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      Effects of cigarette smoke on the human airway epithelial cell transcriptome

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          Abstract

          Cigarette smoke is the major cause of lung cancer, the leading cause of cancer death, and of chronic obstructive pulmonary disease, the fourth leading cause of death in the United States. Using high-density gene expression arrays, we describe genes that are normally expressed in a subset of human airway epithelial cells obtained at bronchoscopy (the airway transcriptome), define how cigarette smoking alters the transcriptome, and detail the effects of variables, such as cumulative exposure, age, sex, and race, on cigarette smoke-induced changes in gene expression. We also determine which changes in gene expression are and are not reversible when smoking is discontinued. The persistent altered expression of a subset of genes in former smokers may explain the risk these individuals have for developing lung cancer long after they have discontinued smoking. The use of gene expression profiling to explore the normal biology of a specific subset of cells within a complex organ across a broad spectrum of healthy individuals and to define the reversible and irreversible genetic effects of cigarette smoke on human airway epithelial cells has not been previously reported.

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          Most cited references13

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          Classification of human lung carcinomas by mRNA expression profiling reveals distinct adenocarcinoma subclasses.

          We have generated a molecular taxonomy of lung carcinoma, the leading cause of cancer death in the United States and worldwide. Using oligonucleotide microarrays, we analyzed mRNA expression levels corresponding to 12,600 transcript sequences in 186 lung tumor samples, including 139 adenocarcinomas resected from the lung. Hierarchical and probabilistic clustering of expression data defined distinct subclasses of lung adenocarcinoma. Among these were tumors with high relative expression of neuroendocrine genes and of type II pneumocyte genes, respectively. Retrospective analysis revealed a less favorable outcome for the adenocarcinomas with neuroendocrine gene expression. The diagnostic potential of expression profiling is emphasized by its ability to discriminate primary lung adenocarcinomas from metastases of extra-pulmonary origin. These results suggest that integration of expression profile data with clinical parameters could aid in diagnosis of lung cancer patients.
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            Gene-expression profiles predict survival of patients with lung adenocarcinoma.

            Histopathology is insufficient to predict disease progression and clinical outcome in lung adenocarcinoma. Here we show that gene-expression profiles based on microarray analysis can be used to predict patient survival in early-stage lung adenocarcinomas. Genes most related to survival were identified with univariate Cox analysis. Using either two equivalent but independent training and testing sets, or 'leave-one-out' cross-validation analysis with all tumors, a risk index based on the top 50 genes identified low-risk and high-risk stage I lung adenocarcinomas, which differed significantly with respect to survival. This risk index was then validated using an independent sample of lung adenocarcinomas that predicted high- and low-risk groups. This index included genes not previously associated with survival. The identification of a set of genes that predict survival in early-stage lung adenocarcinoma allows delineation of a high-risk group that may benefit from adjuvant therapy.
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              Diversity of gene expression in adenocarcinoma of the lung.

              The global gene expression profiles for 67 human lung tumors representing 56 patients were examined by using 24,000-element cDNA microarrays. Subdivision of the tumors based on gene expression patterns faithfully recapitulated morphological classification of the tumors into squamous, large cell, small cell, and adenocarcinoma. The gene expression patterns made possible the subclassification of adenocarcinoma into subgroups that correlated with the degree of tumor differentiation as well as patient survival. Gene expression analysis thus promises to extend and refine standard pathologic analysis.
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                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proceedings of the National Academy of Sciences
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                July 06 2004
                July 06 2004
                June 21 2004
                July 06 2004
                : 101
                : 27
                : 10143-10148
                Article
                10.1073/pnas.0401422101
                454179
                15210990
                aba11357-e20b-46ae-b797-7e8974823761
                © 2004
                History

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