与cuproptosis基因相关的,基于神经网络的预后预测和KIRC的药物标预测
Yixin Liu1,2, Yuan Shao3, Zezhou Hao2
1Department of Surgery, Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Cancer medicine
|December 22, 2023
概括
这项研究开发了一种使用cuproptosis基因的深度神经网络模型,以预测脏清细胞癌 (KIRC) 的预后,并确定了KIRC治疗的敏感药物,提供了新的治疗策略.
科学领域:
- 在瘤学瘤学.
- 基因组学就是基因组学.
- 计算生物学 计算生物学
背景情况:
- 脏清细胞癌 (KIRC) 的预后不佳,预后标志物有限.
- 细胞亡是一种新的细胞死亡模式,显示出作为癌症预后生物标志物的潜力,包括KIRC.
- 准确的预后和个性化药物向对于管理KIRC复发至关重要.
研究的目的:
- 为了确定与KIRC预后相关的cuproptosis基因.
- 为KIRC患者风险分层和生存预测开发一个深度神经网络 (DNN) 模型.
- 用图形神经网络 (GNN) 来选KIRC敏感药物并预测它们的标,用于新的治疗策略.
主要方法:
- 利用TCGA和ICGC数据库进行差异基因表达分析和DNN模型开发/验证.
- 通过KIRC敏感药物的选,并使用GNN (GraphSAGE) 来使用DrugBank数据预测药物向相互作用.
- 对高风险和低风险患者组进行功能分析.
主要成果:
- 基于10个与cuproptosis相关的基因的预后模型显示出良好的预测性能 (AUC 0.739培训,0.707验证).
- 确定了四种对KIRC高度敏感的药物,其中5-Fluorouracil表现出最强的抑制作用.
- 预测的关键药物标,包括cytochrome P450 2D6和UDP-glucuronosyltransferase 1A,具有很高的准确性 (GraphSAGE平均值. 0.817). 在这个问题上.
结论:
- 使用cuproptosis基因开发的KIRC风险预测模型提供了独立的预后价值.
- 确定了潜在的治疗药物及其对KIRC的点,一些相互作用通过现有的文献得到验证.
- 该研究为KIRC的个性化治疗策略和未来药物发现提供了基础.
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