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在清细胞癌中,机器学习衍生出的预后特征整合了编程细胞死亡和线粒体功能:确定PIF1作为新的标

Guangyang Cheng¹, Zhaokai Zhou^1,2, Shiqi Li¹

¹Department of Urology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China.

Cancer immunology, immunotherapy : CII

|February 25, 2025

概括

一种新的机器学习模型 (mpMLDPS) 通过分析线粒体功能和编程细胞死亡,有效地预测细胞癌 (RCC) 患者的预后. 该研究还确定了PIF1作为RCC的潜在治疗标.

科学领域:

线粒体生物学线粒体生物学
癌症研究癌症研究
编程细胞死亡编程细胞死亡

背景情况:

细胞癌 (RCC) 的发病过程涉及复杂的编程细胞死亡 (PCD) 途径.
线粒体,作为细胞的发电站,显著影响细胞死亡机制.
在RCC中,线粒体功能 (MF) 和PCD相互作用的预后影响尚不清楚.

研究的目的:

为了研究线粒体功能的预后意义和编程细胞死亡在细胞癌.
开发一个机器学习衍生的预后签名 (mpMLDPS),集成MF和PCD.
确定RCC的新型预后标记和治疗点.

主要方法:

编制了PCD和MF的基因组.
一个机器学习框架分析了跨患者队伍的MF-PCD关系.
一个预后签名 (mpMLDPS) 被开发和验证.
生存和临床相关性分析确定了预后标志物,并进行了体外验证.

主要成果:

该mpMLDPS签名与RCC患者的预后有很强的相关性,较高的得分表明结果更差.
mpMLDPS与免疫检查点,瘤微环境,人体突变和药物敏感性有关.
确定了PIF1作为一种新的预后标志物;它的敲击在体外抑制了癌细胞的进展.

结论:

mpMLDPS签名显示了作为RCC预后评估的可靠临床工具的潜力.
准确的预后可以指导RCC患者的治疗方案选择.
PIF1为RCC的临床治疗提供了一个有前途的新目标.

关键词:

脏的清晰细胞癌.机器学习机器学习线粒体的功能在PIF1中,PIF1是PIF1,PIF1是PIF1.预测的签名预测的签名编程细胞死亡编程细胞死亡

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