Literature DB >> 12647995

Gene expression in cancer: the application of microarrays.

Pascale F Macgregor1.   

Abstract

Genome-wide monitoring of gene expression using DNA microarrays represents one of the latest breakthroughs in experimental molecular biology and provides unprecedented opportunity to explore the biological processes underlying human diseases by providing a comprehensive survey of a cell's transcriptional landscape. In the cancer field, this revolutionary technology allows the simultaneous assessment of the transcription of tens of thousands of genes, and of their relative expression between normal cells and malignant cells. As microarray analysis emerges from its infancy, there is widespread hope that microarrays will significantly impact on our ability to explore the genetic changes associated with cancer etiology and development, and ultimately lead to the discovery of new biomarkers for disease diagnosis and prognosis prediction, and of new therapeutic tools. This review provides an overview of microarray technology, specifically in the context of cancer research and describes some of its recent applications to the study of cancer. In addition, the challenges of translating microarray findings into molecular cancer diagnosis and prognosis tools, with the potential of altering clinical practice through individualized cancer care and ultimately of contributing to the battle against cancer, are discussed.

Entities:  

Mesh:

Year:  2003        PMID: 12647995     DOI: 10.1586/14737159.3.2.185

Source DB:  PubMed          Journal:  Expert Rev Mol Diagn        ISSN: 1473-7159            Impact factor:   5.225


  6 in total

1.  Accelerating drug discovery.

Authors:  Sandra Kraljevic; Peter J Stambrook; Kresimir Pavelic
Journal:  EMBO Rep       Date:  2004-09       Impact factor: 8.807

Review 2.  Pediatric pharmacogenetic and pharmacogenomic studies: the current state and future perspectives.

Authors:  Roberta Russo; Mario Capasso; Paolo Paolucci; Achille Iolascon
Journal:  Eur J Clin Pharmacol       Date:  2010-11-11       Impact factor: 2.953

3.  Transferring genomics to the clinic: distinguishing Burkitt and diffuse large B cell lymphomas.

Authors:  Chulin Sha; Sharon Barrans; Matthew A Care; David Cunningham; Reuben M Tooze; Andrew Jack; David R Westhead
Journal:  Genome Med       Date:  2015-07-01       Impact factor: 11.117

4.  MetaGxData: Clinically Annotated Breast, Ovarian and Pancreatic Cancer Datasets and their Use in Generating a Multi-Cancer Gene Signature.

Authors:  Deena M A Gendoo; Michael Zon; Vandana Sandhu; Venkata S K Manem; Natchar Ratanasirigulchai; Gregory M Chen; Levi Waldron; Benjamin Haibe-Kains
Journal:  Sci Rep       Date:  2019-06-19       Impact factor: 4.379

5.  A neural network model for constructing endophenotypes of common complex diseases: an application to male young-onset hypertension microarray data.

Authors:  Ke-Shiuan Lynn; Li-Lan Li; Yen-Ju Lin; Chiuen-Huei Wang; Shu-Hui Sheng; Ju-Hwa Lin; Wayne Liao; Wen-Lian Hsu; Wen-Harn Pan
Journal:  Bioinformatics       Date:  2009-02-23       Impact factor: 6.937

6.  Indirect genomic effects on survival from gene expression data.

Authors:  Egil Ferkingstad; Arnoldo Frigessi; Heidi Lyng
Journal:  Genome Biol       Date:  2008-03-22       Impact factor: 13.583

  6 in total

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