子宫内膜癌的分子景观:整合多组学和深度学习以实现个性化的生存预测
Behnaz Haji Molla Hoseyni1, Hossein Lanjanian2, Yasaman Zohrab Beigi1
1Laboratory of Systems Biology and Bioinformatics (LBB), Institute of Biochemistry and Biophysics, University of Tehran, Tehran, 1417614335, Iran.
Computers in biology and medicine
|May 4, 2025
概括
这项研究使用多omics数据和深度学习确定了子宫内膜腺癌存活率的新生物标志物. 关键发现将维生素D和加拉宁受体通路与患者的结果联系起来,改善预后评估.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 子宫内膜质子宫内膜癌对女性构成重大健康风险.
- 了解影响患者结果的因素对于这种疾病至关重要.
研究的目的:
- 开发一个新的存活率分析管道用于子宫内膜腺癌.
- 使用多omics数据识别潜在的生存生物标志物.
- 研究生物变量与生存结果之间的关系.
主要方法:
- 应用了一种新的生存分析管道,用于从TCGA-UCEC子宫内膜样本中获取的多omics数据 (转录组,甲基化,蛋白质组).
- 开发了一种具有定制损失功能的深度学习自动编码器,以建模复杂的生物关系和生存.
- 利用SHAP分析对346个与生存相关的特征进行排名.
主要成果:
- 一个新的自编码器丢失功能增强了生存信息的提取.
- 确定了346个生存排名的特征,专注于前30个.
- 发现了维生素D,其受体和加拉宁受体通路与子宫内膜腺癌存活率之间的关联.
结论:
- 引入了一种创新的深度学习方法,用于多omics生存分析,强调审查数据.
- 确定了子宫内膜癌的潜在预后生物标志物.
- 阐明了与癌症存活相关的途径关联,增强了对疾病进展的理解.
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