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瘤免疫背景模型预测了胃癌的预后和免疫治疗反应

Jiexuan Wang¹, Xin Yang¹, Xuan Dai¹

¹Department of General Surgery, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.

The journal of gene medicine

|December 17, 2025

概括

瘤免疫环境评分 (TIES) 提高了胃癌的预后和免疫治疗的预测. 这种基于免疫的得分增强了风险分层,超出了传统的分阶段,帮助个性化治疗策略.

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:

胃癌 (GC) 的异质性挑战了当前的预后模型,这些模型往往忽视了关键的瘤免疫特征.
现有的模型限制了个性化治疗策略和对GC患者免疫疗法反应的准确预测.

研究的目的:

引入和验证瘤免疫环境评分 (TIES),一种基于免疫的预后模型,用于GC.
为了提高风险分层,并预测使用TIES的GC患者的免疫治疗反应.

主要方法:

使用ssGSEA,LASSO和Cox回归分析了七个GC队列 (1487名患者) 的转录和临床数据.
通过决策曲线分析 (DCA) 和重新分类指标 (NRI/IDI) 评估的临床效用.
使用GSE183924数据评估免疫治疗反应;用12基因RT-qPCR小组 (qTIES) 进行实验验证.

主要成果:

高的TIES与较差的整体存活率 (OS) 和无病存活率 (DFS) 相相关 (p < 0.001).
与TNM分期 (AUC 0.75-0.79与0.61-0.64) 相比,TIES显示出更高的预测准确性,并且与病理阶段相结合时,预测得到了改善.
低TIES表明免疫炎症亚型具有更高的瘤突变负担 (TMB) 和更好的免疫疗法反应概率 (p=0.014).

结论:

TIES是GC的强大预后工具,显著改善了风险分层和免疫治疗反应预测.
将TIES与临床病理特征相结合,可以增强个性化治疗策略和GC患者的治疗结果.

关键词:

胃癌胃癌是一种胃癌.免疫微观环境免疫微环境免疫疗法免疫疗法机器学习是机器学习.预测模型的预测模型.

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