Dataset Information

Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study.

ABSTRACT: Although prognostic gene expression signatures for survival in early-stage lung cancer have been proposed, for clinical application, it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site, blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measurements of gene expression either alone or combined with basic clinical covariates (stage, age, sex) could be used to predict overall survival in lung cancer subjects. Several models examined produced risk scores that substantially correlated with actual subject outcome. Most methods performed better with clinical data, supporting the combined use of clinical and molecular information when building prognostic models for early-stage lung cancer. This study also provides the largest available set of microarray data with extensive pathological and clinical annotation for lung adenocarcinomas.

SUBMITTER: Director's Challenge Consortium for the Molecular Classification of Lung Adenocarcinoma

PROVIDER: S-EPMC2667337 | biostudies-literature | 2008 Aug

REPOSITORIES: biostudies-literature

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Publications

Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study.

Shedden Kerby K Taylor Jeremy M G JM Enkemann Steven A SA Tsao Ming-Sound MS Yeatman Timothy J TJ Gerald William L WL Eschrich Steven S Jurisica Igor I Giordano Thomas J TJ Misek David E DE Chang Andrew C AC Zhu Chang Qi CQ Strumpf Daniel D Hanash Samir S Shepherd Frances A FA Ding Keyue K Seymour Lesley L Naoki Katsuhiko K Pennell Nathan N Weir Barbara B Verhaak Roel R Ladd-Acosta Christine C Golub Todd T Gruidl Michael M Sharma Anupama A Szoke Janos J Zakowski Maureen M Rusch Valerie V Kris Mark M Viale Agnes A Motoi Noriko N Travis William W Conley Barbara B Seshan Venkatraman E VE Meyerson Matthew M Kuick Rork R Dobbin Kevin K KK Lively Tracy T Jacobson James W JW Beer David G DG

Nature medicine 20080720 8

Although prognostic gene expression signatures for survival in early-stage lung cancer have been proposed, for clinical application, it is critical to establish their performance across different subject populations and in different laboratories. Here we report a large, training-testing, multi-site, blinded validation study to characterize the performance of several prognostic models based on gene expression for 442 lung adenocarcinomas. The hypotheses proposed examined whether microarray measur ...[more]

PMID: 18641660

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Project description:Background: Ferroptosis is an iron-dependent programmed cell death process. Recent studies have found that ferroptosis inducers hold promising potential in the treatment of lung adenocarcinoma (LUAD). However, the comprehensive analysis about the prognostic value of ferroptosis-related genes in LUAD remains to be elucidated. Methods: The RNA sequencing data and corresponding clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A total of 259 ferroptosis-related genes were extracted from FerrDb website. The ferroptosis-related prognostic signature was developed by least absolute shrinkage and selection operator (LASSO) Cox regression analysis in TCGA LUAD cohort, and then validated by 5 independent GEO cohorts. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were performed to identify the difference in biological processes and functions between different risk groups. The expression levels of core prognostic genes were then verified in LUAD samples by immunohistochemistry (IHC) and erastin-treated LUAD cell lines by real-time polymerase chain reaction (PCR). The potential roles of GPX2 and DDIT4 as ferroptosis drivers in LUAD cell line were further confirmed by in vitro experiments. Results: A total of 20 intersecting genes between 70 ferroptosis-related DEGs and 45 potential prognostic genes were obtained for LASSO Cox regression analysis. The ferroptosis-related prognostic signature was developed by 7 core prognostic DEGs, and stratified LUAD patients into two risk groups. Kaplan-Meier analysis showed that the overall survival (OS) of LUAD patients in the high-risk group was significantly worse than that of the low-risk group. External validation of 5 independent GEO cohorts further confirmed that the ferroptosis-related prognostic signature was an ideal biomarker for predicting the survival of LUAD patients. Significant enrichment of fatty acid metabolism and cell cycle-related pathways were found in different risk groups. The expression patterns of 7 core prognostic genes in LUAD and adjacent normal lung tissues were validated by IHC, which was almost consistent with the results from public database. Furthermore, the changes related to cell cycle and ferroptosis after erastin treatment were also validated in LUAD cell lines. In addition, silencing GPX2 or DDIT4 could partially reverse the erastin-induced ferroptosis. Conclusion: In summary, the ferroptosis-related prognostic signature based on 7 core prognostic DEGs indicated superior predictive performance of LUAD patients. Targeting ferroptosis holds potential to be a therapeutic alternative for LUAD.

Dataset Information

Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study.

Publications

Gene expression-based survival prediction in lung adenocarcinoma: a multi-site, blinded validation study.

Similar Datasets

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