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在肝细胞癌中基于生物标志物的免疫治疗反应的精确预测

Hsu-Wen Chao^1,2,3, Yi-Mei Joy Lin⁴, Chen-Shiou Wu⁵

¹Department of Physiology, School of Medicine, College of Medicine, Taipei Medical University, Taipei 11031, Taiwan.

Diagnostics (Basel, Switzerland)

|January 10, 2026

概括

一个新的五基因代谢特征可以预测肝细胞癌 (HCC) 免疫治疗反应. 这种生物标志物小组有助于选择免疫检查点抑制剂 (ICI) 治疗的患者,改善晚期HCC的结果.

科学领域:

在瘤学瘤学.
免疫学免疫学免疫学
基因组学就是基因组学.

背景情况:

肝细胞癌 (HCC) 对全球健康造成重大负担,其先进治疗选择有限.
免疫检查点抑制剂 (ICI) 有好处,但低反应率需要预测生物标志物.
代谢重编程极大地影响免疫反应,突出其在HCC免疫治疗中的潜力.

研究的目的:

识别一种代谢基因特征,用于预测HCC.中的免疫治疗反应.
开发一种用于HCC治疗患者分层的预测模型.

主要方法:

从35个ICI处理的HCC样本中整合了三个独立的转录组数据集.
机器学习 (LASSO,随机森林) 的应用用于特征选择和逻辑回归用于预测得分.
差异基因表达分析和元分析以确定关键的代谢基因.

主要成果:

一个五基因的代谢特征 (PLPPR1,CNTN3,HOXA10,HAGLR,ENPP3) 有效地区分了响应者和不响应者.
内部验证证实了签名的辨别能力.
功能分析将HOXA10与免疫逃避,CNTN3与免疫激活联系起来.

结论:

确定了五个基因的签名,作为一个有希望的,生物学上可解释的生物标志物,用于分层HCC患者接受免疫治疗.
这项研究为签名的实用性提供了初步证据,支持在更大的队列中进一步验证.
这些发现为潜在的临床转化为个性化HCC治疗策略铺平了道路.

关键词:

生物标志物发现发现肝细胞癌是肝细胞癌.免疫疗法反应预测免疫疗法反应预测机器学习是机器学习.代谢基因签名代谢基因签名

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Tumor Immunotherapy

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