基于机器学习的多模式放射学和转录学模型,用于预测食道癌的放射治疗敏感性和预后
Chengyu Ye1, Hao Zhang1, Zhou Chi1
1The Affiliated Cancer Hospital of Wenzhou Medical University, Wenzhou Central Hospital, Wenzhou, 325000, PR China.
The Journal of biological chemistry
|May 17, 2025
概括
这项研究使用机器学习来预测食道癌症的放射治疗反应. 通过向SRC,STUB1基因提高了治疗效率,为更好的患者结果提供了新的治疗策略.
科学领域:
- 在瘤学瘤学.
- 遗传学 是一个遗传学.
- 放射疗法是一种放射治疗.
背景情况:
- 放射治疗对于食道癌至关重要,但反应各不相同.
- 预测个体患者的结果仍然是一个挑战.
研究的目的:
- 开发放射治疗敏感性和食道癌的预后预测模型.
- 将机器学习与多式联络放射学和转录学数据集成.
主要方法:
- 应用SEResNet101深度学习模型对来自UCSC Xena和TCGA数据库的成像和转录数据.
- 确定了与预后相关的基因,包括STUB1,PEX12和HEXIM2.
- 使用拉索回归和考克斯分析构建了一个预后风险模型.
主要成果:
- 开发了一个预后风险模型,通过生存概率对患者进行分层.
- 鉴定STUB1为E3泛酸酶,通过降解SRC来增强放射治疗的敏感性.
- 证实STUB1过度表达或SRC静音改善了放射治疗反应在体外和体内.
结论:
- 多模式数据集成可以预测食道癌的放射治疗反应和预后.
- STUB1是改善放射治疗疗效的潜在治疗标.
- 研究结果支持对食道癌患者进行个性化放射治疗计划.
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