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一个基于化学基因的集成机器学习模型预测了肝细胞癌的预后并指导了免疫治疗

Jinlong Zhou¹, Junjie Liu², Xinrong Wei¹

¹Department of Liver Transplantation and Laparoscopic Surgery, The First Affiliated Hospital of Xinjiang Medical University, Urumqi, China.

|December 23, 2025

概括

这项研究开发了一种机器学习模型,使用化学基因基因将肝细胞癌 (HCC) 患者分类为具有不同生存率和免疫特征的亚型. 该模型确定了预测预后和免疫治疗反应的关键基因,有助于个性化HCC治疗.

科学领域:

在瘤学瘤学.
免疫学免疫学免疫学
生物信息学是一种生物信息学.

背景情况:

肝细胞癌 (HCC) 呈现出显著的预后异质性和对免疫检查点抑制剂的有限反应.
应对这些挑战需要新的方法来分层患者和预测治疗疗效.

研究的目的:

使用机器学习 (ML) 集成与化学因相关的基因表达特征,以定义HCC亚型并开发预测模型.
调查已识别的HCC亚型的免疫和代谢特征.
为了确定潜在的生物标志物用于预后和免疫疗法响应在HCC.

主要方法:

从TCGA和GSE14520数据集中对227个基因相关基因进行无监督的共识聚类.
使用10ML算法和10倍交叉验证的预测模型的开发.
使用ESTIMATE,ssGSEA和immunophenoscore进行免疫透,通路丰富和免疫治疗敏感性的分析.

主要成果:

鉴定出了两种不同的与化学激素相关的HCC亚型 (A和B),其中B亚型的整体存活率明显更好.
一个92基因的预后模型 (StepCox+SuperPC) 显示出强大的预测性能 (C指数:0.719/0.653).
高风险患者表现出新陈代谢重编程和免疫抑制;低风险患者表现出免疫-流体协同作用. 确定了11个基因签名,其中SPP1和SLC1A2与预后不佳有关,ITGAM/P2RY6与免疫治疗反应相关.

结论:

一个基于化学基因的ML模型有效地将HCC患者分为具有明显生存和免疫代谢特征的亚型.
一个11基因的预后特征提供了对HCC进展的洞察力,并可以预测免疫治疗反应.
这些发现支持通过利用化基因-免疫-代谢网络,为HCC提供个性化治疗策略.

关键词:

化学物质 (chemokines) 是一种化学物质.肝细胞癌是肝细胞癌.免疫疗法反应的免疫疗法反应机器学习是机器学习.瘤微环境是一个微环境.

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