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基于机器学习的瘤透B淋巴细胞相关指数可以预测肺腺癌的预后和免疫治疗反应

Jiale Fang¹, Siyuan Yu¹, Wei Wang^2,3

¹Department of Pharmacology, Southern University of Science and Technology, Shenzhen, China.

Frontiers in immunology

|April 8, 2025

概括

透瘤的B淋巴细胞 (TILBs) 影响肺腺癌 (LUAD) 的进展. 一个新的指数 (BRI) 预测了生存率和治疗反应,为LUAD患者的风险分层和个性化治疗提供了一个工具.

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:

透瘤的B淋巴细胞 (TILBs) 显著影响瘤免疫微环境 (TIME) 和肺腺癌 (LUAD) 的进展.
目前对LUAD中TILB特征的全面理解有限.

研究的目的:

在LUAD中使用单细胞RNA测序来识别TILB的关键标记物.
开发和验证一种新的指数 (BRI) 来评估TILB透及其在LUAD中的预后和预测价值.

主要方法:

单细胞RNA测序 (GSE117570) 用于识别TILB标记物.
使用十个算法的机器学习方法在TCGA,GSE31210和GSE72094数据集中生成了BRI.
在GSE91061和GSE126044.4中评估了BRI与TIME,免疫生物标志物和免疫治疗反应的关联.

主要成果:

BRI独立预测了LUAD患者的整体存活率.
在高和低BRI组之间观察到免疫细胞群 (B细胞,M2巨细胞,NK细胞,调节性T细胞) 的显著差异.
较低的BRI与更好的免疫疗法反应和降低的药物IC50值相关,而较高的BRI与增加的调节性T细胞和降低CD8+T细胞透率有关.

结论:

开发的BRI模型,利用多个机器学习算法,是 LUAD 中风险分层和预后的宝贵工具.
BRI有助于预测患者对免疫疗法和常规疗法的反应,支持个性化治疗选择.

关键词:

在B细胞中,B细胞是在TCGA中,TCGA就是TCGA.免疫疗法免疫疗法肺癌是一种肺癌.机器学习是机器学习.瘤微环境是一个微环境.

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Tumor Immunotherapy

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Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.