Skip to main content

Advertisement

Log in

Genome-wide pathway analysis in neuroblastoma

  • Research Article
  • Published:
Tumor Biology

Abstract

The aim of this study was to identify candidate single-nucleotide polymorphisms (SNPs) that might play a role in susceptibility to neuroblastoma, elucidate their potential mechanisms, and generate SNP-to-gene-to-pathway hypotheses. A genome-wide association study (GWAS) dataset of neuroblastoma that included 442,976 SNPs from 1,627 neuroblastoma patients and 3,254 control subjects of European descent was used in this study. The identify candidate causal SNPs and pathways (ICSNPathway) analysis was applied to the GWAS dataset. ICSNPathway analysis identified 15 candidate SNPs, 10 genes, and 31 pathways, which revealed 10 hypothetical biological mechanisms. The strongest hypothetical biological mechanism was one wherein SNPrs40401 modulates the role of IL3 in several pathways and conditions, including the stem pathway, asthma (hsa05310), the dendritic cell pathway, and development (0.001 < p < 0.004; 0.001 < FDR < 0.033). The second strongest mechanism identified was that in which rs1048108 and rs16852600 alter the function of BARD1, which negatively regulates developmental process and modulates processes including cell development and programmed cell death (0.001 < p < 0.004; 0.001 < FDR < 0.033). The third mechanism identified was one wherein rs1939212 modulated CFL1, resulting in negative regulation of development, cell death, neural crest cell migration, and apoptosis (0.001 < p < 0.004; 0.001 < FDR < 0.033). By using the ICSNPathway to analyze neuroblastoma GWAS data, 15 candidate SNPs, 10 genes including IL3, BARD1, and CFL, and 31 pathways were identified that might contribute to the susceptibility of patients to neuroblastoma.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Maris JM, Hogarty MD, Bagatell R, Cohn SL. Neuroblastoma. Lancet. 2007;369(9579):2106–20.

    Article  CAS  PubMed  Google Scholar 

  2. Sridhar S, Al-Moallem B, Kamal H, Terrile M, Stallings RL. New insights into the genetics of neuroblastoma. Mol Diagn Ther. 2013;17(2):63–9.

    Article  CAS  PubMed  Google Scholar 

  3. Capasso M, Diskin SJ, Totaro F, Longo L, De Mariano M, Russo R, et al. Replication of GWAS-identified neuroblastoma risk loci strengthens the role of BARD1 and affirms the cumulative effect of genetic variations on disease susceptibility. Carcinogenesis. 2013;34(3):605–11.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  4. Latorre V, Diskin SJ, Diamond MA, Zhang H, Hakonarson H, Maris JM, et al. Replication of neuroblastoma SNP association at the BARD1 locus in African-Americans. Cancer Epidemiol Biomarkers Prev. 2012;21(4):658–63.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  5. Capasso M, Devoto M, Hou C, Asgharzadeh S, Glessner JT, Attiyeh EF, et al. Common variations in BARD1 influence susceptibility to high-risk neuroblastoma. Nat Genet. 2009;41(6):718–23.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  6. Manolio TA. Genomewide association studies and assessment of the risk of disease. N Engl J Med. 2010;363(2):166–76.

    Article  CAS  PubMed  Google Scholar 

  7. Johnson AD, O'Donnell CJ. An open access database of genome-wide association results. BMC Med Genet. 2009;10:6.

    Article  PubMed Central  PubMed  Google Scholar 

  8. Wang K, Li M, Hakonarson H. Analysing biological pathways in genome-wide association studies. Nat Rev Genet. 2010;11(12):843–54.

    Article  CAS  PubMed  Google Scholar 

  9. Lee YH, Song GG. Pathway analysis of genome-wide association studies on uric acid concentrations. Hum Immunol. 2012;73(8):805–10.

    Article  CAS  PubMed  Google Scholar 

  10. Lee YH, Bae SC, Choi SJ, Ji JD, Song GG. Genome-wide pathway analysis of genome-wide association studies on systemic lupus erythematosus and rheumatoid arthritis. Mol Biol Rep. 2012;39(12):10627–35.

    Article  CAS  PubMed  Google Scholar 

  11. Zhang K, Chang S, Cui S, Guo L, Zhang L, Wang J. ICSNPathway: identify candidate causal SNPs and pathways from genome-wide association study by one analytical framework. Nucleic Acids Res. 2011;39(Web Server issue):W437–43.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  12. International HapMap C, Altshuler DM, Gibbs RA, Peltonen L, Altshuler DM, Gibbs RA, et al. Integrating common and rare genetic variation in diverse human populations. Nature. 2010;467(7311):52–8.

    Article  Google Scholar 

  13. Kanehisa M, Goto S, Furumichi M, Tanabe M, Hirakawa M. KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res. 2010;38(Database issue):D355–60.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  14. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet. 2000;25(1):25–9.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  15. Johnson AD, Handsaker RE, Pulit SL, Nizzari MM, O'Donnell CJ, de Bakker PI. SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics. 2008;24(24):2938–9.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  16. Choi SJ, Rho YH, Ji JD, Song GG, Lee YH. Genome scan meta-analysis of rheumatoid arthritis. Rheumatology (Oxford). 2006;45(2):166–70.

    Article  CAS  Google Scholar 

  17. Lee YH, Nath SK. Systemic lupus erythematosus susceptibility loci defined by genome scan meta-analysis. Hum Genet. 2005;118(3–4):434–43.

    Article  PubMed  Google Scholar 

  18. Zeggini E, Ioannidis JP. Meta-analysis in genome-wide association studies. Pharmacogenomics. 2009;10(2):191–201.

    Article  PubMed Central  PubMed  Google Scholar 

  19. Elbers CC, van Eijk KR, Franke L, Mulder F, van der Schouw YT, Wijmenga C, et al. Using genome-wide pathway analysis to unravel the etiology of complex diseases. Genet Epidemiol. 2009;33(5):419–31.

    Article  PubMed  Google Scholar 

  20. Fridley BL, Biernacka JM. Gene set analysis of SNP data: benefits, challenges, and future directions. Eur J Hum Genet. 2011;19(8):837–43.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  21. Pedroso I, Breen G. Gene set analysis and network analysis for genome-wide association studies. Cold Spring Harb Protoc. 2011;2011(9).

  22. Steelman LS, Pohnert SC, Shelton JG, Franklin RA, Bertrand FE, McCubrey JA. JAK/STAT, Raf/MEK/ERK, PI3K/Akt and BCR-ABL in cell cycle progression and leukemogenesis. Leukemia. 2004;18(2):189–218.

    Article  CAS  PubMed  Google Scholar 

  23. Fabbro M, Savage K, Hobson K, Deans AJ, Powell SN, McArthur GA, et al. BRCA1-BARD1 complexes are required for p53Ser-15 phosphorylation and a G1/S arrest following ionizing radiation-induced DNA damage. J Biol Chem. 2004;279(30):31251–8.

    Article  CAS  PubMed  Google Scholar 

  24. Dong Y, Hakimi MA, Chen X, Kumaraswamy E, Cooch NS, Godwin AK, et al. Regulation of BRCC, a holoenzyme complex containing BRCA1 and BRCA2, by a signalosome-like subunit and its role in DNA repair. Mol Cell. 2003;12(5):1087–99.

    Article  CAS  PubMed  Google Scholar 

  25. Thai TH, Du F, Tsan JT, Jin Y, Phung A, Spillman MA, et al. Mutations in the BRCA1-associated RING domain (BARD1) gene in primary breast, ovarian and uterine cancers. Hum Mol Genet. 1998;7(2):195–202.

    Article  CAS  PubMed  Google Scholar 

  26. Bosse KR, Diskin SJ, Cole KA, Wood AC, Schnepp RW, Norris G, et al. Common variation at BARD1 results in the expression of an oncogenic isoform that influences neuroblastoma susceptibility and oncogenicity. Cancer Res. 2012;72(8):2068–78.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  27. Thirone AC, Speight P, Zulys M, Rotstein OD, Szaszi K, Pedersen SF, et al. Hyperosmotic stress induces Rho/Rho kinase/LIM kinase-mediated cofilin phosphorylation in tubular cells: key role in the osmotically triggered F-actin response. Am J Physiol Cell Physiol. 2009;296(3):C463–75.

    Article  CAS  PubMed  Google Scholar 

  28. Chai X, Forster E, Zhao S, Bock HH, Frotscher M. Reelin stabilizes the actin cytoskeleton of neuronal processes by inducing n-cofilin phosphorylation at serine3. J Neurosci. 2009;29(1):288–99.

    Article  CAS  PubMed  Google Scholar 

  29. Frotscher M, Chai X, Bock HH, Haas CA, Forster E, Zhao S. Role of Reelin in the development and maintenance of cortical lamination. J Neural Transm. 2009;116(11):1451–5.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

The authors gratefully acknowledge investigators for sharing their valuable GWAS data.

Conflicts of interest

None.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Young Ho Lee.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Lee, Y.H., Kim, JH. & Song, G.G. Genome-wide pathway analysis in neuroblastoma. Tumor Biol. 35, 3471–3485 (2014). https://doi.org/10.1007/s13277-013-1459-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13277-013-1459-7

Keywords

Navigation