Tumor Biology

, Volume 37, Issue 2, pp 1845–1851 | Cite as

Genome-wide haplotype association analysis identifies SERPINB9, SERPINE2, GAK, and HSP90B1 as novel risk genes for oral squamous cell carcinoma

  • Wei Sun
  • Wenhua Lv
  • Hongchao Lv
  • Ruijie Zhang
  • Yongshuai Jiang
Original Article


The oral squamous cell carcinoma (OSCC) is one of the most common malignant epithelial neoplasms and considered to be caused by the genetic damage. In addition, smoking habit and excessive alcohol consumption have been estimated to be the main risk factors. Although the association between OSCC and genetic susceptibility loci has been observed in many different populations, most of these studies simply focused on the single nucleotide polymorphism. Therefore, we made a contrast analysis between the 112 OSCC patients from the GEO database and 245 normal samples from the HapMap project. First, we performed a genome-wide haplotype association study by comparing the frequency of the haplotypes in the case–control experiment. Then, we mapped the haplotypes to the corresponding genes, screened the risk genes according to significant haplotypes (P < 1E−04), and prioritized the OSCC genes based on their similarity to the known OSCC susceptibility genes. We filtered four OSCC genes including SERPINB9, SERPINE2, GAK, and HSP90B1 through the gene global prioritization score (P < 0.005). SERPINB9 ranked first in the candidate gene list and contained a significant haplotype TAGGA (P value = 3.12E−11). The second risk gene was SERPINE2 with the haplotype GGGCCCTTT, which was closely similar to the SERPINB9.


OSCC Haplotype Association study Risk gene 



This work was supported in part by grants from the National Natural Science Foundation of China (grant numbers 31200934, 61300116, and 81300945) and the Natural Science Foundation of Heilongjiang Province (grant numbers C201206 and QC2013C063).

Conflicts of interest



  1. 1.
    Sterenczak KA, Eckardt A, Kampmann A, Willenbrock S, Eberle N, Länger F, et al. HMGA1 and HMGA2 expression and comparative analyses of HMGA2, Lin28 and let-7 miRNAs in oral squamous cell carcinoma. BMC Cancer. 2014;14:694.CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Peng C-H, Liao C-T, Peng S-C, Chen Y-J, Cheng A-J, Juang J-L, et al. A novel molecular signature identified by systems genetics approach predicts prognosis in oral squamous cell carcinoma. PLoS One. 2011;6:e23452.CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Khor GH, Froemming G, Zain RB, Abraham MT, Thong KL. Screening of differential promoter hypermethylated genes in primary oral squamous cell carcinoma. Asian Pacific J Cancer Prev: APJCP. 2014;15:8957.CrossRefGoogle Scholar
  4. 4.
    Basnaker M, Sr S, Bnvs S. Expression of endoglin (CD-105) and microvessel density in oral dysplasia and squamous cell carcinoma. J Clin Diagnostic Res: JCDR. 2014;8:ZC91–94.Google Scholar
  5. 5.
    Barrett T, Edgar R. Gene expression omnibus: microarray data storage, submission, retrieval, and analysis. Methods Enzymol. 2006;411:352–69.CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Montgomery DC: Introduction to statistical quality control. John Wiley & Sons, 2007Google Scholar
  7. 7.
    Nyholt DR. A simple correction for multiple testing for single-nucleotide polymorphisms in linkage disequilibrium with each other. Am J Hum Genet. 2004;74:765–9.CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Wang N, Akey JM, Zhang K, Chakraborty R, Jin L. Distribution of recombination crossovers and the origin of haplotype blocks: the interplay of population history, recombination, and mutation. Am J Hum Genet. 2002;71:1227–34.CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Barrett JC, Fry B, Maller J, Daly MJ. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics. 2005;21:263–5.CrossRefPubMedGoogle Scholar
  10. 10.
    Barrett JC. Haploview: visualization and analysis of SNP genotype data. Cold Spring Harbor Protocols 2009;2009:pdb. ip71.Google Scholar
  11. 11.
    Lesch K-P, Timmesfeld N, Renner TJ, Halperin R, Röser C, Nguyen TT, et al. Molecular genetics of adult ADHD: converging evidence from genome-wide association and extended pedigree linkage studies. J Neural Transm. 2008;115:1573–85.CrossRefPubMedGoogle Scholar
  12. 12.
    Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA. Online mendelian inheritance in man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 2005;33:D514–7.CrossRefPubMedGoogle Scholar
  13. 13.
    Aerts S, Lambrechts D, Maity S, Van Loo P, Coessens B, De Smet F, et al. Gene prioritization through genomic data fusion. Nat Biotechnol. 2006;24:537–44.CrossRefPubMedGoogle Scholar
  14. 14.
    Turner FS, Clutterbuck DR, Semple CA. Pocus: mining genomic sequence annotation to predict disease genes. Genome Biol. 2003;4:R75.CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Altermann E, Klaenhammer TR. Pathwayvoyager: pathway mapping using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. BMC Genomics. 2005;6:60.CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Beaglehole JC, Banks J. The ‘Endeavour’ journal of Joseph Banks 1768–1771. Angus & Robertson Limited, 1962.Google Scholar
  17. 17.
    Goodridge D, Sloan J, LeDoyen Y, McKenzie J, Knight W, Gayari M. Risk-assessment scores, prevention strategies, and the incidence of pressure ulcers among the elderly in four canadian health-care facilities. Can J Nursing Res= Revue canadienne de Recherche en Sci Infirmieres. 1997;30:23–44.Google Scholar
  18. 18.
    Nobrega MA, Ovcharenko I, Afzal V, Rubin EM. Scanning human gene deserts for long-range enhancers. Science. 2003;302:413.CrossRefPubMedGoogle Scholar
  19. 19.
    Fan J-B, Oliphant A, Shen R, Kermani B, Garcia F, Gunderson K, et al. Highly parallel SNP genotyping: Cold Spring Harbor symposia on quantitative biology. Cold Spring Harbor Laboratory Press. 2003;68:69–78.CrossRefGoogle Scholar
  20. 20.
    Mayr E. Change of genetic environment and evolution. 1954.Google Scholar
  21. 21.
    Tettelin H, Masignani V, Cieslewicz MJ, Donati C, Medini D, Ward NL, et al. Genome analysis of multiple pathogenic isolates of Streptococcus agalactiae: implications for the microbial “pan-genome”. Proc Natl Acad Sci U S A. 2005;102:13950–5.CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Silverman GA, Bird PI, Carrell RW, Coughlin PB, Gettins PG, Irving JI, et al. The serpins are an expanding superfamily of structurally similar but funtionally diverse proteins: evolution, mechanism of inhibition, novel functions, and a revised nomenclature. J Biol Chem. 2001.Google Scholar
  23. 23.
    Celli B, MacNee W, Agusti A, Anzueto A, Berg B, Buist A, et al. Standards for the diagnosis and treatment of patients with copd: a summary of the ATS/ERS position paper. Eur Respir J. 2004;23:932–46.CrossRefPubMedGoogle Scholar

Copyright information

© International Society of Oncology and BioMarkers (ISOBM) 2015

Authors and Affiliations

  1. 1.College of Bioinformatics Science and TechnologyHarbin Medical UniversityHarbinChina

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