Meta-analysis of breast cancer microarray studies in conjunction with conserved cis-elements suggest patterns for coordinate regulation

BackgroundGene expression measurements from breast cancer (BrCa) tumors are established clinical predictive tools to identify tumor subtypes, identify patients showing poor/good prognosis, and identify patients likely to have disease recurrence. However, diverse breast cancer datasets in conjunction with diagnostic clinical arrays show little overlap in the sets of genes identified. One approach to identify a set of consistently dysregulated candidate genes in these tumors is to employ meta-analysis of multiple independent microarray datasets. This allows one to compare expression data from a diverse collection of breast tumor array datasets generated on either cDNA or oligonucleotide arrays.ResultsWe gathered expression data from 9 published microarray studies examining estrogen receptor positive (ER+) and estrogen receptor negative (ER-) BrCa tumor cases from the Oncomine database. We performed a meta-analysis and identified genes that were universally up or down regulated with respect to ER+ versus ER- tumor status. We surveyed both the proximal promoter and 3' untranslated regions (3'UTR) of our top-ranking genes in each expression group to test whether common sequence elements may contribute to the observed expression patterns. Utilizing a combination of known transcription factor binding sites (TFBS), evolutionarily conserved mammalian promoter and 3'UTR motifs, and microRNA (miRNA) seed sequences, we identified numerous motifs that were disproportionately represented between the two gene classes suggesting a common regulatory network for the observed gene expression patterns.ConclusionSome of the genes we identified distinguish key transcripts previously seen in array studies, while others are newly defined. Many of the genes identified as overexpressed in ER- tumors were previously identified as expression markers for neoplastic transformation in multiple human cancers. Moreover, our motif analysis identified a collection of specific cis-acting target sites which may collectively play a role in the differential gene expression patterns observed in ER+ versus ER- breast cancer tumors. Importantly, the gene sets and associated DNA motifs provide a starting point with which to explore the mechanistic basis for the observed expression patterns in breast tumors.

[1]  G. Berx,et al.  DeltaEF1 is a transcriptional repressor of E-cadherin and regulates epithelial plasticity in breast cancer cells , 2005, Oncogene.

[2]  C. Burge,et al.  Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets , 2005, Cell.

[3]  L. Wagner,et al.  21. UniGene: A Unified View of the Transcriptome , 2003 .

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

[5]  Howard Y. Chang,et al.  Robustness, scalability, and integration of a wound-response gene expression signature in predicting breast cancer survival. , 2005, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Christian A. Rees,et al.  Distinctive gene expression patterns in human mammary epithelial cells and breast cancers. , 1999, Proceedings of the National Academy of Sciences of the United States of America.

[7]  S. Sharp,et al.  Explaining heterogeneity in meta-analysis: a comparison of methods. , 1997, Statistics in medicine.

[8]  R. Fisher Statistical methods for research workers , 1927, Protoplasma.

[9]  Klaus Harter,et al.  Cis-motifs upstream of the transcription and translation initiation sites are effectively revealed by their positional disequilibrium in eukaryote genomes using frequency distribution curves , 2006, BMC Bioinformatics.

[10]  Gary H Lyman,et al.  Gene expression profile assays as predictors of recurrence-free survival in early-stage breast cancer: a metaanalysis. , 2006, Clinical breast cancer.

[11]  Brad T. Sherman,et al.  DAVID: Database for Annotation, Visualization, and Integrated Discovery , 2003, Genome Biology.

[12]  R. Russell,et al.  Principles of MicroRNA–Target Recognition , 2005, PLoS biology.

[13]  Stijn van Dongen,et al.  miRBase: microRNA sequences, targets and gene nomenclature , 2005, Nucleic Acids Res..

[14]  K. Lindblad-Toh,et al.  Systematic discovery of regulatory motifs in human promoters and 3′ UTRs by comparison of several mammals , 2005, Nature.

[15]  G. Smith,et al.  Meta-analysis: Potentials and promise , 1997, BMJ.

[16]  R. Tibshirani,et al.  Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[17]  M. Soares,et al.  Construction and characterization of a normalized cDNA library. , 1994, Proceedings of the National Academy of Sciences of the United States of America.

[18]  David L. Wheeler,et al.  GenBank , 2015, Nucleic Acids Res..

[19]  S G Thompson,et al.  Systematic Review: Why sources of heterogeneity in meta-analysis should be investigated , 1994, BMJ.

[20]  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.

[21]  J. Foekens,et al.  Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer , 2005, The Lancet.

[22]  Wen-Hsiung Li,et al.  Human polymorphism at microRNAs and microRNA target sites , 2007, Proceedings of the National Academy of Sciences.

[23]  Y. Yatabe,et al.  Reduced Expression of the let-7 MicroRNAs in Human Lung Cancers in Association with Shortened Postoperative Survival , 2004, Cancer Research.

[24]  N J Yoo,et al.  Mutational analysis of Wnt pathway gene LEF1 in common human carcinomas. , 2007, Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver.

[25]  Joaquín Dopazo,et al.  GEPAS: a web-based resource for microarray gene expression data analysis , 2003, Nucleic Acids Res..

[26]  Gabriel Kreiman,et al.  Identification of sparsely distributed clusters of cis-regulatory elements in sets of co-expressed genes. , 2004, Nucleic acids research.

[27]  Ian Smyth,et al.  Human sebaceous tumors harbor inactivating mutations in LEF1 , 2006, Nature Medicine.

[28]  A. Nobel,et al.  Concordance among Gene-Expression – Based Predictors for Breast Cancer , 2011 .

[29]  C. Burge,et al.  Prediction of Mammalian MicroRNA Targets , 2003, Cell.

[30]  David Botstein,et al.  Different gene expression patterns in invasive lobular and ductal carcinomas of the breast. , 2004, Molecular biology of the cell.

[31]  Yudong D. He,et al.  A Gene-Expression Signature as a Predictor of Survival in Breast Cancer , 2002 .

[32]  Philip M. Long,et al.  Breast cancer classification and prognosis based on gene expression profiles from a population-based study , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[33]  T. Barrette,et al.  ONCOMINE: a cancer microarray database and integrated data-mining platform. , 2004, Neoplasia.

[34]  T. Barrette,et al.  Mining for regulatory programs in the cancer transcriptome , 2005, Nature Genetics.

[35]  N. Rajewsky,et al.  Natural selection on human microRNA binding sites inferred from SNP data , 2006, Nature Genetics.

[36]  Deepak Grover,et al.  Evolution and distribution of RNA polymerase II regulatory sites from RNA polymerase III dependant mobile Alu elements , 2004, BMC Evolutionary Biology.

[37]  Len A. Pennacchio,et al.  Comparative genomic analysis reveals a distant liver enhancer upstream of the COUP-TFII gene , 2005, Mammalian Genome.

[38]  Alexander E. Kel,et al.  TRANSFAC® and its module TRANSCompel®: transcriptional gene regulation in eukaryotes , 2005, Nucleic Acids Res..

[39]  R. Russell,et al.  Animal MicroRNAs Confer Robustness to Gene Expression and Have a Significant Impact on 3′UTR Evolution , 2005, Cell.

[40]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[41]  P. Morin,et al.  β‐catenin signaling and cancer , 1999 .

[42]  M. Waterman,et al.  Lymphoid enhancer factor/T cell factor expression in colorectal cancer , 2004, Cancer and Metastasis Reviews.

[43]  J. Massagué Sorting out breast-cancer gene signatures. , 2007, The New England journal of medicine.

[44]  B. Asselain,et al.  Prognostic value of steroid receptors after long-term follow-up of 2257 operable breast cancers. , 1996, British Journal of Cancer.

[45]  Carsten O. Peterson,et al.  Estrogen receptor status in breast cancer is associated with remarkably distinct gene expression patterns. , 2001, Cancer research.

[46]  Daniel Sinnett,et al.  Detection and characterization of DNA variants in the promoter regions of hundreds of human disease candidate genes. , 2006, Genomics.

[47]  Robert Clarke,et al.  Association of increased basement membrane invasiveness with absence of estrogen receptor and expression of vimentin in human breast cancer cell lines , 1992, Journal of cellular physiology.

[48]  P. Morin,et al.  beta-catenin signaling and cancer. , 1999, BioEssays : news and reviews in molecular, cellular and developmental biology.

[49]  Victor M Montori,et al.  Conducting systematic reviews of diagnostic studies: didactic guidelines , 2002, BMC medical research methodology.

[50]  K R Abrams,et al.  Methods for exploring heterogeneity in meta-analysis. , 2001, Evaluation & the health professions.

[51]  R. Mantovani,et al.  A survey of 178 NF-Y binding CCAAT boxes. , 1998, Nucleic acids research.

[52]  S D Walter,et al.  Variation in baseline risk as an explanation of heterogeneity in meta-analysis. , 1997, Statistics in medicine.

[53]  R. Spang,et al.  Predicting the clinical status of human breast cancer by using gene expression profiles , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[54]  Peter Regitnig,et al.  Identification and meta‐analysis of a small gene expression signature for the diagnosis of estrogen receptor status in invasive ductal breast cancer , 2006, International journal of cancer.

[55]  M. Cronin,et al.  A multigene assay to predict recurrence of tamoxifen-treated, node-negative breast cancer. , 2004, The New England journal of medicine.

[56]  C. Croce,et al.  A microRNA expression signature of human solid tumors defines cancer gene targets , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[57]  Yudong D. He,et al.  Gene expression profiling predicts clinical outcome of breast cancer , 2002, Nature.

[58]  G. Rubin,et al.  Exploiting transcription factor binding site clustering to identify cis-regulatory modules involved in pattern formation in the Drosophila genome , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[59]  C. Croce,et al.  MicroRNA gene expression deregulation in human breast cancer. , 2005, Cancer research.

[60]  Martha L. Bulyk,et al.  Meta-analysis discovery of tissue-specific DNA sequence motifs from mammalian gene expression data , 2006, BMC Bioinformatics.

[61]  Naomi B Dillner,et al.  Transcriptional activation by the zinc-finger homeodomain protein delta EF1 in estrogen signaling cascades. , 2004, DNA and cell biology.

[62]  John D. Storey The positive false discovery rate: a Bayesian interpretation and the q-value , 2003 .

[63]  Minoti Hiremath,et al.  Beta-catenin and Tcfs in mammary development and cancer. , 2003, Journal of mammary gland biology and neoplasia.

[64]  R. Salunga,et al.  Gene expression profiles of human breast cancer progression , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[65]  L. Wagner,et al.  21. UniGene: A Unified View of the Transcriptome , 2003 .

[66]  G. Church,et al.  Computational identification of transcription factor binding sites via a transcription-factor-centric clustering (TFCC) algorithm. , 2002, Journal of molecular biology.

[67]  D. Petitti,et al.  Approaches to heterogeneity in meta‐analysis , 2001, Statistics in medicine.

[68]  R. Fisher,et al.  Statistical Methods for Research Workers , 1930, Nature.

[69]  C. Burge,et al.  The Widespread Impact of Mammalian MicroRNAs on mRNA Repression and Evolution , 2005, Science.

[70]  D. Pe’er,et al.  Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data , 2003, Nature Genetics.

[71]  Joaquín Dopazo,et al.  GEPAS, an experiment-oriented pipeline for the analysis of microarray gene expression data , 2005, Nucleic Acids Res..

[72]  David J. Arenillas,et al.  oPOSSUM: identification of over-represented transcription factor binding sites in co-expressed genes , 2005, Nucleic acids research.

[73]  Joshua M. Stuart,et al.  A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules , 2003, Science.

[74]  R. Tweedie,et al.  Publication Bias in Meta-Analysis: A Bayesian Data-Augmentation Approach to Account for Issues Exempli(cid:12)ed in the Passive Smoking Debate , 1997 .

[75]  J. Craig Venter,et al.  Rapid cDNA sequencing (expressed sequence tags) from a directionally cloned human infant brain cDNA library , 1993, Nature Genetics.

[76]  M. Cronin,et al.  Gene expression and benefit of chemotherapy in women with node-negative, estrogen receptor-positive breast cancer. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[77]  C. Auffray,et al.  The I.M.A.G.E. Consortium: an integrated molecular analysis of genomes and their expression. , 1996, Genomics.

[78]  J. Nevins,et al.  E2Fs link the control of G1/S and G2/M transcription , 2004, The EMBO journal.

[79]  Clifford A. Meyer,et al.  Genome-wide analysis of estrogen receptor binding sites , 2006, Nature Genetics.

[80]  S D Dalebout,et al.  A tutorial on conducting meta-analyses of clinical outcome research. , 1998, Journal of speech, language, and hearing research : JSLHR.

[81]  Debashis Ghosh,et al.  Identification of GATA3 as a breast cancer prognostic marker by global gene expression meta-analysis. , 2005, Cancer research.