A prognostic model for the combined analysis of gene expression profiling in hepatocellular carcinoma.
Microarray techniques using cDNA array and comparative genomic hybridization (CGH) have been developed for several discovery applications. They are frequently applied for the prediction and diagnosis of cancer in recent years. Many studies have shown that integrating genomic data from different sources may increase the reliability of gene expression analysis results in understanding cancer progression. Therefore, developing a good prognostic model dealing simultaneously with different types of dataset is important. The challenge with these types of data is high background noise. We describe an analytical two-stage framework with a multi-parallel data analysis method named wavelet-based generalized singular value decomposition and shaving method (WGSVD-shaving). This method is proposed for de-noising and dimension-reduction during early stage prognosis modeling. We also applied a supervised gene clustering technique with penalized logistic regression with Cox-model on an integrated data. We show the accuracy of the method using a simulated dataset with a case study on Hepatocelluar Carcinoma (HCC) cDNA and CGH data. The method shows improved results from GSVD-shaving and has application in the discovery of candidate genes associated with cancer.
Benny
Zee Chung Ying
ZC
Centre for Clinical Trials, School of Public Health, Chinese University of Hong Kong, Shatin, Hong Kong SAR. bzee@cct.cuhk.edu.hk
Jack
Lee Jock Wai
LJ
Nathalie
Wong
W
Winnie
Yeo
Y
Paul
Lai Bo San
LB
Tony
Mok Shu Kam
MS
Anthony
Chan Tak Cheung
CT
J Pathol. 1999 Mar;187(4):385-95
10398096
Nat Med. 2001 Jun;7(6):673-9
11385503
Nat Med. 2002 Aug;8(8):816-24
12118244
Am J Pathol. 1998 May;152(5):1107-23
9588877
Proc Natl Acad Sci U S A. 2002 Oct 1;99(20):12963-8
12297621
Biostatistics. 2005 Jan;6(1):45-58
15618527
Genes Chromosomes Cancer. 1994 Aug;10(4):231-43
7522536
Cancer Genet Cytogenet. 2006 Apr 1;166(1):56-64
16616112
Nature. 2002 Jan 24;415(6870):436-42
11807556