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Gene expression profiling for molecular distinction and characterization of laser captured primary lung cancers

Overview of attention for article published in Journal of Translational Medicine, November 2008
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Title
Gene expression profiling for molecular distinction and characterization of laser captured primary lung cancers
Published in
Journal of Translational Medicine, November 2008
DOI 10.1186/1479-5876-6-69
Pubmed ID
Authors

Astrid Rohrbeck, Judith Neukirchen, Michael Rosskopf, Guillermo G Pardillos, Helene Geddert, Andreas Schwalen, Helmut E Gabbert, Arndt von Haeseler, Gerald Pitschke, Matthias Schott, Ralf Kronenwett, Rainer Haas, Ulrich-Peter Rohr

Abstract

We examined gene expression profiles of tumor cells from 29 untreated patients with lung cancer (10 adenocarcinomas (AC), 10 squamous cell carcinomas (SCC), and 9 small cell lung cancer (SCLC)) in comparison to 5 samples of normal lung tissue (NT). The European and American methodological quality guidelines for microarray experiments were followed, including the stipulated use of laser capture microdissection for separation and purification of the lung cancer tumor cells from surrounding tissue. Based on differentially expressed genes, different lung cancer samples could be distinguished from each other and from normal lung tissue using hierarchical clustering. Comparing AC, SCC and SCLC with NT, we found 205, 335 and 404 genes, respectively, that were at least 2-fold differentially expressed (estimated false discovery rate: < 2.6%). Different lung cancer subtypes had distinct molecular phenotypes, which also reflected their biological characteristics. Differentially expressed genes in human lung tumors which may be of relevance in the respective lung cancer subtypes were corroborated by quantitative real-time PCR. Genetic programming (GP) was performed to construct a classifier for distinguishing between AC, SCC, SCLC, and NT. Forty genes, that could be used to correctly classify the tumor or NT samples, have been identified. In addition, all samples from an independent test set of 13 further tumors (AC or SCC) were also correctly classified. The data from this research identified potential candidate genes which could be used as the basis for the development of diagnostic tools and lung tumor type-specific targeted therapies.

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Mendeley readers

The data shown below were compiled from readership statistics for 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 5%
Spain 2 3%
United Kingdom 1 2%
Italy 1 2%
Unknown 51 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 29%
Student > Master 9 16%
Student > Ph. D. Student 9 16%
Professor > Associate Professor 5 9%
Student > Bachelor 3 5%
Other 5 9%
Unknown 10 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 15 26%
Computer Science 11 19%
Medicine and Dentistry 9 16%
Biochemistry, Genetics and Molecular Biology 5 9%
Engineering 2 3%
Other 4 7%
Unknown 12 21%