Research Article

使用机器学习方法通过 16 个预后相关基因特征识别 WHO II/III 胶质瘤

卷 29, 期 9, 2022

发表于: 27 August, 2021

页: [1622 - 1639] 页: 18

弟呕挨: 10.2174/0929867328666210827103049

价格: $65

摘要

背景:临床观察发现,同一级别胶质瘤的预后在世界卫生组织(WHO)II级和III级之间存在较大差异。因此,需要更好地了解 WHO II 级和 III 级胶质瘤的遗传学和分子机制,目的是在分子水平而不是传统的病理形态学水平上制定分类方案。 方法:我们使用从中国神经胶质瘤基因组图谱和癌症基因组图谱下载的表达数据集,结合最小绝对收缩和选择算子的机器学习方法进行生存分析。通过总生存相关基因的表达水平及其多变量Cox比例风险回归系数的乘积计算风险评分。 WHO II 级和 III 级胶质瘤分为低危亚组、中危亚组和高危亚组。我们使用 16 个预后相关基因作为输入特征,使用完全连接的神经网络构建基于预后的分类模型。还进行了基因功能注释。 结果:筛选出与胶质瘤预后相关的16个基因(AKNAD1、C7orf13、CDK20、CHRFAM7A、CHRNA1、EFNB1、GAS1、HIST2H2BE、KCNK3、KLHL4、LRRK2、NXPH3、PIGZ、SAMD5、ERINC2、SIX6)。选择的 16 个基因与神经胶质瘤的发展和癌变有关。来自两个队列的全连接神经网络模型的外部验证数据集的准确率达到了 95.5%。我们的方法在将 WHO II 级和 III 级胶质瘤分类为低风险、中风险和高风险亚组方面具有良好的潜在能力。亚组在总生存期方面表现出显着差异(P<0.01)。 结论:这导致了16个与胶质瘤预后相关的基因的鉴定。在这里,我们开发了一种计算方法,将 WHO II 级和 III 级胶质瘤区分为具有不同预后的三个亚组。基于基因表达的方法为确定神经胶质瘤的预后提供了一种可靠的替代方法。

关键词: 胶质瘤,生存分析,基因,神经网络,LASSO,预后相关基因。

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