Regulatory network reconstruction reveals genes with prognostic value for chronic lymphocytic leukemia

BMC Genomics

Volumn 16, Issue 1, 2015, Pages

  Yepes, Sally ^a   Torres, Maria Mercedes ^a   López Kleine, Liliana ^b  

^a UNIVERSIDAD DE LOS ANDES   (Colombia)

^b UNIVERSIDAD NACIONAL DE COLOMBIA   (Colombia)

Author keywords

CLL; IGVH mutational status; Regulatory networks

Indexed keywords

IMMUNOGLOBULIN HEAVY CHAIN; TRANSCRIPTION FACTOR; TRANSCRIPTION FACTOR 7 LIKE 1; TRANSCRIPTION FACTOR RUNX3; IMMUNOGLOBULIN VARIABLE REGION; TUMOR MARKER;

AAK1 GENE; AEBP1 GENE; AFF1 GENE; ALGORITHM; APEX1 GENE; ARID5B GENE; ARSD GENE; ARTICLE; ATOX1 GENE; BCL7A GENE; BRIP1 GENE; CANCER PROGNOSIS; CD247 GENE; CERS6 GENE; CHRONIC LYMPHATIC LEUKEMIA; COHORT ANALYSIS; CONTROLLED STUDY; DIP2C GENE; DOK2 GENE; DOWN REGULATION; EGR3 GENE; ELK3 GENE; EP400 GENE; FARP1 GENE; GENE; GENE EXPRESSION; GENE EXPRESSION PROFILING; GENE MUTATION; GENE REGULATORY NETWORK; GENETIC ANALYSIS; GFI1 GENE; GMDS GENE; HLADMA GENE; HOMER2 GENE; HOXB2 GENE; HUMAN; IGVH GENE; IL2RB GENE; KLF10 GENE; KLF7 GENE; LDOC1 GENE; LHFPL2 GENE; LPL GENE; LRMP GENE; MAJOR CLINICAL STUDY; MASTER REGULATOR ANALYSIS; ME2 GENE; MTA1 GENE; MYBL1 GENE; MYBL2 GENE; NCOR2 GENE; NMB GENE; NME1 GENE; NOD2 GENE; NR2F6 GENE; NRIP1 GENE; NUCB2 GENE; OVERALL SURVIVAL; P2RX1 GENE; PBX3 GENE; PDE8A GENE; PEBP1 GENE; PHEX GENE; PHTF1 GENE; PPARD GENE; RASGRP1 GENE; RUNX3 GENE; SDC3 GENE; SERPINF1 GENE; SFTPB GENE; SLAMF1 GENE; SLC16A6 GENE; SMARCA4 GENE; SNED1 GENE; SORL1 GENE; SUPPORT VECTOR MACHINE; SYNJ2 GENE; TCF3 GENE; TCF7 GENE; TCF7L2 GENE; TFDP1 GENE; TFEC GENE; TIME TO TREATMENT; TLE1 GENE; TMED3 GENE; TRPS1 GENE; TSHZ2 GENE; UGT8 GENE; UPREGULATION; ZNF135 GENE; ZNF236 GENE; ZNF354A GENE; ZNF91 GENE; BIOLOGY; FEMALE; GENE EXPRESSION REGULATION; GENETICS; IMMUNOGLOBULIN VARIABLE REGION; MALE; META ANALYSIS (TOPIC); METABOLISM; MORTALITY; MUTATION; NUCLEIC ACID DATABASE; PROCEDURES; PROGNOSIS;

BIOMARKERS, TUMOR; COMPUTATIONAL BIOLOGY; DATABASES, NUCLEIC ACID; FEMALE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, LEUKEMIC; GENE REGULATORY NETWORKS; HUMANS; IMMUNOGLOBULIN HEAVY CHAINS; IMMUNOGLOBULIN VARIABLE REGION; LEUKEMIA, LYMPHOCYTIC, CHRONIC, B-CELL; MALE; META-ANALYSIS AS TOPIC; MUTATION; PROGNOSIS; TRANSCRIPTION FACTORS;

EID: 84961122728     PISSN: None     EISSN: 14712164     Source Type: Journal    
DOI: 10.1186/s12864-015-2189-6     Document Type: Article

References (68)

1. Zenz T, Mertens D, Küppers R, Döhner H, Stilgenbauer S. From pathogenesis to treatment of chronic lymphocytic leukaemia. Nat Rev Cancer. 2010;10:37-50.
2. Damle RN, Wasil T, Fais F, Ghiotto F, Valetto A, Allen SL, et al. Ig V gene mutation status and CD38 expression as novel prognostic indicators in chronic lymphocytic leukemia. Blood. 1999;94(6):1840-7.
3. Hamblin TJ, Davis Z, Gardiner A, Oscier DG, Stevenson FK. Unmutated Ig V(H) genes are associated with a more aggressive form of chronic lymphocytic leukemia. Blood. 1999;94(6):1848-54.
4. Döhner H, Stielgenbauer S, Benner A, Leupolt E, Kröber A, Bullinger L, et al. Genomic aberrations and survival in chronic lymphocytic leukemia. N Engl J Med. 2000;343(26):1910-6.
5. Wang L, Lawrence MS, Wan Y, Stojanov P, Sougnez C, Stevenson K, et al. SF3B1 and other novel cancer genes in chronic lymphocytic leukemia. N Engl J Med. 2011;365(26):2497-506.
6. Wiestner A, Rosenwald A, Barry TS, Wright G, Davis RE, Henrickson SE, et al. ZAP-70 expression identifies a chronic lymphocytic leukemia subtype with unmutated immunoglobulin genes, inferior clinical outcome, and distinct gene expression profile. Blood. 2003;101(12):4944-51.
7. Klein U, Tu Y, Stolovitzky GA, Mattioli M, Cattoretti G, Husson H, et al. Gene expression profiling of B cell chronic lymphocytic leukemia reveals a homogeneous phenotype related to memory B cells. J Exp Med. 2001;194(11):1625-38.
8. Rosenwald A, Alizadeh AA, Widhopf G, Simon R, Davis RE, Yu X, et al. Relation of gene expression phenotype to immunoglobulin mutation genotype in B cell chronic lymphocytic leukemia. J Exp Med. 2001;194(11):1639-47.
9. Abruzzo LV, Barron LL, Anderson K, Newman RJ, Wierda WG, O'brien S, et al. Identification and validation of biomarkers of IgV(H) mutation status in chronic lymphocytic leukemia using microfluidics quantitative real-time polymerase chain reaction technology. J Mol Diagn. 2007;9(4):546-55.
10. Bae T, Rho K, Choi JW, Horimoto K, Kim W, Kim S. Identification of upstream regulators for prognostic expression signature genes in colorectal cancer. BMC Syst Biol. 2013;7:86.
11. Lim WK, Lyashenko E, Califano A. Master regulators used as breast cancer metastasis classifier. Pac Symp Biocomput. 2009:504-15
12. Ein-Dor L, Kela I, Getz G, Givol D, Domany E. Outcome signature genes in breast cancer: is there a unique set Bioinformatics. 2005;21(2):171-8.
13. Rhodes DR, Chinnaiyan AM. Integrative analysis of the cancer transcriptome. Nat Genet. 2005;37(Suppl):S31-7.
14. Margolin AA, Wang K, Lim WK, Kustagi M, Nemenman I, Califano A. Reverse engineering cellular networks. Nat Protoc. 2006;1(2):662-71.
15. Basso K, Margolin AA, Stolovitzky G, Klein U, Dalla-Favera R, Califano A, et al. Reverse engineering of regulatory networks in human B cells. Nat Genet. 2005;37(4):382-90.
16. Basso K, Saito M, Sumazin P, Margolin AA, Wang K, Lim WK, et al. Integrated biochemical and computational approach identifies BCL6 direct target genes controlling multiple pathways in normal germinal center B cells. Blood. 2010;115(5):975-84.
17. Fletcher MN, Castro MA, Wang X, de Santiago I, O'Reilly M, Chin SF, et al. Master regulators of FGFR2 signalling and breast cancer risk. Nat Commun. 2013;4:2464.
18. Albanus RD, Juliani Siqueira Dalmolin R, Alves Castro MA, Augusto de Bittencourt Pasquali M, de Miranda Ramos V, Pens G, et al. Reverse engineering the neuroblastoma regulatory network uncovers MAX as one of the master regulators of tumor progression. PLoS ONE. 2013;8(12):e82457.
19. Agnelli L, Forcato M, Ferrari F, Tuana G, Todoerti K, Walker BA. The reconstruction of transcriptional networks reveals critical genes with implications for clinical outcome of multiple myeloma. Clin Cancer Res. 2011;17(23):7402-12.
20. GEO DataSets [ http://www.ncbi.nlm.nih.gov/geo/ Accessed 1 Jun 2014. ]
21. Huber W, von Heydebreck A, Sultmann H, Poustka A, Vingron M. Variance stabilization applied to microarray data calibration and to the quantification of differential expression. Bioinformatics. 2002;18 Suppl 1:S96-104.
22. Wang X, Kang DD, Shen K, Song C, Lu S, Chang LC, et al. An R package suite for microarray meta-analysis in quality control, differentially expressed gene analysis and pathway enrichment detection. Bioinformatics. 2012;28(19):2534-6.
23. Taminau J, Lazar C, Meganck S, Nowé A. Comparison of merging and meta-analysis as alternative approaches for integrative gene expression analysis. ISRN Bioinform. 2014;2014:345106.
24. Kang DD, Sibille E, Kaminski N, Tseng GC. MetaQC: objective quality control and inclusion/exclusion criteria for genomic meta-analysis. Nucleic Acids Res. 2012;40(2):e15.
25. Rhodes DR, Barrette TR, Rubin MA, Ghosh D, Chinnaiyan AM. Meta-analysis of microarrays: interstudy validation of gene expression profiles reveals pathway dysregulation in prostate cancer. Cancer Res. 2002;62(15):4427-33.
26. Li J, Tseng GC. An adaptively weighted statistic for detecting differential gene expression when combining multiple transcriptomic studies. Ann Appl Stat. 2011;5:994-1019.
27. Song C, Tseng GC. Hypothesis setting and order statistic for robust genomic meta-analysis. Ann Appl Stat. 2014;8(2):777-800.
28. R Core Team. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2013. ISBN 3-900051-07-0, http://www.R-project.org/. Accessed 1 Jun 2014.
29. Shen K, Tseng GC. Meta-analysis for pathway enrichment analysis when combining multiple genomic studies. Bioinformatics. 2010;26(10):1316-23.
30. Tabas-Madrid D, Nogales-Cadenas R, Pascual-Montano A. GeneCodis3: a non-redundant and modular enrichment analysis tool for functional genomics. Nucleic Acids Res. 2012;40(Web Server issue):W478-83.
31. Nogales-Cadenas R, Carmona-Saez P, Vazquez M, Vicente C, Yang X, Tirado F, et al. GeneCodis: interpreting gene lists through enrichment analysis and integration of diverse biologicalinformation. Nucleic Acids Res. 2009;37(Web Server issue):W317-22.
32. Carmona-Saez P, Chagoyen M, Tirado F, Carazo JM, Pascual-Montano A. GENECODIS: a web-based tool for finding significant concurrent annotations in gene lists. Genome Biol. 2007;8:R3.
33. Lefebvre C, Rajbhandari P, Alvarez MJ, Bandaru P, Lim WK, Sato M, et al. A human B-cell interactome identifies MYB and FOXM1 as master regulators of proliferation in germinal centers. Mol Syst Biol. 2010;6:377.
34. Venet D, Dumont JE, Detours V. Most random gene expression signatures are significantly associated with breast cancer outcome. PLoS Comput Biol. 2011;7(10):e1002240.
35. Floratos A, Smith K, Ji Z, Watkinson J, Califano A. geWorkbench: an open source platform for integrative genomics. Bioinformatics. 2010;26(14):1779-80.
36. Goeman JJ, van de Geer SA, de Kort F, van Houwelingen HC. A global test for groups of genes: testing association with a clinical outcome. Bioinformatics. 2004;20(1):93-9.
37. Goeman JJ, Finos L. The inheritance procedure: multiple testing of tree-structured hypotheses. Stat Appl Genet Mol Biol. 2012;11(1):Article 11.
38. Carey V, Gentleman R, Mar J, Vertrees cfJ, Gatto L. MLInterfaces: Uniform interfaces to R machine learning procedures for data in Bioconductor containers. R package version 1.46.0. https://www.bioconductor.org/packages/release/bioc/html/MLInterfaces.html.
39. Schölkopf B, Smola AJ. Learning with kernels: support vector machines, regularization, optimization, and beyond. Cambridge: The MIT Press; 2002.
40. Su AI, Wiltshire T, Batalov S, Lapp H, Ching KA, Block D, et al. A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci U S A. 2004;101(16):6062-7.
41. Oppezzo P, Vasconcelos Y, Settegrana C, Jeannel D, Vuillier F, Legarff-Tavernier M, et al. The LPL/ADAM29 expression ratio is a novel prognosis indicator in chronic lymphocytic leukemia. Blood. 2005;106(2):650-7.
42. van't Veer MB, Brooijmans AM, Langerak AW, Verhaaf B, Goudswaard CS, Graveland WJ, et al. The predictive value of lipoprotein lipase for survival in chronic lymphocytic leukemia. Haematologica. 2006;91(1):56-63.
43. Gaidano G, Foà R, Dalla-Favera R. Molecular pathogenesis of chronic lymphocytic leukemia. J Clin Invest. 2012;122(10):3432-8.
44. Fais F, Ghiotto F, Hashimoto S, Sellars B, Valetto A, Allen SL, et al. Chronic lymphocytic leukemia B cells express restricted sets of mutated and unmutated antigen receptors. J Clin Invest. 1998;102(8):1515-25.
45. Schüll S, Günther SD, Brodesser S, Seeger JM, Tosetti B, Wiegmann K, et al. Cytochrome c oxidase deficiency accelerates mitochondrial apoptosis by activating ceramide synthase 6. Cell Death Dis. 2015;6:e1691.
46. Edmond V, Dufour F, Poiroux G, Shoji K, Malleter M, Fouqué A, et al. Downregulation of ceramide synthase-6 during epithelial-to-mesenchymal transition reduces plasma membrane fluidity and cancer cell motility. Oncogene. 2015;34(8):996-1005.
47. Lu D, Zhao Y, Tawatao R, Cottam HB, Sen M, Leoni LM, et al. Activation of the Wnt signaling pathway in chronic lymphocytic leukemia. Proc Natl Acad Sci U S A. 2004;101(9):3118-23.
48. Kienle D, Benner A, Läufle C, Winkler D, Schneider C, Bühler A, et al. Gene expression factors as predictors of genetic risk and survival in chronic lymphocytic leukemia. Haematologica. 2010;95(1):102-9.
49. Herold T, Jurinovic V, Metzeler KH, Boulesteix AL, Bergmann M, Seiler T, et al. An eight-gene expression signature for the prediction of survival and time to treatment in chronic lymphocytic leukemia. Leukemia. 2011;25(10):1639-45.
50. Bou Samra E, Klein B, Commes T, Moreaux J. Identification of a 20-gene expression-based risk score as a predictor of clinical outcome in chronic lymphocytic leukemia patients. Biomed Res Int. 2014;2014:423174.
51. Wu JQ, Seay M, Schulz VP, Hariharan M, Tuck D, Lian J, et al. Tcf7 is an important regulator of the switch of self-renewal and differentiation in a multipotential hematopoietic cell line. PLoS Genet. 2012;8(3):e1002565.
52. Golay J, Facchinetti V, Ying G, Introna M. The A-myb transcription factor in neoplastic and normal B cells. Leuk Lymphoma. 1997;26(3-4):271-9.
53. Stamatopoulos B, Haibe-Kains B, Equeter C, Meuleman N, Sorée A, De Bruyn C, et al. Gene expression profiling reveals differences in microenvironment interaction between patients with chronic lymphocytic leukemia expressing high versus low ZAP70 mRNA. Haematologica. 2009;94(6):790-9.
54. Golay J, Broccoli V, Lamorte G, Bifulco C, Parravicini C, Pizzey A, et al. The A-Myb transcription factor is a marker of centroblasts in vivo. J Immunol. 1998;160(6):2786-93.
55. Bret C, Klein B, Moreaux J. Gene expression-based risk score in diffuse large B-cell lymphoma. Oncotarget. 2012;3(12):1700-10.
56. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lossos IS, Rosenwald A, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature. 2000;403(6769):503-11.
57. Lenz G, Wright G, Dave SS, Xiao W, Powell J, Zhao H, et al. Stromal gene signatures in large-B-cell lymphomas. N Engl J Med. 2008;359(22):2313-23.
58. Mosca L, Fabris S, Lionetti M, Todoerti K, Agnelli L, Morabito F, et al. Integrative genomics analyses reveal molecularly distinct subgroups of B-cell chronic lymphocytic leukemia patients with 13q14 deletion. Clin Cancer Res. 2010;16:5641-53.
59. Fabris S, Mosca L, Todoerti K, Cutrona G, Lionetti M, Intini D, et al. Molecular and transcriptional characterization of 17p loss in B-cell chronic lymphocytic leukemia. Genes Chromosomes Cancer. 2008;47(9):781-93.
60. Del Giudice I, Chiaretti S, Tavolaro S, De Propris MS, Maggio R, Mancini F, et al. Spontaneous regression of chronic lymphocytic leukemia: clinical and biologic features of 9 cases. Blood. 2009;114(3):638-46.
61. Baptista MJ, Muntañola A, Calpe E, Abrisqueta P, Salamero O, Fernández E, et al. Differential gene expression profile associated to apoptosis induced by dexamethasone in CLL cells according to IGHV/ZAP-70 status. Clin Cancer Res. 2012;18(21):5924-33.
62. Saiya-Cork K, Collins R, Parkin B, Ouillette P, Kuizon E, Kujawski L, et al. A pathobiological role of the insulin receptor in chronic lymphocytic leukemia. Clin Cancer Res. 2011;17(9):2679-92.
63. Haslinger C, Schweifer N, Stilgenbauer S, Döhner H, Lichter P, Kraut N, et al. Microarray gene expression profiling of B-cell chronic lymphocytic leukemia subgroups defined by genomic aberrations and VH mutation status. J Clin Oncol. 2004;22(19):3937-49.
64. Seifert M, Sellmann L, Bloehdorn J, Wein F, Stilgenbauer S, Dürig J, et al. Cellular origin and pathophysiology of chronic lymphocytic leukemia. J Exp Med. 2012;209(12):2183-98.
65. GEO DataSets [ http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29605 ]. Accessed 1 Jun 2014.
66. Fabris S, Mosca L, Cutrona G, Lionetti M, Agnelli L, Ciceri G, et al. Chromosome 2p gain in monoclonal B-cell lymphocytosis and in early stage chronic lymphocytic leukemia. Am J Hematol. 2013;88(1):24-31.
67. Ronchetti D, Mosca L, Cutrona G, Tuana G, Gentile M, Fabris S, et al. A Small nucleolar RNAs as new biomarkers in chronic lymphocytic leukemia. BMC Med Genomics. 2013;6:27.
68. Chuang HY, Rassenti L, Salcedo M, Licon K, Kohlmann A, Haferlach T, et al. Subnetwork-based analysis of chronic lymphocytic leukemia identifies pathways that associate with disease progression. Blood. 2012;120(13):2639-49.