Literature DB >> 20306232

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

Ki-Yeol Kim1, Jin Kim, Hyung Jun Kim, Woong Nam, In-Ho Cha.   

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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Year:  2010        PMID: 20306232     DOI: 10.1007/s11517-010-0595-0

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN: 0140-0118            Impact factor:   2.602


  27 in total

Review 1.  Comparative genomic hybridization.

Authors:  Daniel Pinkel; Donna G Albertson
Journal:  Annu Rev Genomics Hum Genet       Date:  2005       Impact factor: 8.929

2.  A faster circular binary segmentation algorithm for the analysis of array CGH data.

Authors:  E S Venkatraman; Adam B Olshen
Journal:  Bioinformatics       Date:  2007-01-18       Impact factor: 6.937

3.  CGHweb: a tool for comparing DNA copy number segmentations from multiple algorithms.

Authors:  Weil Lai; Vidhu Choudhary; Peter J Park
Journal:  Bioinformatics       Date:  2008-02-22       Impact factor: 6.937

4.  A fast and flexible method for the segmentation of aCGH data.

Authors:  Erez Ben-Yaacov; Yonina C Eldar
Journal:  Bioinformatics       Date:  2008-08-15       Impact factor: 6.937

Review 5.  Copy number variation: new insights in genome diversity.

Authors:  Jennifer L Freeman; George H Perry; Lars Feuk; Richard Redon; Steven A McCarroll; David M Altshuler; Hiroyuki Aburatani; Keith W Jones; Chris Tyler-Smith; Matthew E Hurles; Nigel P Carter; Stephen W Scherer; Charles Lee
Journal:  Genome Res       Date:  2006-06-29       Impact factor: 9.043

6.  The role of epigenetics in cancer. DNA Methylation, Imprinting and the Epigenetics of Cancer--an American Association for Cancer Research Special Conference. Las Croabas, Puerto Rico, 12-16 1997 December.

Authors:  C Lengauer; J P Issa
Journal:  Mol Med Today       Date:  1998-03

7.  Identification of homozygous deletions of tumor suppressor gene FAT in oral cancer using CGH-array.

Authors:  K Nakaya; H D Yamagata; N Arita; K-I Nakashiro; M Nose; T Miki; H Hamakawa
Journal:  Oncogene       Date:  2007-02-26       Impact factor: 9.867

8.  Array-comparative genomic hybridization to detect genomewide changes in microdissected primary and metastatic oral squamous cell carcinomas.

Authors:  Chung-Ji Liu; Shu-Chun Lin; Yann-Jang Chen; Kuo-Ming Chang; Kuo-Wei Chang
Journal:  Mol Carcinog       Date:  2006-10       Impact factor: 4.784

9.  Identification of p53 regulators by genome-wide functional analysis.

Authors:  Qihong Huang; Angel Raya; Paul DeJesus; Sheng-Hao Chao; Kim C Quon; Jeremy S Caldwell; Sumit K Chanda; Juan C Izpisua-Belmonte; Peter G Schultz
Journal:  Proc Natl Acad Sci U S A       Date:  2004-02-27       Impact factor: 11.205

10.  Improving the prediction accuracy in classification using the combined data sets by ranks of gene expressions.

Authors:  Ki-Yeol Kim; Dong Hyuk Ki; Hei-Cheul Jeung; Hyun Cheol Chung; Sun Young Rha
Journal:  BMC Bioinformatics       Date:  2008-06-16       Impact factor: 3.169

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  2 in total

1.  Low-complexity PDE-based approach for automatic microarray image processing.

Authors:  Bogdan Belean; Romulus Terebes; Adrian Bot
Journal:  Med Biol Eng Comput       Date:  2014-10-29       Impact factor: 2.602

Review 2.  Genome-wide approaches (GWA) in oral and craniofacial diseases research.

Authors:  H Kim; S Gordon; R Dionne
Journal:  Oral Dis       Date:  2012-08-23       Impact factor: 3.511

  2 in total

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