Identification of early-stage lung adenocarcinoma prognostic signatures based on statistical modeling.

BACKGROUND: Current staging methods are lack of precision in predicting prognosis of early-stage lung adenocarcinomas. OBJECTIVE: We aimed to develop a gene expression signature to identify high- and low-risk groups of patients. METHODS: We used the Bayesian Model Averaging algorithm to analyze the DNA microarray data from 442 lung adenocarcinoma patients from three independent cohorts, one of which was used for training. RESULTS: The patients were assigned to either high- or low-risk groups based on the calculated risk scores based on the identified 25-gene signature. The prognostic power was evaluated using Kaplan-Meier analysis and the log-rank test. The testing sets were divided into two distinct groups with log-rank test p-values of 0.00601 and 0.0274 respectively. CONCLUSIONS: Our results show that the prognostic models could successfully predict patients' outcome and serve as biomarkers for early-stage lung adenocarcinoma overall survival analysis. [ABSTRACT FROM AUTHOR]