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针对侵入性PitNET的机器学习驱动PCDI分类器

Guanyu Wang1,2, Song Yan1,2, Luyang Zhang1

  • 1Department of Neurosurgery, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150086, China.

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概括
此摘要是机器生成的。

一个新的编程细胞死亡指数 (PCDI) 准确地区分了侵袭性垂体瘤. 这种生物标志物捕获了免疫代谢交叉声,为侵入性PitNET的个性化治疗提供了新的途径.

关键词:
下垂体神经内分泌瘤基因表达的总体总体.免疫透 免疫透是什么?机器学习是机器学习.胰腺癌. 这是癌症.预测模型的预测模型.编程细胞死亡相关指数

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科学领域:

  • 在瘤学瘤学.
  • 分子生物学分子生物学
  • 免疫学 免疫学 免疫学

背景情况:

  • 侵袭性垂体神经内分泌瘤 (PitNETs) 由于侵袭性和耐治疗性而存在治疗挑战.
  • 现有的预后标志物未能捕捉到分子异质性,凸显了对新生物标志物的需求.
  • 不调节的编程细胞死亡 (PCD) 途径与癌症有关,但它们在侵入性PitNET中的作用尚不清楚.

研究的目的:

  • 为了识别攻击性PitNETs的新型分子生物标志物.
  • 调查PCD途径在侵入性PitNET中的预后相关性.
  • 开发风险分层和个性化治疗的预测指数.

主要方法:

  • 对GEO数据集 (GSE51618,GSE169498,GSE260487) 的差异基因表达分析,比较非侵入性和侵入性PitNETs.
  • 整合了一个1548基因的PCD相关基因组.
  • 机器学习 (LASSO,SVM-RFE) 来构建PCD相关指数 (PCDI);通过ROC分析,免疫透评估和RT-qPCR进行验证.

主要成果:

  • 11基因的PCDI精确地区分了侵入性和非侵入性的PitNET.
  • 高PCDI瘤显示丰富的代谢途径和免疫激活.
  • 共识聚类确定了两种亚型;C2 (高PCDI) 呈现出增加的免疫评分和通路活性,关键基因表达经过实验验证.

结论:

  • 通过整合PCD-免疫-代谢交叉通话,PCDI超越了传统模型,从而提高了侵入性PitNET的预后准确性.
  • 高PCDI瘤显示免疫逃避尽管检查点分子表达,表明结合MAPK抑制剂和免疫治疗潜力.
  • PCDI提供了一个分子框架,用于风险分层和对侵入性PitNET的个性化治疗.