通过集成机器学习进行系统性途径选,识别了FOXO介导的转录特征,用于在非小细胞肺癌中预测强大的免疫治疗反应
Shuqi Wu1, Chenxi Deng1,2, Chaofan Fan3
1Department of Oncology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China, fimmu.com.
Human mutation
|January 21, 2026
概括
一个新的机器学习模型,FOXO相关签名 (FRS),有效地预测非小细胞肺癌 (NSCLC) 患者对免疫检查点抑制剂 (ICI) 的反应. 这种生物标志物对患者进行了分层,识别出那些可能从免疫治疗中获益最多的患者,以改善生存结果.
科学领域:
- 在瘤学瘤学.
- 免疫治疗是一种免疫疗法.
- 生物信息学是一种生物信息学.
背景情况:
- 非小细胞肺癌 (NSCLC) 是全球癌症死亡的主要原因.
- 免疫检查点抑制剂 (ICI) 改善了一些NSCLC患者的结果,但需要生物标志物来进行个性化治疗选择.
研究的目的:
- 确定可靠的生物标志物,以预测NSCLC患者的免疫治疗反应.
- 开发一个预测模型,整合转录数据和机器学习算法.
主要方法:
- 分析了584名接受ICI治疗的NSCLC患者的转录组数据.
- 101个机器学习算法组合应用于12025个路径以识别预后签名.
- 与Lasso-RSF算法相结合的FOXO介导的转录途径被选为最佳预测器.
主要成果:
- 与FOXO相关的签名 (FRS) 将患者分为高风险和低风险组,显著影响无进展生存 (PFS) 和整体生存 (OS).
- 与临床变量和现有模型相比,FRS显示出更高的预测准确性.
- 免疫组织化学 (IHC) 和免疫分析支持FRS的生物相关性和与免疫治疗反应的关联.
结论:
- 机器学习衍生的FRS是NSCLC免疫疗法反应和生存的强有力的预测器.
- 通过整合FOXO介导的免疫调节见解,FRS为精密瘤学提供了一个临床可翻译的工具.
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