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Curated Journal Articles on Mesothelioma

Identification of Novel Candidate Oncogenes and Tumor Suppressors in Malignant Pleural Mesothelioma Using Large-Scale Transcriptional Profiling

The American Journal of Pathology. 2005;166:1827-1840. [Link]

Gavin J. Gordon,a Graham N. Rockwell,b Roderick V. Jensen,c James G. Rheinwald,d Jonathan N. Glickman,e Joshua P. Aronson,a Brian J. Pottorf,a Matthew D. Nitz,a William G. Richards,a David J. Sugarbaker,a and Raphael Buenoa

aThe Thoracic Surgery Oncology Laboratory (www.generatios.com, ) and the Division of Thoracic Surgery, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., Boston, Massachusetts 02115 USA
bDepartment of Neurology, Brigham and Women’s Hospital, Harvard Medical School, 65 Landsdowne St. Cambridge, Massachusetts 02139 USA
cDepartment of Physics, Wesleyan University, Middletown, Connecticut 06457 USA
dDepartment of Dermatology, Brigham and Women’s Hospital, Harvard Medical School, 77 Avenue Louis Pasteur, Boston, Massachusetts 02115 USA
eDepartment of Pathology, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis St., Boston, Massachusetts 02115 USA

Abstract

Malignant pleural mesothelioma (MPM) is a highly lethal, poorly understood neoplasm that is typically associated with asbestos exposure. We performed transcriptional profiling using high-density oligonucleotide microarrays containing 22,000 genes to elucidate potential molecular and pathobiological pathways in MPM using discarded human MPM tumor specimens (n = 40), normal lung specimens (n = 4), normal pleura specimens (n = 5), and MPM and SV40-immortalized mesothelial cell lines (n = 5). In global expression analysis using unsupervised clustering techniques, we found two potential subclasses of mesothelioma that correlated loosely with tumor histology. We also identified sets of genes with expression levels that distinguish between multiple tumor subclasses, normal and tumor tissues, and tumors with different morphologies. Microarray gene expression data were confirmed using quantitative reverse transcriptase-polymerase chain reaction and protein analysis for three novel candidate oncogenes (NME2, CRI1, and PDGFC) and one candidate tumor suppressor (GSN). Finally, we used bioinformatics tools (ie, software) to create and explore complex physiological pathways. Combined, all of these data may advance our understanding of mesothelioma tumorigenesis, pathobiology, or both.

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