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Development and validation of a prognostic gene-expression signature for lung adenocarcinoma.


ABSTRACT: Although several prognostic signatures have been developed in lung cancer, their application in clinical practice has been limited because they have not been validated in multiple independent data sets. Moreover, the lack of common genes between the signatures makes it difficult to know what biological process may be reflected or measured by the signature. By using classical data exploration approach with gene expression data from patients with lung adenocarcinoma (n = 186), we uncovered two distinct subgroups of lung adenocarcinoma and identified prognostic 193-gene gene expression signature associated with two subgroups. The signature was validated in 4 independent lung adenocarcinoma cohorts, including 556 patients. In multivariate analysis, the signature was an independent predictor of overall survival (hazard ratio, 2.4; 95% confidence interval, 1.2 to 4.8; p = 0.01). An integrated analysis of the signature revealed that E2F1 plays key roles in regulating genes in the signature. Subset analysis demonstrated that the gene signature could identify high-risk patients in early stage (stage I disease), and patients who would have benefit of adjuvant chemotherapy. Thus, our study provided evidence for molecular basis of clinically relevant two distinct two subtypes of lung adenocarcinoma.

SUBMITTER: Park YY 

PROVIDER: S-EPMC3436895 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature

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Development and validation of a prognostic gene-expression signature for lung adenocarcinoma.

Park Yun-Yong YY   Park Eun Sung ES   Kim Sang Bae SB   Kim Sang Cheol SC   Sohn Bo Hwa BH   Chu In-Sun IS   Jeong Woojin W   Mills Gordon B GB   Byers Lauren Averett LA   Lee Ju-Seog JS  

PloS one 20120907 9


Although several prognostic signatures have been developed in lung cancer, their application in clinical practice has been limited because they have not been validated in multiple independent data sets. Moreover, the lack of common genes between the signatures makes it difficult to know what biological process may be reflected or measured by the signature. By using classical data exploration approach with gene expression data from patients with lung adenocarcinoma (n = 186), we uncovered two dis  ...[more]

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