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基于机器学习的风险模型,结合瘤免疫和 stromal contexture 预测癌症预后和免疫疗法的有效性

Li-Na He¹, Haifeng Li^1,2, Wei Du^1,2

¹Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China.

|July 7, 2023

概括

使用6个瘤微环境 (TME) 基因的新风险评分模型 (TMErisk) 预测肺癌患者的治疗结果不佳. 这种TME风险模型还表明,在各种癌症中,免疫治疗的反应减少了.

科学领域:

在瘤学瘤学.
免疫学免疫学免疫学
生物信息学是一种生物信息学.

背景情况:

瘤微环境 (TME),包括免疫和层细胞,显著影响癌症的进展和治疗的有效性.
了解TME的作用对于开发预测生物标志物和改善癌症治疗策略至关重要.

研究的目的:

开发一种基于状细胞肺癌的TME相关基因的预测风险评分模型 (TMErisk).
评估模型预测患者预后和免疫治疗反应的能力.

主要方法:

通过分析与免疫和 stromal 评分的相关性,确定了与 TME 相关的基因.
利用LASSO-Cox回归来构建TME风险模型,使用六个选定的基因.
在多个非小细胞肺癌 (NSCLC) 数据集和其他癌症中验证了TME风险模型.

主要成果:

建立了一个六基因TME风险模型,显示了与肺状细胞癌 (LUSC) 和NSCLC的不利整体存活率 (OS) 的显著相关性.
高TME风险组被免疫抑制途径中的基因丰富,并且表现出免疫抑制细胞透率增加.
在多种癌症类型中,高TME风险与更差的预后和免疫治疗反应的降低有关.

结论:

TME风险模型作为一个强大的生物标志物,用于预测癌症患者的整体存活率和免疫治疗反应.
该模型强调了瘤微环境在肺癌和其他癌症中的预后和预测意义.

关键词:

生物科学生物科学癌症癌症癌症癌症免疫学免疫学免疫学机器学习是机器学习.

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