整合机器学习和多omics分析,探索Treg相关编程细胞死亡特征清细胞细胞癌
Haojie Dai1, Xi Zhang2, Lu Yin3
1The First Clinical Medical College, Nanjing Medical University, Nanjing, 211166, Jiangsu, China.
这项研究揭示了调节性T细胞 (Tregs) 和编程细胞死亡 (PCD) 在清细胞细胞癌 (ccRCC) 中的关键作用. 我们开发了诊断和预后模型,确定SLC11A1作为ccRCC治疗的治疗点.
科学领域:
- 在瘤学瘤学.
- 免疫学 免疫学 免疫学
- 计算生物学 计算生物学
背景情况:
- 调节性T细胞 (Tregs) 和编程细胞死亡 (PCD) 是癌症进展的关键因素,但它们在清细胞细胞癌 (ccRCC) 中的相互作用尚不清楚.
- 研究与Treg相关的PCD特征对于理解ccRCC瘤微环境和开发向疗法至关重要.
研究的目的:
- 阐明Treg和PCD特征在ccRCC瘤微环境中的意义.
- 开发用于ccRCC诊断,预后和个性化治疗选择的临床适用工具.
- 确定ccRCC的新型治疗点.
主要方法:
- 孟德尔随机化 (MR) 用于识别与ccRCC结果相关的PCD基因和Treg标记物.
- 开发了一种多层感知子 (MLP) 模型用于诊断,并集成了10个机器学习算法用于预后模型构建.
- SHAP分析解释了预后模型,而体外实验验验证了核心基因SLC11A1.1. 药物查和分子对接确定了潜在的抑制剂.
主要成果:
- MR在Treg亚型,CASP9和ccRCC结果之间建立了因果关系.
- 一个基于MLP的诊断模型在外部验证中获得了0.987的AUC.
- 一个Treg-Programmed Cell Death Score (TPCDS) 预后指数显示了队列中的优异分层,与免疫抑制微环境相关,并指导化疗敏感性.
- 核心基因SLC11A1被证实是致癌的驱动因素,而阿托瓦昆被确定为高亲和度抑制剂.
结论:
- 该研究建立了用于ccRCC诊断,预后和个性化治疗选择的临床翻译工具,突出了可解释AI在精密瘤学的实用性.
- SLC11A1被确定为ccRCC的一个有前途的治疗点.
- 了解Treg-PCD相互作用为ccRCC治疗策略提供了新的途径.
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