Identification of key regulators of pancreatic cancer progression through multidimensional systems-level analysis

Genome Medicine

Volumn 8, Issue 1, 2016, Pages

  Rajamani, Deepa ^a   Bhasin, Manoj K ^a  

^a BETH ISRAEL DEACONESS MEDICAL CENTER   (United States)

Author keywords

Biomarkers; Meta analysis; Multi omics; PDAC; Systems biology

Indexed keywords

HIGH MOBILITY GROUP A2 PROTEIN; MESSENGER RNA; MICRORNA; TRANSCRIPTION FACTOR E2F3; TRANSCRIPTOME;

ACTN1 GENE; ARTICLE; CANCER SURVIVAL; CONTROLLED STUDY; DLL1 GENE; DNA METHYLATION; DOWN REGULATION; E2F3 GENE; EPIGENETICS; GENE IDENTIFICATION; GENE INTERACTION; GENE REGULATORY NETWORK; GENETIC ASSOCIATION; HMGA2 GENE; IRS1 GENE; MOLECULAR INTERACTION; NUAK1 GENE; ONCOGENE; PANCREAS ADENOCARCINOMA; PATHOPHYSIOLOGY; PRIORITY JOURNAL; RASA1 GENE; REGULATOR GENE; SKI GENE; SYSTEM ANALYSIS; UPREGULATION; DISEASE COURSE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENETIC DATABASE; GENETICS; HUMAN; PANCREAS CARCINOMA; PANCREAS TUMOR; PRINCIPAL COMPONENT ANALYSIS; PROCEDURES; SURVIVAL ANALYSIS; SYSTEMS BIOLOGY;

CARCINOMA, PANCREATIC DUCTAL; DATABASES, GENETIC; DISEASE PROGRESSION; DNA METHYLATION; EPIGENOMICS; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION, NEOPLASTIC; GENE REGULATORY NETWORKS; HUMANS; MICRORNAS; PANCREATIC NEOPLASMS; PRINCIPAL COMPONENT ANALYSIS; SURVIVAL ANALYSIS; SYSTEMS BIOLOGY;

EID: 84964746581     PISSN: None     EISSN: 1756994X     Source Type: Journal    
DOI: 10.1186/s13073-016-0282-3     Document Type: Article

References (68)

1. Jemal A, Siegel R, Ward E, Hao Y, Xu J, Thun MJ. Cancer statistics, 2009. CA Cancer J Clin. 2009;59(4):225-49. doi: 10.3322/caac.20006.
2. Siegel R, Ma J, Zou Z, Jemal A. Cancer statistics, 2014. CA Cancer J Clin. 2014;64(1):9-29. doi: 10.3322/caac.21208.
3. Nai Q, Luo H, Zhang P, Hossain MA, Gu P, Sidhom IW, et al. How early can pancreatic cancer be recognized? A case report and review of the literature. Case Rep Oncol. 2015;8(1):46-9. doi: 10.1159/000375121.
4. Bünger S, Laubert T, Roblick UJ, Habermann JK. Serum biomarkers for improved diagnostic of pancreatic cancer: a current overview. J Cancer Res Clin Oncol. 2011;137(3):375-89. doi: 10.1007/s00432-010-0965-x.
5. Jamieson NB, Carter CR, McKay CJ, Oien KA. Tissue biomarkers for prognosis in pancreatic ductal adenocarcinoma: a systematic review and meta-analysis. Clin Cancer Res. 2011;17(10):3316-31. doi: 10.1158/1078-0432.CCR-10-3284.
6. Volinia S, Calin GA, Liu CG, Ambs S, Cimmino A, Petrocca F, et al. A microRNA expression signature of human solid tumors defines cancer gene targets. Proc Natl Acad Sci U S A. 2006;103(7):2257-61. doi: 10.1073/pnas.0510565103.
7. Pedersen KS, Bamlet WR, Oberg AL, de Andrade M, Matsumoto ME, Tang H, et al. Leukocyte DNA methylation signature differentiates pancreatic cancer patients from healthy controls. PLoS One. 2011;6(3):e18223. doi: 10.1371/journal.pone.0018223.
8. Edgar R, Domrachev M, Lash AE. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 2002;30(1):207-10.
9. Zhang J, Baran J, Cros A, Guberman JM, Haider S, Hsu J, et al. International Cancer Genome Consortium Data Portal--a one-stop shop for cancer genomics data. Database (Oxford). 2011;2011:bar026. doi: 10.1093/database/bar026.
10. Weinstein JN, Collisson EA, Mills GB, Shaw KR, Ozenberger BA, Ellrott K, et al. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet. 2013;45(10):1113-20. doi: 10.1038/ng.2764.
11. Jeong H, Mason SP, Barabási AL, Oltvai ZN. Lethality and centrality in protein networks. Nature. 2001;411(6833):41-2. doi: 10.1038/35075138.
12. Chuang HY, Lee E, Liu YT, Lee D, Ideker T. Network-based classification of breast cancer metastasis. Mol Syst Biol. 2007;3:140. doi: 10.1038/msb4100180.
13. R Development Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2015. URL https://www.R-project.org/
14. Irizarry RA, Hobbs B, Collin F, Beazer-Barclay YD, Antonellis KJ, Scherf U, et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics. 2003;4(2):249-64. doi: 10.1093/biostatistics/4.2.249.
15. Du P, Kibbe WA, Lin SM. lumi: a pipeline for processing Illumina microarray. Bioinformatics. 2008;24(13):1547-8. doi: 10.1093/bioinformatics/btn224.
16. Waggott D, Chu K, Yin S, Wouters BG, Liu FF, Boutros PC. NanoStringNorm: an extensible R package for the pre-processing of NanoString mRNA and miRNA data. Bioinformatics. 2012;28(11):1546-8. doi: 10.1093/bioinformatics/bts188.
17. López-Romero P. Pre-processing and differential expression analysis of Agilent microRNA arrays using the AgiMicroRna Bioconductor library. BMC Genomics. 2011;12:64. doi: 10.1186/1471-2164-12-64.
18. López-Romero P, González MA, Callejas S, Dopazo A, Irizarry RA. Processing of Agilent microRNA array data. BMC Res Notes. 2010;3:18. doi: 10.1186/1756-0500-3-18.
19. Kauffmann A, Gentleman R, Huber W. arrayQualityMetrics--a bioconductor package for quality assessment of microarray data. Bioinformatics. 2009;25(3):415-6. doi: 10.1093/bioinformatics/btn647.
20. Wang C, Rao N, Wang Y. Principal component analysis for exploring gene expression patterns. Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2007;24(4):736-41.
21. Yeung KY, Ruzzo WL. Principal component analysis for clustering gene expression data. Bioinformatics. 2001;17(9):763-74.
22. Smyth GK. Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol. 2004;3:Article3. doi: 10.2202/1544-6115.1027.
23. Bardou P, Mariette J, Escudié F, Djemiel C, Klopp C. jvenn: an interactive Venn diagram viewer. BMC Bioinformatics. 2014;15:293. doi: 10.1186/1471-2105-15-293.
24. Breitling R, Armengaud P, Amtmann A, Herzyk P. Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments. FEBS Lett. 2004;573(1-3):83-92. doi: 10.1016/j.febslet.2004.07.055.
25. Hong F, Breitling R, McEntee CW, Wittner BS, Nemhauser JL, Chory J. RankProd: a bioconductor package for detecting differentially expressed genes in meta-analysis. Bioinformatics. 2006;22(22):2825-7. doi: 10.1093/bioinformatics/btl476.
26. Ihnatova I. MAMA: Meta-Analysis of MicroArray. R package version 2.2.1. http://cran.r-project.org/package=MAMA. 2013.
27. Favero F. RmiR: package to work with miRNAs and miRNA targets with R. R package version 1.18.0. http://www.bioconductor.org/packages/release/bioc/html/RmiR.html. 2015.
28. Park M, Kim M, Hwang D, Kim WK, Kim SK, Shin J, et al. Characterization of gene expression and activated signaling pathways in solid-pseudopapillary neoplasm of pancreas. Mod Pathol. 2014;27(4):580-93. doi: 10.1038/modpathol.2013.154.
29. Smoot ME, Ono K, Ruscheinski J, Wang PL, Ideker T. Cytoscape 2.8: new features for data integration and network visualization. Bioinformatics. 2011;27(3):431-2. doi: 10.1093/bioinformatics/btq675.
30. Tarcea VG, Weymouth T, Ade A, Bookvich A, Gao J, Mahavisno V, et al. Michigan molecular interactions r2: from interacting proteins to pathways. Nucleic Acids Res. 2009;37(Database issue):D642-6. doi: 10.1093/nar/gkn722.
31. Montojo J, Zuberi K, Rodriguez H, Bader GD, Morris Q. GeneMANIA: Fast gene network construction and function prediction for Cytoscape. F1000Res. 2014;3:153. doi: 10.12688/f1000research.4572.1.
32. Scardoni G, Petterlini M, Laudanna C. Analyzing biological network parameters with CentiScaPe. Bioinformatics. 2009;25(21):2857-9. doi: 10.1093/bioinformatics/btp517.
33. Tong C, Niu J, Dai B, Xie Z. A novel complex networks clustering algorithm based on the core influence of nodes. ScientificWorldJournal. 2014;2014:801854. doi: 10.1155/2014/801854.
34. Maslov S, Sneppen K. Specificity and stability in topology of protein networks. Science. 2002;296(5569):910-3. doi: 10.1126/science.1065103.
35. Kaufman L, Rousseeuw PJ. Finding Groups in Data: an Introduction to Cluster Analysis. New York: Wiley; 1990.
36. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 2005;102(43):15545-50. doi: 10.1073/pnas.0506580102.
37. Hollander M, Wolfe DA. Nonparametric Statistical Methods. 2nd ed. New York: John Wiley & Sons; 1999.
38. Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, et al. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc Natl Acad Sci U S A. 1999;96(6):2907-12.
39. Haibe-Kains B, Desmedt C, Sotiriou C, Bontempi G. A comparative study of survival models for breast cancer prognostication based on microarray data: does a single gene beat them all? Bioinformatics. 2008;24(19):2200-8. doi: 10.1093/bioinformatics/btn374.
40. Goonesekere NC, Wang X, Ludwig L, Guda C. A meta analysis of pancreatic microarray datasets yields new targets as cancer genes and biomarkers. PLoS One. 2014;9(4):e93046. doi: 10.1371/journal.pone.0093046.
41. Sun T, Kong X, Du Y, Li Z. Aberrant microRNAs in pancreatic cancer: researches and clinical implications. Gastroenterol Res Pract. 2014;2014:386561. doi: 10.1155/2014/386561.
42. Ohuchida K, Mizumoto K, Lin C, Yamaguchi H, Ohtsuka T, Sato N, et al. MicroRNA-10a is overexpressed in human pancreatic cancer and involved in its invasiveness partially via suppression of the HOXA1 gene. Ann Surg Oncol. 2012;19(7):2394-402. doi: 10.1245/s10434-012-2252-3.
43. Kwon MS, Kim Y, Lee S, Namkung J, Yun T, Yi SG, et al. Integrative analysis of multi-omics data for identifying multi-markers for diagnosing pancreatic cancer. BMC Genomics. 2015;16 Suppl 9:S4. doi: 10.1186/1471-2164-16-S9-S4.
44. Zhang H, Duan HO, Kirley SD, Zukerberg LR, Wu CL. Aberrant splicing of cables gene, a CDK regulator, in human cancers. Cancer Biol Ther. 2005;4(11):1211-5.
45. Sumazin P, Yang X, Chiu HS, Chung WJ, Iyer A, Llobet-Navas D, et al. An extensive microRNA-mediated network of RNA-RNA interactions regulates established oncogenic pathways in glioblastoma. Cell. 2011;147(2):370-81. doi: 10.1016/j.cell.2011.09.041.
46. Lee JH, Kim DG, Bae TJ, Rho K, Kim JT, Lee JJ, et al. CDA: combinatorial drug discovery using transcriptional response modules. PLoS One. 2012;7(8):e42573. doi: 10.1371/journal.pone.0042573.
47. Qin Q, Furong W, Baosheng L. Multiple functions of hypoxia-regulated miR-210 in cancer. J Exp Clin Cancer Res. 2014;33:50. doi: 10.1186/1756-9966-33-50.
48. Stinson S, Lackner MR, Adai AT, Yu N, Kim HJ, O'Brien C, et al. miR-221/222 targeting of trichorhinophalangeal 1 (TRPS1) promotes epithelial-to-mesenchymal transition in breast cancer. Sci Signal. 2011;4(186):pt5. doi: 10.1126/scisignal.2002258.
49. Meng Z, Fu X, Chen X, Zeng S, Tian Y, Jove R, et al. miR-194 is a marker of hepatic epithelial cells and suppresses metastasis of liver cancer cells in mice. Hepatology. 2010;52(6):2148-57. doi: 10.1002/hep.23915.
50. Zhou Y, Jiang H, Gu J, Tang Y, Shen N, Jin Y. MicroRNA-195 targets ADP-ribosylation factor-like protein 2 to induce apoptosis in human embryonic stem cell-derived neural progenitor cells. Cell Death Dis. 2013;4:e695. doi: 10.1038/cddis.2013.195.
51. Gu J, Tamura M, Yamada KM. Tumor suppressor PTEN inhibits integrin- and growth factor-mediated mitogen-activated protein (MAP) kinase signaling pathways. J Cell Biol. 1998;143(5):1375-83.
52. Ramasamy A, Mondry A, Holmes CC, Altman DG. Key issues in conducting a meta-analysis of gene expression microarray datasets. PLoS Med. 2008;5(9):e184. doi: 10.1371/journal.pmed.0050184.
53. Ping Y, Deng Y, Wang L, Zhang H, Zhang Y, Xu C, et al. Identifying core gene modules in glioblastoma based on multilayer factor-mediated dysfunctional regulatory networks through integrating multi-dimensional genomic data. Nucleic Acids Res. 2015;43(4):1997-2007. doi: 10.1093/nar/gkv074.
54. 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. doi: 10.1038/msb.2010.31.
55. Craven KE, Gore J, Wilson JL, Korc M. Angiogenic gene signature in human pancreatic cancer correlates with TGF-beta and inflammatory transcriptomes. Oncotarget. 2015;7:323-41. doi: 10.18632/oncotarget.6345.
56. Huang B, Zhao J, Unkeless JC, Feng ZH, Xiong H. TLR signaling by tumor and immune cells: a double-edged sword. Oncogene. 2008;27(2):218-24. doi: 10.1038/sj.onc.1210904.
57. Chen JC, Alvarez MJ, Talos F, Dhruv H, Rieckhof GE, Iyer A, et al. Identification of causal genetic drivers of human disease through systems-level analysis of regulatory networks. Cell. 2014;159(2):402-14. doi: 10.1016/j.cell.2014.09.021.
58. Feinberg AP, Vogelstein B. Hypomethylation distinguishes genes of some human cancers from their normal counterparts. Nature. 1983;301(5895):89-92.
59. Nones K, Waddell N, Song S, Patch AM, Miller D, Johns A, et al. Genome-wide DNA methylation patterns in pancreatic ductal adenocarcinoma reveal epigenetic deregulation of SLIT-ROBO, ITGA2 and MET signaling. Int J Cancer. 2014;135(5):1110-8. doi: 10.1002/ijc.28765.
60. Lu J, Getz G, Miska EA, Alvarez-Saavedra E, Lamb J, Peck D, et al. MicroRNA expression profiles classify human cancers. Nature. 2005;435(7043):834-8. doi: 10.1038/nature03702.
61. Frampton AE, Krell J, Jamieson NB, Gall TM, Giovannetti E, Funel N, et al. microRNAs with prognostic significance in pancreatic ductal adenocarcinoma: a meta-analysis. Eur J Cancer. 2015;51(11):1389-404. doi: 10.1016/j.ejca.2015.04.006.
62. Muñoz-Rodríguez JL, Vrba L, Futscher BW, Hu C, Komenaka IK, Meza-Montenegro MM, et al. Differentially expressed microRNAs in postpartum breast cancer in hispanic women. PLoS One. 2015;10(4), e0124340. doi: 10.1371/journal.pone.0124340.
63. Aytes A, Mitrofanova A, Lefebvre C, Alvarez MJ, Castillo-Martin M, Zheng T, et al. Cross-species regulatory network analysis identifies a synergistic interaction between FOXM1 and CENPF that drives prostate cancer malignancy. Cancer Cell. 2014;25(5):638-51. doi: 10.1016/j.ccr.2014.03.017.
64. Chudnovsky Y, Kim D, Zheng S, Whyte WA, Bansal M, Bray MA, et al. ZFHX4 interacts with the NuRD core member CHD4 and regulates the glioblastoma tumor-initiating cell state. Cell Rep. 2014;6(2):313-24. doi: 10.1016/j.celrep.2013.12.032.
65. Compagno M, Lim WK, Grunn A, Nandula SV, Brahmachary M, Shen Q, et al. Mutations of multiple genes cause deregulation of NF-kappaB in diffuse large B-cell lymphoma. Nature. 2009;459(7247):717-21. doi: 10.1038/nature07968.
66. Bai J, Li Y, Shao T, Zhao Z, Wang Y, Wu A, et al. Integrating analysis reveals microRNA-mediated pathway crosstalk among Crohn's disease, ulcerative colitis and colorectal cancer. Mol Biosyst. 2014;10(9):2317-28. doi: 10.1039/c4mb00169a.
67. De Keersmaecker K, Real PJ, Gatta GD, Palomero T, Sulis ML, Tosello V, et al. The TLX1 oncogene drives aneuploidy in T cell transformation. Nat Med. 2010;16(11):1321-7. doi: 10.1038/nm.2246.
68. Della Gatta G, Palomero T, Perez-Garcia A, Ambesi-Impiombato A, Bansal M, Carpenter ZW, et al. Reverse engineering of TLX oncogenic transcriptional networks identifies RUNX1 as tumor suppressor in T-ALL. Nat Med. 2012;18(3):436-40. doi: 10.1038/nm.2610.