Gene Expression Signature of Cigarette Smoking and Its Role in Lung Adenocarcinoma Development and Survival
By M. Landi, T. Dracheva, Melissa Rotunno, J. Figueroa, Huaitian Liu, A. Dasgupta, Felecia Mann, J. Fukuoka, Megan L. Hames, A. Bergen and 9 more. PLoS ONE, 2008
664 citations recorded by the source corpus.
Background Tobacco smoking is responsible for over 90% of lung cancer cases, and yet the precise molecular alterations induced by smoking in lung that develop into cancer and impact survival have remained obscure. Methodology/Principal Findings We performed gene expression analysis using HG-U133A Affymetrix chips on 135 fresh frozen tissue samples of adenocarcinoma and paired noninvolved lung tissue from current, former and never smokers, with biochemically validated smoking information. ANOVA analysis adjusted for potential confounders, multiple testing procedure, Gene Set Enrichment Analysis, and GO-functional classification were conducted for gene selection. Results were confirmed in independent adenocarcinoma and non-tumor tissues from two studies. We identified a gene expression signature characteristic of smoking that includes cell cycle genes, particularly those involved in the mitotic spindle formation (e.g., NEK2, TTK, PRC1). Expression of these genes strongly differentiated both smokers from non-smokers in lung tumors and early stage tumor tissue from non-tumor tissue (p<0.001 and fold-change >1.5, for each comparison), consistent with an important role for this pathway in lung carcinogenesis induced by smoking. These changes persisted many years after smoking cessation. NEK2 (p<0.001) and TTK (p = 0.002) expression in the noninvolved lung tissue was also associated with a 3-fold increased risk of mortality from lung adenocarcinoma in smokers. Conclusions/Significance Our work provides insight into the smoking-related mechanisms of lung neoplasia, and shows that the very mitotic genes known to be involved in cancer development are induced by smoking and affect survival. These genes are candidate targets for chemoprevention and treatment of lung cancer in smokers.
Subfields: Cell Biology, Oncology, Pulmonary and Respiratory Medicine
View Paper: https://www.semanticscholar.org/paper/954ac101aafac502cbf469182595c987a8ac53d1
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Citations in the corpus
- Drug repositioning for non-small cell lung cancer by using machine learning algorithms and topological graph theory (2016, methodology): “The microarray datasets consist of data from experiments GSE7670 [25] and GSE10072 [26], which were conducted using the HG-U133A array; and data from experiments GSE19804 [27] and GSE27262 [28], which were performed using HG-U133 plus 2.0 chip.”
- Identification of a Novel Biomarker, SEMA5A, for Non–Small Cell Lung Carcinoma in Nonsmoking Women (2010, methodology): “(29), despite differences in population, gender, and smoking history.”