基尼克指数:人工智能驱动的预测模型和多目标药物发现框架,用于肝细胞癌患者
Jinglin Zhou1, Yuhan Jiang2, Miao Yu1
1Program for Cancer and Cell Biology, School of Basic Medical Sciences, and Peking University International Cancer Institute, State Key Laboratory of Molecular Oncology, Peking University Health Science Center, Beijing, 100191, China.
NPJ precision oncology
|February 14, 2026
概括
一个新的人工智能模型,即异构化指数 (KinicI),将肝细胞癌 (HCC) 患者分为高风险组. 这种方法确定了关键的预后因素和潜在的药物标,以改善肝癌治疗.
科学领域:
- 在瘤学瘤学.
- 生物化学 生物化学
- 生物信息学是一种生物信息学.
背景情况:
- 肝细胞癌 (HCC) 由于其复杂性和有限的治疗选择,给全球健康带来了重大挑战.
- 异基化 (Kinic),一种新型的酸化,正在成为肝癌进展的关键调节剂.
研究的目的:
- 开发一个人工智能驱动的预测模型,Kinic指数 (KinicI),用于分层HCC患者.
- 确定HCC的预后生物标志物和潜在的治疗点.
主要方法:
- 集成的多学科数据和共识聚类,以创建人工智能驱动的KinicI模型.
- 采用机器学习 (LASSO,RSF) 和SHAP分析来确定预后变量.
- 利用单细胞,空间转录组学,GraphBAN,ADMET-AI和分子对接用于目标验证和药物发现.
主要成果:
- KinicI模型成功地将HCC患者分为两个不同的子组,生存率不同.
- 高KinicI小组患者的整体存活率明显降低,验证了KinicI的风险分层.
- 鉴定出CYP2C9和G6PD是临界的预后因素,局部存在于侵袭性恶性肝细胞中.
- 针对CYP2C9和G6PD的候选化合物显示出强烈的结合 afinities,表明治疗潜力.
结论:
- 该KinicI作为一个强大的AI支持的平台,用于HCC预后建模和分子分层.
- 这项研究为HCC的精密瘤学和耐药意识治疗策略提供了基础.
- 确定了新的治疗标和HCC的候选化合物,为药物发现铺平了道路.
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