/j.ejca.2012.03.007.View ArticlePubMedGoogle Scholar
- Rudin CM, Avila-Tang E, Harris CC, Herman JG, Hirsch FR, Pao W, Schwartz AG, Vahakangas KH, Samet JM: Lung cancer in never smokers: molecular profiles and therapeutic implications. Clin Cancer Res. 2009, 15 (18): 5646-5661. 10.1158/1078-0432.CCR-09-0377.PubMed CentralView ArticlePubMedGoogle Scholar
- Macneil LT, Walhout AJ: Gene regulatory networks and the role of robustness and stochasticity in the control of gene expression. Genome Res. 2011, 21 (5): 645-657. 10.1101/gr.097378.109.PubMed CentralView ArticlePubMedGoogle Scholar
- Wang K, Narayanan M, Zhong H, Tompa M, Schadt EE, Zhu J: Meta-analysis of inter-species liver co-expression networks elucidates traits associated with common human diseases. PLoS Comput Biol. 2009, 5 (12): e1000616-10.1371/journal.pcbi.1000616.PubMed CentralView ArticlePubMedGoogle Scholar
- Lum PY, Chen Y, Zhu J, Lamb J, Melmed S, Wang S, Drake TA, Lusis AJ, Schadt EE: Elucidating the murine brain transcriptional network in a segregating mouse population to identify core functional modules for obesity and diabetes. J Neurochem. 2006, 97 (Suppl 1): 50-62.View ArticlePubMedGoogle Scholar
- da Huang W, Sherman BT, Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 2009, 4 (1): 44-57.View ArticlePubMedGoogle Scholar
- Zhang B, Kirov S, Snoddy J: WebGestalt: an integrated system for exploring gene sets in various biological contexts. Nucleic Acids Res. 2005, 33 (Web Server issue): W741-W748.PubMed CentralView ArticlePubMedGoogle Scholar
- Yeung KY, Medvedovic M, Bumgarner RE: From co-expression to co-regulation: how many microarray experiments do we need?. Genome Biol. 2004, 5 (7): R48-10.1186/gb-2004-5-7-r48.PubMed CentralView ArticlePubMedGoogle Scholar
- Saito R, Smoot ME, Ono K, Ruscheinski J, Wang PL, Lotia S, Pico AR, Bader GD, Ideker T: A travel guide to Cytoscape plugins. Nat Methods. 2012, 9 (11): 1069-1076. 10.1038/nmeth.2212.PubMed CentralView ArticlePubMedGoogle Scholar
- Johnson DG: The paradox of E2F1: oncogene and tumor suppressor gene. Mol Carcinog. 2000, 27 (3): 151-157. 10.1002/(SICI)1098-2744(200003)27:3<151::AID-MC1>3.0.CO;2-C.View ArticlePubMedGoogle Scholar
- Biswas AK, Johnson DG: Transcriptional and nontranscriptional functions of E2F1 in response to DNA damage. Cancer Res. 2012, 72 (1): 13-17. 10.1158/0008-5472.CAN-11-2196.PubMed CentralView ArticlePubMedGoogle Scholar
- Lin WC, Lin FT, Nevins JR: Selective induction of E2F1 in response to DNA damage, mediated by ATM-dependent phosphorylation. Genes Dev. 2001, 15 (14): 1833-1844.PubMed CentralPubMedGoogle Scholar
- Hecht SS: Lung carcinogenesis by tobacco smoke. Int J Cancer. 2012, 131 (12): 2724-2732. 10.1002/ijc.27816.PubMed CentralView ArticlePubMedGoogle Scholar
- Tsao AS, Liu D, Lee JJ, Spitz M, Hong WK: Smoking affects treatment outcome in patients with advanced nonsmall cell lung cancer. Cancer. 2006, 106 (11): 2428-2436. 10.1002/cncr.21884.View ArticlePubMedGoogle Scholar
- Helgason GV, O’Prey J, Ryan KM: Oncogene-induced sensitization to chemotherapy-induced death requires induction as well as deregulation of E2F1. Cancer Res. 2010, 70 (10): 4074-4080. 10.1158/0008-5472.CAN-09-2876.PubMed CentralView ArticlePubMedGoogle Scholar
- Hirano G, Izumi H, Kidani A, Yasuniwa Y, Han B, Kusaba H, Akashi K, Kuwano M, Kohno K: Enhanced expression of PCAF endows apoptosis resistance in cisplatin-resistant cells. Mol Cancer Res. 2010, 8 (6): 864-872. 10.1158/1541-7786.MCR-09-0458.View ArticlePubMedGoogle Scholar
- Zhai JM, Yin XY, Lai YR, Hou X, Cai JP, Hao XY, Liang LJ, Zhang LJ: Sorafenib enhances the chemotherapeutic efficacy of S-1 against hepatocellular carcinoma through downregulation of transcription factor E2F-1. Cancer Chemother Pharmacol. 2013, 71 (5): 1255-1264. 10.1007/s00280-013-2120-2.View ArticlePubMedGoogle Scholar
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