PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Gene expression and and copy number profiling suggests the importance of allelic imbalance in 19p in asbestos-associated lung cancer
H. Wikman, S. Ruosaari, P. Nymark, V.K. Sarhadi, J. Saharinen, E. Vanhala, A. Karjalainen, Jaakko Hollmen, S. Anttila and S. Knuutila
Oncogene Volume 26, Number 32, pp. 4730-4737, 2007.

Abstract

Asbestos is a pulmonary carcinogen known to give rise to DNA and chromosomal damage, but the exact carcinogenic mechanisms are still largely unknown. In this study, gene expression arrays were performed on lung tumor samples from 14 heavily asbestos-exposed and 14 non-exposed patients matched for other characteristics. Using a two-step statistical analysis, 47 genes were revealed that could differentiate the tumors of asbestos-exposed from those of non-exposed patients. To identify asbestos-associated regions with DNA copy number and expressional changes, the gene expression data were combined with comparative genomic hybridization microarray data. As a result, a combinatory profile of DNA copy number aberrations and expressional changes significantly associated with asbestos exposure was obtained. Asbestos-related areas were detected in 2p21–p16.3, 3p21.31, 5q35.2–q35.3, 16p13.3, 19p13.3–p13.1 and 22q12.3–q13.1. The most prominent of these, 19p13, was further characterized by microsatellite analysis in 62 patients for the differences in allelic imbalance (AI) between the two groups of lung tumors. 79% of the exposed and 45% of the non-exposed patients (P=0.008) were found to be carriers of AI in their lung tumors. In the exposed group, AI in 19p was prevalent regardless of the histological tumor type. In adenocarcinomas, AI in 19p appeared to occur independently of the asbestos exposure.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:3606
Deposited By:Jaakko Hollmen
Deposited On:13 February 2008