Wu Chunxiao, Zhang Donglei
Department of Thoracic Surgery, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai 200032, China.
Department of Thoracic Surgery, Renji Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 201112, China.
Cancer Biomark. 2017;18(2):117-123. doi: 10.3233/CBM-151368.
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.
目前的分期方法在预测早期肺腺癌的预后方面缺乏精确性。
我们旨在开发一种基因表达特征来识别高危和低危患者群体。
我们使用贝叶斯模型平均算法分析来自三个独立队列的442例肺腺癌患者的DNA微阵列数据，其中一个队列用于训练。
根据基于所识别的25基因特征计算出的风险评分，将患者分为高危或低危组。使用Kaplan-Meier分析和对数秩检验评估预后能力。测试集被分为两个不同的组，对数秩检验p值分别为0.00601和0.0274。
我们的结果表明，预后模型可以成功预测患者的预后，并作为早期肺腺癌总生存分析的生物标志物。
