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Transcriptional Profiling of Non-small Cell Lung Cancer Using Oligonucleotide Microarrays* FREE TO VIEW

Gady Cojocaru, BsC; Nir Friedman, PhD; Meir Krupsky, MD; Penina Yaron, MsC; David Simansky, MD, FCCP; Alon Yellin, MD; Gideon Rechavi, MD; Yossef Barash, MsC; Amir Ben-Dor, PhD; Zohar Yakhini, PhD; Naftali Kaminski, MD
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*From Functional Genomics (Mr. Cojocaru and Dr. Simansky), Respiratory Medicine (Drs. Krupsky, Kaminski, and Ms. Yaron), Thoracic Surgery (Drs. Yellin and Simanski), and Pediatric Hemato-oncology (Dr. Rechavi), Sheba Medical Center, Tel Hashomer, Israel; Computer Sciences (Dr. Friedman and Mr. Barash), Hebrew University, Jerusalem, Israel; and Agilent Laboratories (Drs. Ben-Dor and Yakhini), Palo Alto, CA.

Correspondence to: Naftali Kaminski, MD, Functional Genomics Unit, Molecular Hemato-oncology and Institute of Respiratory Medicine, Hematology Lab Bldg, Room 202, Sheba Medical Center, Tel Hashomer 52621, Israel

Chest. 2002;121(3_suppl):44S. doi:10.1378/chest.121.3_suppl.44S
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Lung cancer is a common malignancy and a major determinant of overall cancer mortality in developed and developing countries. Despite intensive research, little has changed in the understanding and management of the disease. In order to determine the transcriptional programs that are active in non-small cell lung cancer, we analyzed gene expression patterns using GeneChip U95A microarrays (Affymetrix; Santa Clara, CA) that allow for the analysis of approximately 12,000 genes in 12 non-small cell lung cancer tumor samples, 6 normal histology samples from lung resections for cancer, and pooled normal lung RNA (five individual lungs) obtained commercially. Preliminary analysis revealed that gene expression patterns were highly distinct in tumor and normal tissues. Furthermore, hierarchical clustering clearly distinguished between normal and tumor samples. In order to determine the most informative genes in our data set, we implemented the total-number-of-misclassifications, information-content, and Gaussian-error scores. One evident observation was that informative genes were overabundant in our data set, thus supporting the significance of the results. Among the genes that were most significantly increased in the tumors, we distinguished several categories: genes probably related to cellular infiltrate, such as lymphocyte and macrophage restricted genes; genes clearly related to cancer, such as known oncogenes and cell cycle regulators; and extracellular matrix-related genes possibly representing fibrous tissue. In the genes that were decreased, a symmetrical but opposite trend was observed in cancer-related genes, with known tumor-suppressor genes and inhibitors of cell-cycle progression being decreased. The wealth of statistically significant and biologically meaningful information in our data set supports our contention that transcriptional profiling will lead to new insights into the pathogenesis of lung cancer, thus leading to development of new tools for early detection and treatment of this devastating disease.




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