Development and validation of a quantitative real-time polymerase chain reaction classifier for lung cancer prognosis.

ABSTRACT:

Introduction

This prospective study aimed to develop a robust and clinically applicable method to identify patients with high-risk early-stage lung cancer and then to validate this method for use in future translational studies.

Methods

Three published Affymetrix microarray data sets representing 680 primary tumors were used in the survival-related gene selection procedure using clustering, Cox model, and random survival forest analysis. A final set of 91 genes was selected and tested as a predictor of survival using a quantitative real-time polymerase chain reaction-based assay using an independent cohort of 101 lung adenocarcinomas.

Results

The random survival forest model built from 91 genes in the training set predicted patient survival in an independent cohort of

SUBMITTER: Chen G 

PROVIDER: S-EPMC3167380 | biostudies-literature | 2011 Sep

REPOSITORIES: biostudies-literature