Urothelial cancer gene regulatory networks inferred from large-scale RNAseq, Bead and Oligo gene expression data

BackgroundUrothelial 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.MethodsIn 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.ResultsWe 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.ConclusionTo 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.

[1]  R. Albert.  Scale-free networks in cell biology , 2005, Journal of Cell Science.

[2]  Ralf Herwig,et al.  ConsensusPathDB: toward a more complete picture of cell biology , 2010, Nucleic Acids Res..

[3]  Frank Emmert-Streib,et al.  Influence of Statistical Estimators of Mutual Information and Data Heterogeneity on the Inference of Gene Regulatory Networks , 2011, PloS one.

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

[5]  Franziska Michor,et al.  Combination of a Novel Gene Expression Signature with a Clinical Nomogram Improves the Prediction of Survival in High-Risk Bladder Cancer , 2012, Clinical Cancer Research.

[6]  J. Falck,et al.  Cytochrome P450 ω-hydroxylase promotes angiogenesis and metastasis by upregulation of VEGF and MMP-9 in non-small cell lung cancer , 2011, Cancer Chemotherapy and Pharmacology.

[7]  Gianluca Bontempi,et al.  minet: A R/Bioconductor Package for Inferring Large Transcriptional Networks Using Mutual Information , 2008, BMC Bioinformatics.

[8]  Sumio Sugano,et al.  Identification of DMC1, a novel gene in the TOC region on 17q25.1 that shows loss of expression in multiple human cancers , 2001, Journal of Human Genetics.

[9]  H. Ozen.  Bladder cancer. , 1998, Current opinion in oncology.

[10]  Matthias Dehmer,et al.  B-cell lymphoma gene regulatory networks: biological consistency among inference methods , 2013, Front. Genet..

[11]  Joaquim Bellmunt,et al.  Identification of prefoldin amplification (1q23.3-q24.1) in bladder cancer using comparative genomic hybridization (CGH) arrays of urinary DNA , 2013, Journal of Translational Medicine.

[12]  Insuk Lee,et al.  A high-accuracy consensus map of yeast protein complexes reveals modular nature of gene essentiality , 2007, BMC Bioinformatics.

[13]  Kathy Pritchard-Jones,et al.  Frequent Long-Range Epigenetic Silencing of Protocadherin Gene Clusters on Chromosome 5q31 in Wilms' Tumor , 2009, PLoS genetics.

[14]  Frank Emmert-Streib,et al.  Inferring the conservative causal core of gene regulatory networks , 2010, BMC Systems Biology.

[15]  Frank Emmert-Streib,et al.  Organizational structure and the periphery of the gene regulatory network in B-cell lymphoma , 2012, BMC Systems Biology.

[16]  D. Hanahan,et al.  Hallmarks of Cancer: The Next Generation , 2011, Cell.

[17]  Ying-Li Lin,et al.  Clinical significance of protocadherin-8 (PCDH8) promoter methylation in bladder cancer , 2013, The Journal of international medical research.

[18]  K. Polyak,et al.  Tumor heterogeneity: causes and consequences. , 2010, Biochimica et biophysica acta.

[19]  Kevin Kontos,et al.  Information-Theoretic Inference of Large Transcriptional Regulatory Networks , 2007, EURASIP J. Bioinform. Syst. Biol..

[20]  David P Wood.  Re: Expression Signature of E2F1 and its Associated Genes Predict Superficial to Invasive Progression of Bladder Tumors. , 2011, The Journal of urology.

[21]  T. Hubbard,et al.  A census of human cancer genes , 2004, Nature Reviews Cancer.

[22]  Edsger W. Dijkstra,et al.  A note on two problems in connexion with graphs , 1959, Numerische Mathematik.

[23]  Gos Micklem,et al.  Supporting Online Material Materials and Methods Figs. S1 to S50 Tables S1 to S18 References Identification of Functional Elements and Regulatory Circuits by Drosophila Modencode , 2022 .

[24]  Young Nyun Park,et al.  Tudor domain-containing protein 4 as a potential cancer/testis antigen in liver cancer. , 2011, The Tohoku journal of experimental medicine.

[25]  T. Speed,et al.  Summaries of Affymetrix GeneChip probe level data. , 2003, Nucleic acids research.

[26]  Damian Szklarczyk,et al.  STRING v9.1: protein-protein interaction networks, with increased coverage and integration , 2012, Nucleic Acids Res..

[27]  Colin N. Dewey,et al.  RNA-Seq gene expression estimation with read mapping uncertainty , 2009, Bioinform..

[28]  Chris Wiggins,et al.  ARACNE: An Algorithm for the Reconstruction of Gene Regulatory Networks in a Mammalian Cellular Context , 2004, BMC Bioinformatics.

[29]  Christie S. Chang,et al.  The BioGRID interaction database: 2013 update , 2012, Nucleic Acids Res..

[30]  B. Oliver,et al.  Microarrays, deep sequencing and the true measure of the transcriptome , 2011, BMC Biology.

[31]  Garrett M. Dancik,et al.  Transcriptional signatures of Ral GTPase are associated with aggressive clinicopathologic characteristics in human cancer. , 2012, Cancer research.

[32]  V. Velculescu,et al.  Mutational analysis of gene families in human cancer. , 2005, Current opinion in genetics & development.

[33]  T. Speed,et al.  GOstat: find statistically overrepresented Gene Ontologies within a group of genes. , 2004, Bioinformatics.

[34]  Robert Gentleman,et al.  matchprobes: a Bioconductor package for the sequence-matching of microarray probe elements , 2004, Bioinform..

[35]  Yusuke Nakamura,et al.  Identification of Myc-associated protein with JmjC domain as a novel therapeutic target oncogene for lung cancer , 2007, Molecular Cancer Therapeutics.

[36]  Torben F. Ørntoft,et al.  Emmprin and Survivin Predict Response and Survival following Cisplatin-Containing Chemotherapy in Patients with Advanced Bladder Cancer , 2007, Clinical Cancer Research.

[37]  G. Altay,et al.  Structural influence of gene networks on their inference: analysis of C3NET. , 2011 .

[38]  N. Seki,et al.  LY6K is a novel molecular target in bladder cancer on basis of integrate genome-wide profiling , 2010, British Journal of Cancer.

[39]  Gabriele Ausiello,et al.  MINT: the Molecular INTeraction database , 2006, Nucleic Acids Res..

[40]  Y L Lin,et al.  Clinical and Prognostic Significance of Protocadherin-10 (PCDH10) Promoter Methylation in Bladder Cancer , 2012, The Journal of international medical research.

[41]  Gianluca Bontempi,et al.  On the Impact of Entropy Estimation on Transcriptional Regulatory Network Inference Based on Mutual Information , 2008, EURASIP J. Bioinform. Syst. Biol..

[42]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[43]  Gabriele Sales,et al.  graphite - a Bioconductor package to convert pathway topology to gene network , 2012, BMC Bioinformatics.

[44]  Adam A. Margolin,et al.  Reverse engineering of regulatory networks in human B cells , 2005, Nature Genetics.

[45]  G. Parmigiani,et al.  The Consensus Coding Sequences of Human Breast and Colorectal Cancers , 2006, Science.

[46]  Frank Emmert-Streib,et al.  Bagging Statistical Network Inference from Large-Scale Gene Expression Data , 2012, PloS one.

[47]  Edwin Wang,et al.  Signaling network assessment of mutations and copy number variations predict breast cancer subtype-specific drug targets. , 2013, Cell reports.

[48]  M. Gerstein,et al.  Relating whole-genome expression data with protein-protein interactions. , 2002, Genome research.

[49]  V. Karantza,et al.  Keratins in health and cancer: more than mere epithelial cell markers , 2011, Oncogene.

[50]  Derek Y. Chiang,et al.  MapSplice: Accurate mapping of RNA-seq reads for splice junction discovery , 2010, Nucleic acids research.

[51]  Steve Horvath,et al.  WGCNA: an R package for weighted correlation network analysis , 2008, BMC Bioinformatics.

[52]  Yun Yen,et al.  Human RIF1 encodes an anti-apoptotic factor required for DNA repair. , 2009, Carcinogenesis.

[53]  Zhong Liu,et al.  Long non-coding RNA expression profile in human gastric cancer and its clinical significances , 2013, Journal of Translational Medicine.

[54]  B. Snel,et al.  The yeast coexpression network has a small‐world, scale‐free architecture and can be explained by a simple model , 2004, EMBO reports.

[55]  A. E. EATON.  Did Flowers Exist during the Carboniferous Epoch? , 1879, Nature.

[56]  Li Li,et al.  Mapping and Genome Sequence Analysis of Chromosome 5 Regions Involved in Bladder Cancer Progression , 2001, Laboratory Investigation.

[57]  Ivan Molineris,et al.  A new computational approach to analyze human protein complexes and predict novel protein interactions , 2007, Genome Biology.

[58]  B. Challacombe,et al.  Recent advances in diagnosis and treatment of transitional cell carcinoma of the bladder. , 2013, International journal of surgery.

[59]  Zhong Liu,et al.  Clinical significance of the low expression of FER1L4 in gastric cancer patients , 2014, Tumor Biology.

[60]  Gábor Csárdi,et al.  The igraph software package for complex network research , 2006 .

[61]  C. Daub,et al.  BMC Systems Biology , 2007 .

[62]  R. Alba.  A graph‐theoretic definition of a sociometric clique† , 1973 .

[63]  Matthias Dehmer,et al.  Interfacing cellular networks of S. cerevisiae and E. coli: Connecting dynamic and genetic information , 2013, BMC Genomics.

[64]  Terje Espevik,et al.  The GOLD domain-containing protein TMED7 inhibits TLR4 signalling from the endosome upon LPS stimulation , 2012, Nature Communications.

[65]  Carsten Janke,et al.  Tubulin glycylases are required for primary cilia, control of cell proliferation and tumor development in colon , 2014, The EMBO journal.

[66]  Andreas Holzinger,et al.  Functional and genetic analysis of the colon cancer network , 2014, BMC Bioinformatics.

[67]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[68]  Matthias Dehmer,et al.  The gene regulatory network for breast cancer: integrated regulatory landscape of cancer hallmarks , 2014, Front. Genet..

[69]  D. Kwiatkowski,et al.  Targeting molecular aberrations in urothelial carcinoma: are we almost there? , 2013, American Society of Clinical Oncology educational book. American Society of Clinical Oncology. Annual Meeting.

[70]  Jan Albrecht,et al.  Increased expression of a glutamine transporter SNAT3 is a marker of malignant gliomas , 2004, Neuroreport.

[71]  Jens-Peter Volkmer,et al.  Anti-CD47 antibody–mediated phagocytosis of cancer by macrophages primes an effective antitumor T-cell response , 2013, Proceedings of the National Academy of Sciences.

[72]  Xiang-Sun Zhang,et al.  NOA: a novel Network Ontology Analysis method , 2011, Nucleic acids research.

[73]  Martin Vingron,et al.  IntAct: an open source molecular interaction database , 2004, Nucleic Acids Res..

[74]  Melanie I. Stefan,et al.  Molecules for memory: modelling CaMKII , 2007, BMC Systems Biology.

[75]  Mogens Kruhøffer,et al.  Gene Expression in the Urinary Bladder , 2004, Cancer Research.

[76]  I S Kohane,et al.  Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. , 1999, Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing.

[77]  Hanno Steen,et al.  Development of human protein reference database as an initial platform for approaching systems biology in humans. , 2003, Genome research.

[78]  J. Collins,et al.  Large-Scale Mapping and Validation of Escherichia coli Transcriptional Regulation from a Compendium of Expression Profiles , 2007, PLoS biology.

[79]  Karim Chamie,et al.  Recurrence of high‐risk bladder cancer: A population‐based analysis , 2013, Cancer.

[80]  G. Yousef,et al.  Clinical utility of kallikrein-related peptidases (KLK) in urogenital malignancies , 2013, Thrombosis and Haemostasis.

[81]  Eun-Jung Kim,et al.  Predictive value of progression-related gene classifier in primary non-muscle invasive bladder cancer , 2010, Molecular Cancer.

[82]  A Semjonow,et al.  Cytogenetic analysis of multifocal bladder cancer supports a monoclonal origin and intraepithelial spread of tumor cells. , 2001, Cancer research.

[83]  The Cancer Genome Atlas Research Network.  Comprehensive molecular characterization of urothelial bladder carcinoma , 2014, Nature.

[84]  C. Pál,et al.  The evolutionary dynamics of eukaryotic gene order , 2004, Nature Reviews Genetics.

[85]  In-Sun Chu,et al.  Expression signature of E2F1 and its associated genes predict superficial to invasive progression of bladder tumors. , 2010, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[86]  Steven J. M. Jones,et al.  Comprehensive molecular characterization of urothelial bladder carcinoma , 2014, Nature.

[87]  P. Geurts,et al.  Inferring Regulatory Networks from Expression Data Using Tree-Based Methods , 2010, PloS one.

[88]  Mark Newman,et al.  Networks: An Introduction , 2010 .