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• W2560778758 abstract "Current staging methods are lack of precision in predicting prognosis of early-stage lung adenocarcinomas.We aimed to develop a gene expression signature to identify high- and low-risk groups of patients.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.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.Our results show that the prognostic models could successfully predict patients' outcome and serve as biomarkers for early-stage lung adenocarcinoma overall survival analysis." @default.
• W2560778758 created "2016-12-16" @default.
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• W2560778758 date "2017-02-27" @default.
• W2560778758 modified "2023-09-23" @default.
• W2560778758 title "Identification of early-stage lung adenocarcinoma prognostic signatures based on statistical modeling" @default.
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• W2560778758 doi "https://doi.org/10.3233/cbm-151368" @default.
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