Personalized medicine is critical for lung cancer treatment. Different gene signatures that can classify lung cancer patients as high- or low-risk for cancer recurrence have been found. The aim of this study is to ide...Personalized medicine is critical for lung cancer treatment. Different gene signatures that can classify lung cancer patients as high- or low-risk for cancer recurrence have been found. The aim of this study is to identify a novel gene signature that has higher recurrence risk prediction accuracy for non-small cell lung cancer patients than previous re-search, which can clearly differentiate the high- and low-risk groups. To accomplish this we employed an ensemble of feature selection algorithms, an ensemble of classification algorithms, and a genetic algorithm, an evolutionary search algorithm. Compared to one previous study, our 12-gene signature more accurately classifies the patients in the training set (n = 256), 57.32% compared to 50.78%, as well as in the two test sets (n = 104 and n = 82), 67.07% compared to 54.9% and 57.32% compared to 54.8%;where the prediction accuracy was determined by the average of the four classifiers. Through Kaplan-Meier analysis on high- and low-risk patients our 12-gene signature revealed statistically significant risk differentiation in each data set: the training set had a p-value less than 0.001 (log-rank) and the two test sets had (log-rank) p-values less than 0.05. Analysis of the posterior probabilities revealed strong correlation between 5-year survival and the 12-gene signature. Also, functional pathway analysis uncovered associations between the 12-gene signature and cancer causing genes in the literature.展开更多
文摘Personalized medicine is critical for lung cancer treatment. Different gene signatures that can classify lung cancer patients as high- or low-risk for cancer recurrence have been found. The aim of this study is to identify a novel gene signature that has higher recurrence risk prediction accuracy for non-small cell lung cancer patients than previous re-search, which can clearly differentiate the high- and low-risk groups. To accomplish this we employed an ensemble of feature selection algorithms, an ensemble of classification algorithms, and a genetic algorithm, an evolutionary search algorithm. Compared to one previous study, our 12-gene signature more accurately classifies the patients in the training set (n = 256), 57.32% compared to 50.78%, as well as in the two test sets (n = 104 and n = 82), 67.07% compared to 54.9% and 57.32% compared to 54.8%;where the prediction accuracy was determined by the average of the four classifiers. Through Kaplan-Meier analysis on high- and low-risk patients our 12-gene signature revealed statistically significant risk differentiation in each data set: the training set had a p-value less than 0.001 (log-rank) and the two test sets had (log-rank) p-values less than 0.05. Analysis of the posterior probabilities revealed strong correlation between 5-year survival and the 12-gene signature. Also, functional pathway analysis uncovered associations between the 12-gene signature and cancer causing genes in the literature.
