title: Urothelial cancer gene regulatory networks inferred from large-scale RNAseq, Bead and Oligo gene expression data.
abstract: BACKGROUND
  Urothelial pathogenesis is a complex process driven by an underlying network of interconnected genes. The identification of novel genomic target regions and gene targets that drive urothelial carcinogenesis is crucial in order to improve our current limited understanding of urothelial cancer (UC) on the molecular level. The inference of genome-wide gene regulatory networks (GRN) from large-scale gene expression data provides a promising approach for a detailed investigation of the underlying network structure associated to urothelial carcinogenesis.
  
  
  METHODS
  In our study we inferred and compared three GRNs by the application of the BC3Net inference algorithm to large-scale transitional cell carcinoma gene expression data sets from Illumina RNAseq (179 samples), Illumina Bead arrays (165 samples) and Affymetrix Oligo microarrays (188 samples). We investigated the structural and functional properties of GRNs for the identification of molecular targets associated to urothelial cancer.
  
  
  RESULTS
  We found that the urothelial cancer (UC) GRNs show a significant enrichment of subnetworks that are associated with known cancer hallmarks including cell cycle, immune response, signaling, differentiation and translation. Interestingly, the most prominent subnetworks of co-located genes were found on chromosome regions 5q31.3 (RNAseq), 8q24.3 (Oligo) and 1q23.3 (Bead), which all represent known genomic regions frequently deregulated or aberated in urothelial cancer and other cancer types. Furthermore, the identified hub genes of the individual GRNs, e.g., HID1/DMC1 (tumor development), RNF17/TDRD4 (cancer antigen) and CYP4A11 (angiogenesis/ metastasis) are known cancer associated markers. The GRNs were highly dataset specific on the interaction level between individual genes, but showed large similarities on the biological function level represented by subnetworks. Remarkably, the RNAseq UC GRN showed twice the proportion of significant functional subnetworks. Based on our analysis of inferential and experimental networks the Bead UC GRN showed the lowest performance compared to the RNAseq and Oligo UC GRNs.
  
  
  CONCLUSIONS
  To our knowledge, this is the first study investigating genome-scale UC GRNs. RNAseq based gene expression data is the data platform of choice for a GRN inference. Our study offers new avenues for the identification of novel putative diagnostic targets for subsequent studies in bladder tumors.
authors.affiliation: Centre for Cancer Research and Cell Biology (CCRCB), Queens University Belfast, 97 Lisburn Road, Belfast, County Antrim, Northern Ireland, UK. r.dematossimoes@qub.ac.uk.
authors.forename: Ricardo
authors.initials: R
authors.lastname: de Matos Simoes
authors.affiliation: Centre for Cancer Research and Cell Biology (CCRCB), Queens University Belfast, 97 Lisburn Road, Belfast, County Antrim, Northern Ireland, UK. s.dalleau@qub.ac.uk.
authors.forename: Sabine
authors.initials: S
authors.lastname: Dalleau
authors.affiliation: Centre for Cancer Research and Cell Biology (CCRCB), Queens University Belfast, 97 Lisburn Road, Belfast, County Antrim, Northern Ireland, UK. k.williamson@qub.ac.uk.
authors.forename: Kate E
authors.initials: KE
authors.lastname: Williamson
authors.affiliation: Computational Medicine and Statistical Learning Laboratory, Department of Signal Processing, Tampere University of Technology, Tampere, 33720, Finland. v@bio-complexity.com.|Institute of Biosciences and Medical Technology, Tampere, 33520, Finland. v@bio-complexity.com.
authors.forename: Frank
authors.initials: F
authors.lastname: Emmert-Streib
mesh_terms: D019295:Computational Biology; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D017423:Sequence Analysis, RNA; D015398:Signal Transduction / Q000235:genetics; D001749:Urinary Bladder Neoplasms / Q000235:genetics* / Q000473:pathology; D019459:Urothelium / Q000378:metabolism* / Q000473:pathology