Functional genomics in translational cancer research: focus on breast cancer

Date

2008

Authors

Yulug, I. G.
Gur-Dedeoglu, B.

Editor(s)

Advisor

Supervisor

Co-Advisor

Co-Supervisor

Instructor

Source Title

Briefings in Functional Genomics and Proteomics

Print ISSN

1473-9550

Electronic ISSN

Publisher

Oxford University Press

Volume

7

Issue

1

Pages

1 - 7

Language

English

Journal Title

Journal ISSN

Volume Title

Series

Abstract

Conventional molecular and genetic methods for studying cancer are limited to the analysis of one locus at a time. A cluster of genes that are regulated together can be identified by DNA microarray, and the functional relationships can uncover new aspects of cancer biology. Breast cancer can be used to provide a model to demonstrate the current approaches to the molecular analysis of cancer. Meta-analysis is an important tool for the identification and validation of differentially expressed genes to increase power in clinical and biological studies across different sets of data. Recently, meta-analysis approaches have been applied to large collections of microarray datasets to investigate molecular commonalities of multiple cancer types not only to find the common molecular pathways in tumour development but also to compare the individual datasets to other cancer datasets to identify new sets of genes. Several investigators agree that microarray results should be validated. One commonly used method is quantitative reverse transcription PCR (qRT-PCR) to validate the expression profiles of the target genes obtained through microarray experiments. qRT-PCR is attractive for clinical use, since it can be automated and performed on fresh or archived formalin-fixed, paraffin-embedded tissue samples. The outcome of these analyses might accelerate the application of basic research findings into daily clinical practice through translational research and may have an impact on foreseeing the clinical outcome, predicting tumour response to specific therapy, identification of new prognostic biomarkers, discovering targets for the development of novel therapies and providing further insights into tumour biology. © The Author 2008. Published by Oxford University Press.

Course

Other identifiers

Book Title

Citation