Chunxiao Wu1,1, Donglei Zhang2,1. 1. Department of Thoracic Surgery, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China. 2. Department of Thoracic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201112, China.
Abstract
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.
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 adenocarcinomapatients 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.
Entities:
Keywords:
Bayesian Model Averaging; Lung adenocarcinoma; gene expression; prognosis