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对ccRCC线粒体相关基因机器学习模型的构建和多omics分析以及关键基因FKBP10的验证

Shihang Pu¹, Shijie Qian¹, Jiajun Xing¹

¹Department of Urology, First Affiliated Hospital of Nanjing Medical University, No.300, Guangzhou Street, Nanjing, Jiangsu Province 210029, China.

International immunopharmacology

|September 12, 2025

概括

线粒体功能障碍是清细胞细胞癌 (ccRCC) 的关键. 我们确定了7个核心基因,包括FKBP10,它通过IL-6/JAK/STAT3通路驱动ccRCC,提供了新的治疗点.

科学领域:

在瘤学瘤学.
线粒体生物学线粒体生物学
生物信息学是一种生物信息学.

背景情况:

清细胞细胞癌 (ccRCC) 是最常见的癌.
线粒体功能障碍越来越被认为是癌症发展和进展的驱动因素.
了解线粒体在ccRCC中的作用对于确定新的治疗策略至关重要.

研究的目的:

为了识别和表征参与ccRCC病变的线粒体相关蛋白质.
开发基于线粒体基因的ccRCC的预后风险模型.
通过实验验证一个关键的基因在ccRCC中的作用.

主要方法:

综合分析多主题数据 (TCGA-KIRC,GEO) 和MitoCarta3.0数据库.
使用整体机器学习 (10个算法) 开发和优化预后风险模型.
在ccRCC中对核心基因FKBP10的体内实验验证.

主要成果:

确定了102个与线粒体相关的具有预后意义的差异表达基因.
选择LASSO+GBM算法来导出一个7核基因预后模型.
FKBP10通过IL-6/JAK/STAT3通路激活促进ccRCC恶性病变,具有明显的高风险和低风险组特征.

结论:

这项研究提供了基于线粒体基因的ccRCC强大的预后模型.
FKBP10是通过IL-6/JAK/STAT3通路进行ccRCC进展的关键驱动因素.
这些发现突出了ccRCC治疗的潜在治疗目标.

关键词:

在 CAF 中,CAF 是 CAF.在 FKBP1010 里.机器学习是机器学习.与线粒体相关的基因 ccRCCCC 在线观看

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