Consistent Differential Expression Pattern (CDEP) on microarray to identify genes related to metastatic behavior

BackgroundTo utilize the large volume of gene expression information generated from different microarray experiments, several meta-analysis techniques have been developed. Despite these efforts, there remain significant challenges to effectively increasing the statistical power and decreasing the Type I error rate while pooling the heterogeneous datasets from public resources. The objective of this study is to develop a novel meta-analysis approach, Consistent Differential Expression Pattern (CDEP), to identify genes with common differential expression patterns across different datasets.ResultsWe combined False Discovery Rate (FDR) estimation and the non-parametric RankProd approach to estimate the Type I error rate in each microarray dataset of the meta-analysis. These Type I error rates from all datasets were then used to identify genes with common differential expression patterns. Our simulation study showed that CDEP achieved higher statistical power and maintained low Type I error rate when compared with two recently proposed meta-analysis approaches. We applied CDEP to analyze microarray data from different laboratories that compared transcription profiles between metastatic and primary cancer of different types. Many genes identified as differentially expressed consistently across different cancer types are in pathways related to metastatic behavior, such as ECM-receptor interaction, focal adhesion, and blood vessel development. We also identified novel genes such as AMIGO2, Gem, and CXCL11 that have not been shown to associate with, but may play roles in, metastasis.ConclusionsCDEP is a flexible approach that borrows information from each dataset in a meta-analysis in order to identify genes being differentially expressed consistently. We have shown that CDEP can gain higher statistical power than other existing approaches under a variety of settings considered in the simulation study, suggesting its robustness and insensitivity to data variation commonly associated with microarray experiments.Availability: CDEP is implemented in R and freely available at: http://genomebioinfo.musc.edu/CDEP/Contact: zhengw@musc.edu

[1]  Yudi Pawitan,et al.  False discovery rate, sensitivity and sample size for microarray studies , 2005, Bioinform..

[2]  R. Weinberg,et al.  The Biology of Cancer , 2006 .

[3]  Rainer Breitling,et al.  A comparison of meta-analysis methods for detecting differentially expressed genes in microarray experiments , 2008, Bioinform..

[4]  Y. Kikkawa,et al.  Alivin 1, a Novel Neuronal Activity-Dependent Gene, Inhibits Apoptosis and Promotes Survival of Cerebellar Granule Neurons , 2003, The Journal of Neuroscience.

[5]  John D. Storey,et al.  Statistical significance for genomewide studies , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Sergio Romagnani,et al.  An Alternatively Spliced Variant of CXCR3 Mediates the Inhibition of Endothelial Cell Growth Induced by IP-10, Mig, and I-TAC, and Acts as Functional Receptor for Platelet Factor 4 , 2003, The Journal of experimental medicine.

[7]  Ron Edgar,et al.  Mining microarray data at NCBI's Gene Expression Omnibus (GEO)*. , 2006, Methods in molecular biology.

[8]  Shuichi Tsutsumi,et al.  Global gene expression analysis of gastric cancer by oligonucleotide microarrays. , 2002, Cancer research.

[9]  Sangsoo Kim,et al.  Combining multiple microarray studies and modeling interstudy variation , 2003, ISMB.

[10]  Rafael A Irizarry,et al.  Exploration, normalization, and summaries of high density oligonucleotide array probe level data. , 2003, Biostatistics.

[11]  X. Wang,et al.  An osteopontin fragment is essential for tumor cell invasion in hepatocellular carcinoma , 2007, Oncogene.

[12]  Rainer Breitling,et al.  Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments , 2004, FEBS letters.

[13]  Erin M. Conlon,et al.  A Bayesian mixture model for metaanalysis of microarray studies , 2008, Functional & Integrative Genomics.

[14]  Amber L. Wells,et al.  Biochemical and Structural Characterization of the Gem GTPase* , 2007, Journal of Biological Chemistry.

[15]  F. Pépin,et al.  Osteoactivin Promotes Breast Cancer Metastasis to Bone , 2007, Molecular Cancer Research.

[16]  Kellie J Archer,et al.  A non-parametric meta-analysis approach for combining independent microarray datasets: application using two microarray datasets pertaining to chronic allograft nephropathy , 2008, BMC Genomics.

[17]  BMC Bioinformatics , 2005 .

[18]  Jun S. Liu,et al.  Bayesian models for pooling microarray studies with multiple sources of replications , 2006, BMC Bioinformatics.

[19]  John Quackenbush,et al.  Sources of variation in baseline gene expression levels from toxicogenomics study control animals across multiple laboratories , 2008, BMC Genomics.

[20]  T. Barrette,et al.  Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. , 2002, Cancer research.

[21]  Ying Ma,et al.  [Expression of maspin and its relation to tumor vascularization in epithelian ovarian cancer]. , 2009, Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition.

[22]  Igor Jurisica,et al.  Gene Expression Profiling in Cervical Cancer: An Exploration of Intratumor Heterogeneity , 2006, Clinical Cancer Research.

[23]  Alan Hall,et al.  The cytoskeleton and cancer , 2009, Cancer and Metastasis Reviews.

[24]  Jean-Marc Guinebretière,et al.  A six-gene signature predicting breast cancer lung metastasis. , 2008, Cancer research.

[25]  David Botstein,et al.  The Stanford Microarray Database , 2001, Nucleic Acids Res..

[26]  Patrick Cahan,et al.  Meta-analysis of microarray results: challenges, opportunities, and recommendations for standardization. , 2007, Gene.

[27]  Brad T. Sherman,et al.  Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources , 2008, Nature Protocols.

[28]  Hyung-Seok Kim,et al.  Maspin and p53 Protein Expression in Gastric Adenocarcinoma and Its Clinical Applications , 2007, Applied immunohistochemistry & molecular morphology : AIMM.

[29]  Marie Joseph,et al.  Gene Signatures of Progression and Metastasis in Renal Cell Cancer , 2005, Clinical Cancer Research.

[30]  A. Chinnaiyan,et al.  Bioinformatics Strategies for Translating Genome‐Wide Expression Analyses into Clinically Useful Cancer Markers , 2004, Annals of the New York Academy of Sciences.

[31]  A. Chambers,et al.  Transcriptional regulation of osteopontin and the metastatic phenotype: Evidence for a Ras-activated enhancer in the human OPN promoter , 2004, Clinical & Experimental Metastasis.

[32]  Jian Huang,et al.  Regularized gene selection in cancer microarray meta-analysis , 2009, BMC Bioinformatics.

[33]  J. Schmid,et al.  The Ras‐like GTPase Gem is involved in cell shape remodelling and interacts with the novel kinesin‐like protein KIF9 , 2001, The EMBO journal.

[34]  Andrew Thomas,et al.  The BUGS project: Evolution, critique and future directions , 2009, Statistics in medicine.

[35]  Sunil Singhal,et al.  Gene expression signature predicts lymphatic metastasis in squamous cell carcinoma of the oral cavity , 2005, Oncogene.

[36]  Raffaele Fronza,et al.  Global alterations in mRNA polysomal recruitment in a cell model of colorectal cancer progression to metastasis. , 2006, Carcinogenesis.

[37]  M. Becich,et al.  Gene expression profiles of prostate cancer reveal involvement of multiple molecular pathways in the metastatic process , 2007, BMC Cancer.

[38]  P. Kuo,et al.  Sp1 regulates osteopontin expression in SW480 human colon adenocarcinoma cells. , 2007, Surgery.

[39]  M. Becich,et al.  Gene expression alterations in prostate cancer predicting tumor aggression and preceding development of malignancy. , 2004, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[40]  Jean YH Yang,et al.  Bioconductor: open software development for computational biology and bioinformatics , 2004, Genome Biology.

[41]  Shunpu Zhang,et al.  A comprehensive evaluation of SAM, the SAM R-package and a simple modification to improve its performance , 2007, BMC Bioinformatics.

[42]  Rainer Breitling,et al.  RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis , 2006, Bioinform..

[43]  Debashis Ghosh,et al.  Statistical issues and methods for meta-analysis of microarray data: a case study in prostate cancer , 2003, Functional & Integrative Genomics.

[44]  B. De Moor,et al.  Comparison and meta-analysis of microarray data: from the bench to the computer desk. , 2003, Trends in genetics : TIG.

[45]  D. Fan,et al.  Positive Correlation of Osteopontin, Cyclooxygenase-2 and Vascular Endothelial Growth Factor in Gastric Cancer , 2008, Cancer investigation.

[46]  A. Chinnaiyan,et al.  Integrative analysis of the cancer transcriptome , 2005, Nature Genetics.

[47]  John R. Stevens,et al.  Meta-Analysis Combines Affymetrix Microarray Results Across Laboratories , 2005, Comparative and functional genomics.

[48]  Sergio Contrino,et al.  ArrayExpress—a public repository for microarray gene expression data at the EBI , 2004, Nucleic Acids Res..

[49]  John Quackenbush,et al.  Confounding effects in "A six-gene signature predicting breast cancer lung metastasis". , 2009, Cancer research.

[50]  Debashis Ghosh,et al.  Prognostic meta-signature of breast cancer developed by two-stage mixture modeling of microarray data , 2004, BMC Genomics.

[51]  Andrew Thomas,et al.  WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility , 2000, Stat. Comput..

[52]  P. Brown,et al.  Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[53]  J. Kuja-Panula,et al.  AMIGO, a transmembrane protein implicated in axon tract development, defines a novel protein family with leucine-rich repeats , 2003, The Journal of cell biology.