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A method for detecting significant genomic regions associated with oral squamous cell carcinoma using aCGH

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Abstract

Array comparative genomic hybridization (aCGH) provides a genome-wide technique for identifying chromosomal aberrations in human diseases, including cancer. Chromosomal aberrations in cancers are defined as regions that contain an increased or decreased DNA copy number, relative to normal samples. The identification of genomic regions associated with systematic aberrations provides insights into initiation and progression of cancer, and improves diagnosis, prognosis, and therapy strategies. The McNemar test can be used to detect differentially expressed genes after discretization of gene expressions in a microarray experiment for the matched dataset. In this study, we propose a method to detect significantly altered DNA regions, shifted McNemar test, which is based on the standard McNemar test and takes into account changes in copy number variations and the region size throughout the whole genome. In addition, this novel method can be used to detect genomic regions associated with the progress of oral squamous cell carcinoma (OSCC). The performance of the proposed method was evaluated based on the homogeneity within the selected regions and the classification accuracies of the selected regions. This method might be useful for identifying new candidate genes that neighbor known genes based on the whole-genomic variation because it detects significant chromosomal regions, not independent probes.

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References

  1. Bennett BM, Underwood RE (1970) On McNemar’s test for the 2 × 2 table and its power function. Biometrics 26:339–343

    Article  Google Scholar 

  2. Ben-Yaacov E, Eldar YC (2008) A fast and flexible method for the segmentation of aCGH data. Bioinformatics 24(16):i139–i145

    Article  Google Scholar 

  3. Chen HI, Hsu FH, Jiang Y, Tsai MH, Yang PC, Meltzer PS, Chuang EY, Chen Y (2008) A probe-density-based analysis method for array CGH data: simulation, normalization and centralization. Bioinformatics 24(16):1749–1756

    Article  Google Scholar 

  4. Chien CY, Su CY, Chuang HC, Fang FM, Huang HY, Chen CM, Chen CH, Huang CC (2008) Angiopoietin-1 and -2 expression in recurrent squamous cell carcinoma of the oral cavity. J Surg Oncol 97(3):273–277

    Article  Google Scholar 

  5. Eilers PH, de Menezes RX (2005) Quantile smoothing of array CGH data. Bioinformatics 21(7):1146–1153

    Article  Google Scholar 

  6. Freeman JL, Perry GH, Feuk L, Redon R, McCarroll SA, Altshuler DM, Aburatani H, Jones KW, Tyler-Smith C, Hurles ME et al (2006) Copy number variation: new insights in genome diversity. Genome Res 16(8):949–961

    Article  Google Scholar 

  7. Garnis C, Campbell J, Zhang L, Rosin MP, Lam WL (2004) OCGR array: an oral cancer genomic regional array for comparative genomic hybridization analysis. Oral Oncol 40(5):511–519

    Article  Google Scholar 

  8. Huang Q, Raya A, DeJesus P, Chao SH, Quon KC, Caldwell JS, Chanda SK, Izpisua-Belmonte JC, Schultz PG (2004) Identification of p53 regulators by genome-wide functional analysis. Proc Natl Acad Sci USA 101(10):3456–3461

    Article  Google Scholar 

  9. Huang J, Gusnanto A, O’Sullivan K, Staaf J, Borg A, Pawitan Y (2007) Robust smooth segmentation approach for array CGH data analysis. Bioinformatics 23(18):2463–2469

    Article  Google Scholar 

  10. Hupe P, Stransky N, Thiery JP, Radvanyi F, Barillot E (2004) Analysis of array CGH data: from signal ratio to gain and loss of DNA regions. Bioinformatics 20(18):3413–3422

    Article  Google Scholar 

  11. Katoh M (2002) Molecular cloning and characterization of OSR1 on human chromosome 2p24. Int J Mol Med 10(2):221–225

    Google Scholar 

  12. Kim KY, Ki DH, Jeung HC, Chung HC, Rha SY (2008) Improving the prediction accuracy in classification using the combined data sets by ranks of gene expressions. BMC Bioinformatics 9:283

    Article  Google Scholar 

  13. Lai W, Choudhary V, Park PJ (2008) CGHweb: a tool for comparing DNA copy number segmentations from multiple algorithms. Bioinformatics 24(7):1014–1015

    Article  Google Scholar 

  14. Lengauer C, Issa JP (1998) The role of epigenetics in cancer. DNA methylation, imprinting, the epigenetics of cancer—an American Association for Cancer Research Special Conference. Las Croabas, Puerto Rico, 12–16 1997 December. Mol Med Today 4(3):102–103

    Article  Google Scholar 

  15. Li Y, Zhu J (2007) Analysis of array CGH data for cancer studies using fused quantile regression. Bioinformatics 23(18):2470–2476

    Article  Google Scholar 

  16. Li C, Feng HC, Chen JC, Song YF (2005) Expression and significance of angiopoietin-1 and angiopoietin-2 in oral squamous cell cacinoma. Ai Zheng 24(11):1388–1393

    Google Scholar 

  17. Liu J, Mohammed J, Carter J, Ranka S, Kahveci T, Baudis M (2006) Distance-based clustering of CGH data. Bioinformatics 22(16):1971–1978

    Article  Google Scholar 

  18. Liu CJ, Lin SC, Chen YJ, Chang KM, Chang KW (2006) Array-comparative genomic hybridization to detect genome wide changes in microdissected primary and metastatic oral squamous cell carcinomas. Mol Carcinog 45(10):721–731

    Article  Google Scholar 

  19. Liu J, Ranka S, Kahveci T (2008) Classification and feature selection algorithms for multi-class CGH data. Bioinformatics 24(13):i86–i95

    Article  Google Scholar 

  20. McNemar Q (1947) Note on the sampling error of the differences between correlated proportions or percentages. Psychometrika 12:53–157

    Article  Google Scholar 

  21. Nakaya K, Yamagata HD, Arita N, Nakashiro KI, Nose M, Miki T, Hamakawa H (2007) Identification of homozygous deletions of tumor suppressor gene FAT in oral cancer using CGH-array. Oncogene 26(36):5300–5308

    Article  Google Scholar 

  22. O’Regan EM, Toner ME, Smyth PC, Finn SP, Timon C, Cahill S, Flavin R, O’Leary JJ, Sheils O (2006) Distinct array comparative genomic hybridization profiles in oral squamous cell carcinoma occurring in young patients. Head Neck 28(4):330–338

    Article  Google Scholar 

  23. Pinkel D, Albertson DG (2005) Comparative genomic hybridization. Annu Rev Genomics Hum Genet 6:331–354

    Article  Google Scholar 

  24. Shah SP, Xuan X, DeLeeuw RJ, Khojasteh M, Lam WL, Ng R, Murphy KP (2006) Integrating copy number polymorphisms into array CGH analysis using a robust HMM. Bioinformatics 22(14):e431–e439

    Article  Google Scholar 

  25. Squire JA, Bayani J, Luk C, Unwin L, Tokunaga J, MacMillan C, Irish J, Brown D, Gullane P, Kamel-Reid S (2002) Molecular cytogenetic analysis of head and neck squamous cell carcinoma: by comparative genomic hybridization, spectral karyotyping, and expression array analysis. Head Neck 24(9):874–887

    Article  Google Scholar 

  26. Suzuki E, Imoto I, Pimkhaokham A, Nakagawa T, Kamata N, Kozaki KI, Amagasa T, Inazawa J (2007) PRTFDC1, a possible tumor-suppressor gene, is frequently silenced in oral squamous-cell carcinomas by aberrant promoter hypermethylation. Oncogene 26(57):7921–7932

    Article  Google Scholar 

  27. Venkatraman ES, Olshen AB (2007) A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 23(6):657–663

    Article  Google Scholar 

  28. Watabe K, Ito A, Asada H, Endo Y, Kobayashi T, Nakamoto K, Itami S, Takao S, Shinomura Y, Aikou T et al (2001) Structure, expression and chromosome mapping of MLZE, a novel gene which is preferentially expressed in metastatic melanoma cells. Jpn J Cancer Res 92(2):140–151

    Google Scholar 

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Acknowledgments

This work was supported by Priority Research Centers Program through the National Research Foundation of Korea(NRF), funded by the Ministry of Education, Science and Technology (2009-0094030).

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Authors and Affiliations

  1. Oral Cancer Research Institute, College of Dentistry, Yonsei University, Seoul, 120-752, Republic of Korea

    Ki-Yeol Kim, Jin Kim & In-Ho Cha

  2. Department of Oral and Maxillofacial Surgery, College of Dentistry, Yonsei University, Seoul, 120-752, Republic of Korea

    Hyung Jun Kim, Woong Nam & In-Ho Cha

Authors
  1. Ki-Yeol Kim
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  2. Jin Kim
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  3. Hyung Jun Kim
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  4. Woong Nam
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  5. In-Ho Cha
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Correspondence to In-Ho Cha.

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Kim, KY., Kim, J., Kim, H.J. et al. A method for detecting significant genomic regions associated with oral squamous cell carcinoma using aCGH. Med Biol Eng Comput 48, 459–468 (2010). https://doi.org/10.1007/s11517-010-0595-0

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  • DOI: https://doi.org/10.1007/s11517-010-0595-0

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