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首页> 外文期刊>Iranian journal of public health. >Predicting the Survival Time for Bladder Cancer Using an Addi-tive Hazards Model in Microarray Data
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Predicting the Survival Time for Bladder Cancer Using an Addi-tive Hazards Model in Microarray Data

机译：使用微阵列数据中的附加危险模型预测膀胱癌的生存时间

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Background: One substantial part of microarray studies is to predict patients’ survival based on their gene expression profile. Variable selection techniques are powerful tools to handle high dimensionality in analysis of microarray data. However, these techniques have not been investigated in competing risks setting. This study aimed to investigate the performance of four sparse variable selection methods in estimating the survival time.Methods: The data included 1381 gene expression measurements and clinical information from 301 patients with bladder cancer operated in the years 1987 to 2000 in hospitals in Denmark, Sweden, Spain, France, and England. Four methods of the least absolute shrinkage and selection operator, smoothly clipped absolute deviation, the smooth integration of counting and absolute deviation and elastic net were utilized for simultaneous variable selection and estimation under an additive hazards model. The criteria of area under ROC curve, Brier score and c-index were used to compare the methods.Results: The median follow-up time for all patients was 47 months. The elastic net approach was indicated to outperform other methods. The elastic net had the lowest integrated Brier score (0.137±0.07) and the greatest median of the over-time AUC and C-index (0.803±0.06 and 0.779±0.13, respectively). Five out of 19 selected genes by the elastic net were significant (P<0.05) under an additive hazards model. It was indicated that the expression of RTN4, SON, IGF1R and CDC20 decrease the survival time, while the expression of SMARCAD1 increase it.Conclusion: The elastic net had higher capability than the other methods for the prediction of survival time in patients with bladder cancer in the presence of competing risks base on additive hazards model.
机译：背景：微阵列研究的重要组成部分是根据患者的基因表达谱预测其生存情况。变量选择技术是处理微阵列数据分析中的高维数的强大工具。但是，尚未在竞争风险设定中研究这些技术。本研究旨在探讨四种稀疏变量选择方法在估计生存时间方面的性能。方法：数据包括1987年至2000年在丹麦丹麦医院接受手术的301例膀胱癌患者的1381个基因表达测量值和临床信息。 ，西班牙，法国和英国。在加性危害模型下，采用最小收缩和选择算子最小的四种方法，平滑限幅的绝对偏差，计数和绝对偏差的平滑积分以及弹性网，用于同时变量选择和估计。方法：采用ROC曲线下面积，Brier评分和c-index标准进行比较。结果：所有患者中位随访时间为47个月。结果表明，弹性网方法优于其他方法。弹性网的综合Brier得分最低（0.137±0.07），超时AUC和C指数的中位数最大（分别为0.803±0.06和0.779±0.13）。在加性危害模型下，通过弹性网选择的19个基因中有5个具有显着性（P <0.05）。提示RTN4，SON，IGF1R和CDC20的表达降低了生存时间，而SMARCAD1的表达增加了生存时间。结论：弹性网预测膀胱癌患者生存时间的能力比其他方法高。在存在附加风险模型的竞争风险下。

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