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在膀癌中推进个性化,预测性和预防性医学:用于新型预后建模,免疫分析和治疗点发现的多omics和机器学习方法

Han Yan¹, Xinyu Ji², Bohan Li³

¹Department of Pain Medicine, The First Hospital of China Medical University, Liaoning, Shenyang, China.

Frontiers in immunology

|May 7, 2025

概括

这项研究开发了一个免疫细胞死亡 (ICD) 相关的预后签名 (ICDRS) 膀癌 (BLCA). ICDRS有效地预测患者的生存率,并识别免疫逃避机制,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
基因组学就是基因组学.

背景情况:

膀癌 (BLCA) 的预后和治疗反应是复杂的.
免疫性细胞死亡 (ICD) 在抗瘤免疫力中起着至关重要的作用.
多主题数据为开发新生物标志物提供了潜力.

研究的目的:

在BLCA中识别和分析ICD相关的多omics特征.
为BLCA患者构建一个预后签名 (ICDRS).
探索ICDRS的临床和生物影响,包括免疫微环境和药物敏感性.

主要方法:

综合单细胞RNA测序 (scRNA-seq) 和来自TCGA和GEO的大量RNA-seq数据.
使用Seurat进行scRNA-seq处理和ssGSEA进行ICD评分.
使用WGCNA和机器学习 (Lasso,Ridge,CoxBoost) 来构建ICDRS.

主要成果:

基于八个基因的ICDRS将BLCA患者分为两层,分为不同的高风险和低风险组,具有显著的生存差异 (P<0.001).
高的ICDRS得分与免疫抑制性瘤微环境 (TME) 和特定的免疫细胞透相关.
途径分析揭示了与胆固醇平衡,EMT和KRAS信号的关联;高风险患者对某些化疗药物表现出耐药性.

结论:

ICDRS是BLCA的强有力的预后生物标志物,提供了对免疫逃避的见解.
签名有助于理解瘤生物学和识别治疗点.
ICDRS支持个性化治疗策略,并强调ICD在BLCA精密医学中的重要性.

关键词:

膀癌:膀癌是一种癌症.免疫性细胞死亡免疫性细胞死亡免疫疗法免疫疗法机器学习是机器学习.多主题整合多主题整合.预后签名预后签名

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