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ABSTRACT: Background
Precision therapy for lung cancer will require comprehensive genomic testing to identify actionable targets as well as ascertain disease prognosis. RNA-seq is a robust platform that meets these requirements, but microarray-derived prognostic signatures are not optimal for RNA-seq data. Thus, we undertook the first prognostic analysis of lung adenocarcinoma RNA-seq data and generated a prognostic signature.Methods
Lung adenocarcinoma RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) were divided chronologically into training (n = 255) and validation (n = 157) cohorts. In the training cohort, prognostic association was assessed by univariate Cox analysis. A prognostic signature was built with stepwise multivariable Cox analysis. Outcomes by risk group,
SUBMITTER: Shukla S
PROVIDER: S-EPMC5051943 | biostudies-literature | 2017 Jan
REPOSITORIES: biostudies-literature