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基于机器学习的PANoptosis相关模型的表征,用于改善肺腺癌患者的预后和免疫治疗反应

Ziqiao Fu¹, Jia Zeng¹, Xiaomin Xiong¹

¹Respiratory and Critical Care Medicine, Guangyuan Central Hospital, Guangyuan, 628000, Sichuan, China.

Discover oncology

|August 24, 2025

概括

这项研究确定了一种与PANoptosis相关的新型特征 (PRS),用于预测肺腺癌患者的生存率和指导治疗. PRS有助于风险分层,并确定潜在的药物点,以改善患者的治疗结果.

科学领域:

癌症学
细胞死亡研究
生物信息学

背景情况:

PANoptosis是一种炎症编程的细胞死亡途径,涉及热死,死和死.
肺腺癌 (LUAD) 迫切需要改善预后和治疗策略.

研究的目的:

在肺腺癌中研究PANoptosis的临床影响.
为患者预后和治疗反应开发预测模型.

主要方法:

根据使用ConsensusClusterPlus的PANoptosis调节器确定了LUAD分子亚型.
使用权重基因相关网络分析 (WGCNA) 来识别亚型相关基因模块.
使用机器学习算法开发了与PANoptosis相关的签名 (PRS),并验证了其预后和预测价值.

主要成果:

在LUAD中发现了两种与PANoptosis相关的分子亚型.
构建了一个强大的PRS准确预测整体存活率,并作为一个独立的预后因素.
低PRS患者的生存率和免疫透率更好,而高PRS与更高的突变率和对特定化疗药物 (docetaxel,gemcitabine) 的敏感性相关.
在高PRS患者中确定了七种候选药物和一种治疗标 (TRPA1).

结论:

开发的PRS为LUAD患者的风险分层和预后提供了有价值的工具.
候选药物和治疗标为高PRS LUAD患者提供了潜在的新治疗途径.

关键词:

候选药物免疫细胞肺腺癌肺腺癌机器学习泛光症一个TRPA1

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