title: Meta-analysis of prostate cancer gene expression data identifies a novel discriminatory signature enriched for glycosylating enzymes.
abstract: BACKGROUND
  Tumorigenesis is characterised by changes in transcriptional control. Extensive transcript expression data have been acquired over the last decade and used to classify prostate cancers. Prostate cancer is, however, a heterogeneous multifocal cancer and this poses challenges in identifying robust transcript biomarkers.
  
  
  METHODS
  In this study, we have undertaken a meta-analysis of publicly available transcriptomic data spanning datasets and technologies from the last decade and encompassing laser capture microdissected and macrodissected sample sets.
  
  
  RESULTS
  We identified a 33 gene signature that can discriminate between benign tissue controls and localised prostate cancers irrespective of detection platform or dissection status. These genes were significantly overexpressed in localised prostate cancer versus benign tissue in at least three datasets within the Oncomine Compendium of Expression Array Data. In addition, they were also overexpressed in a recent exon-array dataset as well a prostate cancer RNA-seq dataset generated as part of the The Cancer Genomics Atlas (TCGA) initiative. Biologically, glycosylation was the single enriched process associated with this 33 gene signature, encompassing four glycosylating enzymes. We went on to evaluate the performance of this signature against three individual markers of prostate cancer, v-ets avian erythroblastosis virus E26 oncogene homolog (ERG) expression, prostate specific antigen (PSA) expression and androgen receptor (AR) expression in an additional independent dataset. Our signature had greater discriminatory power than these markers both for localised cancer and metastatic disease relative to benign tissue, or in the case of metastasis, also localised prostate cancer.
  
  
  CONCLUSIONS
  In conclusion, robust transcript biomarkers are present within datasets assembled over many years and cohorts and our study provides both examples and a strategy for refining and comparing datasets to obtain additional markers as more data are generated.
authors.affiliation: Prostate Cancer Research Group, Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership University of Oslo and Oslo University Hospital, Oslo, Norway. stefan.barfeld@ncmm.uio.no.
authors.forename: Stefan J
authors.initials: SJ
authors.lastname: Barfeld
authors.affiliation: Bioinformatics & Biostatistics, Cancer Research UK London Research Institute, 44 Lincoln's Inn Fields, London, WC2A 3PX, UK. phil.east@cancer.org.uk.
authors.forename: Philip
authors.initials: P
authors.lastname: East
authors.affiliation: Prostate Cancer Research Group, Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership University of Oslo and Oslo University Hospital, Oslo, Norway. verena.zuber@ncmm.uio.no.
authors.forename: Verena
authors.initials: V
authors.lastname: Zuber
authors.affiliation: Prostate Cancer Research Group, Centre for Molecular Medicine Norway (NCMM), Nordic EMBL Partnership University of Oslo and Oslo University Hospital, Oslo, Norway. ian.mills@ncmm.uio.no.|Department of Cancer Prevention and Urology, Institute of Cancer Research and Oslo University Hospital, Oslo, Norway. ian.mills@ncmm.uio.no.
authors.forename: Ian G
authors.initials: IG
authors.lastname: Mills
mesh_terms: D018507:Gene Expression Regulation, Developmental; D015972:Gene Expression Regulation, Neoplastic; D006031:Glycosylation; D006801:Humans; D008297:Male; D009363:Neoplasm Proteins / Q000096:biosynthesis / Q000235:genetics; D011471:Prostatic Neoplasms / Q000201:enzymology / Q000235:genetics; D059467:Transcriptome
chemical_list: D009363:Neoplasm Proteins