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Predictive Immune Modeling of Solid Tumors
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整合多omics和机器学习生存框架,以建立基于免疫功能和细胞死亡模式的预后模型,在肺腺癌队列中的肺腺癌
Yiluo Xie1,2, Huili Chen3, Mei Tian1
1Anhui Province Key Laboratory of Clinical and Preclinical Research in Respiratory Disease, MolecularDiagnosis Center, Joint Research Center for Regional Diseases of Institute of Health and Medicine (IHM), First Affiliated Hospital of Bengbu Medical University, Bengbu, China.
Frontiers in immunology
|September 30, 2024
概括
这项研究确定了三种肺腺癌亚型和基于免疫和细胞死亡基因的新预后模型 (PIGRS). PSME3显示出作为肺腺癌的新预后因素的潜力.
科学领域:
- 在瘤学瘤学.
- 免疫学 免疫学 免疫学
- 遗传学 是一个遗传学.
背景情况:
- 编程细胞死亡 (PCD) 和与免疫相关的基因对肺腺癌 (LUAD) 的发展和预后至关重要.
- 在LUAD中,免疫基因和细胞死亡之间的相互作用的预后影响需要进一步调查.
研究的目的:
- 调查免疫相关基因和细胞死亡模式在LUAD中的预后意义.
- 开发一个强大的计算框架来分析在LUAD中的多omics数据.
- 为了确定LUAD的新型预后生物标志物.
主要方法:
- 应用于10个聚类算法对多omics数据 (细胞死亡基因,免疫基因,甲基化,体性突变) 用于LUAD分子类型.
- 开发了一种免疫相关编程细胞死亡模型 (PIGRS),使用具有101个算法组合的机器学习框架.
- 进行了体外实验,以探索PSME3在LUAD中的作用.
主要成果:
- 将TCGA-LUAD患者分为三个亚型 (CS1,CS2,CS3) 与不同的预后;CS3显示出最好的结果.
- 包含15个高影响基因的PIGRS模型显示了LUAD患者强大的预后性能.
- 确定PSME3作为肺腺癌的潜在新预后因素,之前的研究有限.
结论:
- 生物信息分析成功确定了具有临床意义的三个LUAD亚型.
- 皮格斯模型为LUAD的预后评估提供了一个强大的工具.
- 通过PI3K/AKT/Bcl-2通路,PSME3可能会影响LUAD细胞的亡,这表明它有可能成为治疗点.
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