Literature DB >> 12044926

DNA microarrays for assessing ovarian cancer gene expression.

Izhak Haviv1, Ian G Campbell.   

Abstract

Although DNA microarray analysis is presented as a revolution in gene expression studies, it is in fact based on the classic technique of Southern DNA hybridisation where a labelled DNA probe is hybridised to single stranded DNA that is bound to a solid support matrix. The truly revolutionary aspect of microarray analysis lies in the fact that, within a given cell population, the expression of tens of thousands of genes, and ultimately the entire genome, can be assayed simultaneously. This capability, when coupled with powerful data analysis software, allows researchers to rapidly compare gene expression between two cell populations. In the cancer field, this enables researchers to compare gene expression between normal and malignant cells and to identify genes that are differentially regulated during cancer development. Microarray data can also be used to categorize tumours on the basis of their molecular profile, which may provide important biological, diagnostic and prognostic information. As little as 5 years ago identifying even a few differentially expressed genes may have taken several years and cost tens of thousands of dollars. Today microarrays can identify ten times the number of candidate genes in just a few months and at a tenth of the cost. Even so, microarray analysis is still in its infancy and the technology is advancing rapidly. There is little doubt that microarrays will revolutionize our ability to quantify the complex changes that occur in gene expression during cancer development. The greatest challenge that lies ahead is how to translate this knowledge into clinically useful diagnostic and therapeutic tools. In this review, we describe the technical aspects of DNA microarray analysis and some of the current and future applications of this technology for analysing gene expression in ovarian cancer.

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Year:  2002        PMID: 12044926     DOI: 10.1016/s0303-7207(02)00063-1

Source DB:  PubMed          Journal:  Mol Cell Endocrinol        ISSN: 0303-7207            Impact factor:   4.102


  4 in total

1.  Gene expression patterns in ovarian carcinomas.

Authors:  Marci E Schaner; Douglas T Ross; Giuseppe Ciaravino; Therese Sorlie; Olga Troyanskaya; Maximilian Diehn; Yan C Wang; George E Duran; Thomas L Sikic; Sandra Caldeira; Hanne Skomedal; I-Ping Tu; Tina Hernandez-Boussard; Steven W Johnson; Peter J O'Dwyer; Michael J Fero; Gunnar B Kristensen; Anne-Lise Borresen-Dale; Trevor Hastie; Robert Tibshirani; Matt van de Rijn; Nelson N Teng; Teri A Longacre; David Botstein; Patrick O Brown; Branimir I Sikic
Journal:  Mol Biol Cell       Date:  2003-09-05       Impact factor: 4.138

2.  Changes in gene expression associated with reproductive maturation in wild female baboons.

Authors:  Courtney C Babbitt; Jenny Tung; Gregory A Wray; Susan C Alberts
Journal:  Genome Biol Evol       Date:  2011-12-08       Impact factor: 3.416

3.  Variation in gene expression patterns in effusions and primary tumors from serous ovarian cancer patients.

Authors:  Marci E Schaner; Ben Davidson; Martina Skrede; Reuven Reich; Vivi Ann Flørenes; Björn Risberg; Aasmund Berner; Iris Goldberg; Vered Givant-Horwitz; Claes G Tropè; Gunnar B Kristensen; Jahn M Nesland; Anne-Lise Børresen-Dale
Journal:  Mol Cancer       Date:  2005-07-21       Impact factor: 27.401

4.  Sensitive detection of SARS coronavirus RNA by a novel asymmetric multiplex nested RT-PCR amplification coupled with oligonucleotide microarray hybridization.

Authors:  Zhi-wei Zhang; Yi-ming Zhou; Yan Zhang; Yong Guo; Sheng-ce Tao; Ze Li; Qiong Zhang; Jing Cheng
Journal:  Methods Mol Med       Date:  2005
  4 in total

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