Title

A prognostic model for the combined analysis of gene expression profiling in hepatocellular carcinoma

Authors

Zee Chung Ying Benny^{1, *}, Lee Jock Wai Jack¹, Wong Nathalie², Yeo Winnie³, Lai Bo San Paul⁴, Mok Shu Kam Tony³, Chan Tak Cheung Anthony³

Affiliation

¹Centre for Clinical Trials, School of Public Health,; ²Department of Anatomic and Cellular Pathology; ³Department of Clinical Oncology; ⁴Department of Surgery, Chinese University of Hong Kong, Shatin, Hong Kong SAR

Email

bzee@cct.cuhk.edu.hk; * Corresponding author

Article Type

Hypothesis

Date

received February 26, 2008; revised March 07, 2008; accepted May 14, 2008; published July 14, 2008

Abstract

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.

Keywords

wavelets; generalized singular decomposition; Cox-model; HCC

Citation

Benny et al., Bioinformation 2(9): 395-400 (2008)

Edited by

P. Kangueane

ISSN

0973-2063

Publisher

Biomedical Informatics

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.