Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Expression data from lung adenocarcinoma

ABSTRACT: Affymetrix Human Genome U133A platform was used to obtain gene expression profiles of 28 pathologically and clinically well characterized adenocarcinomas of the lung. In addition, EGFR status was determined by fluorescent in situ hybridization and immunohistochemistry. The classification of the cases using a clustering algorithm applied to the expression data correlated significantly with the histopathological tumor grades. In contrast, no correlation of gene expression data and EGFR status could be detected. In this study we evaluated the gene expression of 28 clinico-pathological well characterized lung adenocarcinomas.

ORGANISM(S): Homo sapiens

SUBMITTER: Jens Neumann

PROVIDER: E-GEOD-17475 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Gene expression profiles of lung adenocarcinoma linked to histopathological grading and survival but not to EGF-R status: a microarray study.

Neumann Jens J Feuerhake Friedrich F Kayser Gian G Wiech Thorsten T Aumann Konrad K Passlick Bernward B Fisch Paul P Werner Martin M Zur Hausen Axel A

BMC cancer 20100302

<h4>Background</h4>Several different gene expression signatures have been proposed to predict response to therapy and clinical outcome in lung adenocarcinoma. Herein, we investigate if elements of published gene sets can be reproduced in a small dataset, and how gene expression profiles based on limited sample size relate to clinical parameters including histopathological grade and EGFR protein expression.<h4>Methods</h4>Affymetrix Human Genome U133A platform was used to obtain gene expression p ...[more]

PMID: 20196851

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Project description:Approximately 15% of lung cancer cases are not associated with smoking and show molecular and clinical characteristics distinct from those in smokers. Epidermal growth factor receptor (EGFR) gene mutations, which are correlated with sensitivity to EGFR-tyrosine kinase inhibitors (EGFR-TKIs), are more frequent in never-smoker lung cancers. In this study, microRNA (miRNA) expression profiling of 28 never-smoker lung cancer cases identified aberrantly expressed miRNAs, which were much fewer than in lung cancers of smokers and included miRNAs previously identified (e.g., upregulated miR-21) and unidentified (e.g., downregulated miR-138) in those smoker cases. The changes in expression of some of these miRNAs were more remarkable in cases with EGFR mutations than in those without: the most upregulated miRNA, miR-21, was more abundant in cancers with EGFR mutation. A significant correlation between phosphorylated-EGFR (p-EGFR) and miR-21 levels in lung carcinoma cell lines and the suppression of miR-21 by an EGFR-TKI, AG1478, suggested that the EGFR signaling pathway positively regulated miR-21 expression. In a never-smoker-derived lung adenocarcinoma cell line H3255 with mutant EGFR and high levels of p-EGFR and miR-21, antisense inhibition of miR-21 enhanced AG1478-induced apoptosis. In a never-smoker-derived adenocarcinoma cell line H441 with wild-type EGFR, the antisense miR-21 not only showed the additive effect with AG1478 but also induced apoptosis by itself. These results suggest that aberrantly increased expression of miR-21, which is further enhanced by the activated EGFR signaling pathway, plays a critical role in lung carcinogenesis in never-smokers and is a potential therapeutic target in both EGFR mutant and wild-type cases. Twenty-eight pairs of lung cancer tissues and corresponding noncancerous lung tissues were obtained from never-smokers who had undergone surgical resection from 2000 to 2004 at the University of Maryland Medical Center (n=15), Mayo Clinic (n=7) in United States and Hamamatsu University School of Medicine (n=6) in Japan.

Project description:INTRODUCTION: CDKN2A (p16) inactivation is common in lung cancer and occurs via homozygous deletions, methylation of promoter region, or point mutations. Although p16 promoter methylation has been linked to KRAS mutation and smoking, the associations between p16 inactivation mechanisms and other common genetic mutations and smoking status are still controversial or unknown. METHODS: We determined all three p16 inactivation mechanisms with the use of multiple methodologies for genomic status, methylation, RNA, and protein expression, and correlated them with EGFR, KRAS, STK11 mutations and smoking status in 40 cell lines and 45 tumor samples of primary non-small-cell lung carcinoma. We also performed meta-analyses to investigate the impact of smoke exposure on p16 inactivation. RESULTS: p16 inactivation was the major mechanism of RB pathway perturbation in non-small-cell lung carcinoma, with homozygous deletion being the most frequent method, followed by methylation and the rarer point mutations. Inactivating mechanisms were tightly correlated with loss of mRNA and protein expression. p16 inactivation occurred at comparable frequencies regardless of mutational status of EGFR, KRAS, and STK11, however, the major inactivation mechanism of p16 varied. p16 methylation was linked to KRAS mutation but was mutually exclusive with EGFR mutation. Cell lines and tumor samples demonstrated similar results. Our meta-analyses confirmed a modest positive association between p16 promoter methylation and smoking. CONCLUSION: Our results confirm that all the inactivation mechanisms are truly associated with loss of gene product and identify specific associations between p16 inactivation mechanisms and other genetic changes and smoking status. 40 NSCLC cell lines were profiled on Illumina WG6-V3 expression arrays. They consist of 4 groups of 10 cell lines each: non-smokers with EGFR mutation, non-smokers with EGFR wild-type, smokers with KRAS mutation, smokers with KRAS wild-type. Note that smoking status is not always clear.

Dataset Information

Expression data from lung adenocarcinoma

Publications

Gene expression profiles of lung adenocarcinoma linked to histopathological grading and survival but not to EGF-R status: a microarray study.

Similar Datasets

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