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在乳头细胞癌中代谢异质性和生存结果:来自多数据集和机器学习分析的见解

Jian Hu¹, Yi-Heng Liu¹, Gui-Lian Xu²

¹Urinary Nephropathy Center, The Second Affiliated Hospital, Chongqing Medical University, Chongqing, China.

|September 27, 2025

概括

这项研究确定了脏乳头细胞癌 (KIRP) 中的关键代谢相关基因,并开发了一种机器学习模型,用于准确的预后预测和改进治疗策略.

科学领域:

在瘤学瘤学.
代谢学代谢学代谢学
生物信息学是一种生物信息学.

背景情况:

细胞癌 (RCC) 呈现出显著的免疫和代谢变化.
代谢重编程驱动瘤细胞的增殖和透到RCC.
脏乳头细胞癌 (KIRP) 是一种具有明显代谢特征的RCC亚型.

研究的目的:

描述KIRP中的与代谢相关的分子.
为了确定KIRP的新型预后生物标志物.
开发KIRP中患者结果的预测模型.

主要方法:

使用权重基因共同表达网络分析和差异表达分析对与代谢相关的基因进行全面分析.
使用 90 个机器学习算法构建一种与代谢相关的签名 (MRS).
基于考克斯回归的预测名录的开发,其次是功能丰富,基因组变异,化疗反应和免疫细胞透分析.
使用单细胞RNA测序,定量PCR和免疫组织化学染色来验证关键基因.

主要成果:

鉴定出16种不同表达的代谢基因.
随机生存森林模型显示最佳预测性能 (AUC=0.989为5年生存期).
MRS是一个独立的预后因素,与瘤阶段相关,并预测对特定化疗 (苏尼提尼布,帕佐帕尼布,伦瓦提尼布,特姆西罗利斯) 的反应.
构建了一个四基因名录,并验证了PYCR1,INMT和KIF20A在KIRP中高度表达.

结论:

为KIRP建立了一个预测机器学习模型,揭示了代谢异质性.
该模型增强了KIRP患者的风险分层.
这些发现可以优化化疗策略和KIRP中的患者管理.

关键词:

机器学习机器学习代谢重新编程的新陈代谢预测预后预测预测脏皮质细胞癌是脏的皮质细胞癌.

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