基于机器学习的免疫细胞死亡相关得分的构建,以改善质瘤的预后和个性化治疗
Guoyin Li1,2, Yukui Zhao1, Yubo He3
1Key Laboratory of Modern Teaching Technology, Ministry of Education, Shaanxi Normal University, No. 199 Chang'an South Road, Xi'an, 710062, Shaanxi, China.
Scientific reports
|August 19, 2025
概括
我们开发了一种免疫细胞死亡 (ICD) 风险评分来预测质瘤患者的预后和药物反应. 高得分与增加的免疫细胞透和SERPINH1表达相关,这通过JAK/STAT信号促进质瘤的进展.
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:
- 免疫性细胞死亡 (ICD) 激活了先天性和适应性免疫.
- 质瘤预后和药物敏感性评估需要改进生物标志物.
研究的目的:
- 为结质瘤预后和药物敏感性开发与ICD相关的签名.
- 确定关键的基因和涉及质瘤进展的途径.
主要方法:
- 达成共识的集群和NMF用于患者分组.
- 通过拉索回归来构建ICD相关风险评分 (ICDS).
- 网络分类,估计,流细胞测量,CCK-8,EDU,Transwell,qPCR,西部斑点,IHC和免疫光.
主要成果:
- ICDS在多个队列中准确预测了质瘤患者的预后,并超过了71个现有的签名.
- 高的ICDS得分与增加的免疫细胞透和免疫检查点抑制剂基因表达相关.
- ICDS中的关键基因SERPINH1在质瘤中过度表达,通过JAK/STAT信号促进了扩散和迁移.
结论:
- ICDS是评估质瘤预后和药物反应的宝贵工具.
- 通过激活JAK/STAT通路,SERPINH1促进结质瘤恶性瘤的发生.
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