Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of human stage non-small cell lung cancer samples, metaanalysis for a predictive signature

ABSTRACT: We applied a meta-analysis of datasets from seven different microarray studies on lung cancer for differentially expressed genes related to survival time (under 2 y and over 5 y). Systematic bias adjustment in the datasets was performed by distance-weighted discrimination (DWD). We identified a gene expression signature consisting of 64 genes that is highly predictive of which stage I lung cancer patients may benefit from more aggressive therapy. Experiment Overall Design: RNA was extracted from frozen tissue of primary stage I Non-Small Cell lung tumors for gene array analysis

ORGANISM(S): Homo sapiens

SUBMITTER: Yan Lu

PROVIDER: E-GEOD-6253 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

A gene expression signature predicts survival of patients with stage I non-small cell lung cancer.

Lu Yan Y Lemon William W Liu Peng-Yuan PY Yi Yijun Y Morrison Carl C Yang Ping P Sun Zhifu Z Szoke Janos J Gerald William L WL Watson Mark M Govindan Ramaswamy R You Ming M

PLoS medicine 20061201 12

<h4>Background</h4>Lung cancer is the leading cause of cancer-related death in the United States. Nearly 50% of patients with stages I and II non-small cell lung cancer (NSCLC) will die from recurrent disease despite surgical resection. No reliable clinical or molecular predictors are currently available for identifying those at high risk for developing recurrent disease. As a consequence, it is not possible to select those high-risk patients for more aggressive therapies and assign less aggress ...[more]

PMID: 17194181

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Project description:Objective. Microarray analysis was used to determine whether children with recent onset polyarticular juvenile idiopathic arthritis (JIA) exhibit biologically or clinically informative gene expression signatures in peripheral blood mononuclear cells (PBMC). Methods. Peripheral blood samples were obtained from 59 healthy children and 61 children with polyarticular JIA prior to treatment with second-line medications, such as methotrexate or biological agents. RNA was purified from Ficoll-isolated mononuclear cells, fluorescently labeled and then hybridized to Affymetrix U133 Plus 2.0 GeneChips. Data were analyzed using ANOVA at a 5% false discovery rate threshold after Robust Multi-Array Average pre-processing and Distance Weighted Discrimination normalization. Results. Initial analysis revealed 873 probe sets for genes that were differentially expressed between polyarticular JIA and controls. Hierarchical clustering of these probe sets distinguished three subgroups within polyarticular JIA. Prototypical subjects within each subgroup were identified and used to define subgroup-specific gene expression signatures. One of these signatures was associated with monocyte markers, another with transforming growth factor-beta-inducible genes, and a third with immediate-early genes. Correlation of these gene expression signatures with clinical and biological features of JIA subgroups suggests direct relevance to aspects of disease activity and supports the division of polyarticular JIA into distinct subsets. Conclusions. PBMC gene expression signatures in recent onset polyarticular JIA reflect discrete disease processes and offer a molecular classification of disease. Keywords: Patient vs. control, reassessment of phenotype PBMC samples were obtained from 59 healthy children and 61 children with polyarticular JIA prior to treatment with second-line medications, such as methotrexate or biological agents. RNA was purified from Ficoll-isolated mononuclear cells, fluorescently labeled and then hybridized to Affymetrix U133 Plus 2.0 GeneChips. Data were analyzed using ANOVA at a 5% false discovery rate threshold after Robust Multi-Array Average pre-processing and Distance Weighted Discrimination normalization.

Dataset Information

Transcription profiling of human stage non-small cell lung cancer samples, metaanalysis for a predictive signature

Publications

A gene expression signature predicts survival of patients with stage I non-small cell lung cancer.

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