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Kinic index: an artificial intelligence-driven predictive model and multitarget drug discovery framework for hepatocellular carcinoma patients 期刊论文

编号：

F809E0D9F497B9761170D4EFE37C280C
作者：

Zhou, Jinglin#^[1,2]Jiang, Yuhan^[3];Yu, Miao^[1,2];Wang, Mengyuan^[1,2];Li, Yixiao^[1,2];Ji, Dengbo(吉登波)*^[4]Zhan, Jun*^[1,2]Zhang, Hongquan*^[1,2,5]
语种：

英文
期刊：

NPJ PRECISION ONCOLOGY ISSN：2397-768X 2026 年 10 卷 1 期 ; FEB 14
收录：
摘要：

Hepatocellular carcinoma (HCC) remains a major global health challenge due to its molecular heterogeneity, late diagnosis, and limited therapeutic options. Recent studies have identified isonicotinylation (Kinic), a novel lysine acylation, as a regulatory modification influencing carcinogenic protein activity and liver cancer progression. In this study, we established the Kinic Index (KinicI), an artificial intelligence (AI)-driven predictive model that integrates multi-omics data and consensus clustering to classify HCC patients into two distinct Kinic subgroups. Patients in the high-Kinic subgroup exhibited significantly worse overall survival, demonstrating the value of KinicI for risk stratification and outcome prediction. Machine learning approaches (LASSO, RSF) coupled with Shapley additive explanation (SHAP) analysis identified CYP2C9 and G6PD as the most influential prognostic variables associated with HCC progression. Single-cell and spatial transcriptomic analyses confirmed that CYP2C9 and G6PD are primarily localized in malignant hepatocytes with high metastatic potential, underscoring their clinical relevance. Importantly, using the GraphBAN deep learning framework and ADMET-AI screening, we prioritized candidate compounds targeting CYP2C9 and G6PD, followed by molecular docking that validated strong binding affinities, suggesting their potential as novel therapeutics. Together, our study demonstrates that KinicI is a powerful AI-enabled platform for prognostic modeling, molecular stratification, and multitarget drug discovery, providing a foundation for precision oncology and resistance-aware treatment strategies in HCC patients.
推荐引用方式
GB/T 7714：

Zhou Jinglin,Jiang Yuhan,Yu Miao, et al. Kinic index: an artificial intelligence-driven predictive model and multitarget drug discovery framework for hepatocellular carcinoma patients [J].NPJ PRECISION ONCOLOGY,2026,10(1).
APA：

Zhou Jinglin,Jiang Yuhan,Yu Miao,Wang Mengyuan,&Zhang Hongquan.(2026).Kinic index: an artificial intelligence-driven predictive model and multitarget drug discovery framework for hepatocellular carcinoma patients .NPJ PRECISION ONCOLOGY,10(1).
MLA：

Zhou Jinglin, et al. "Kinic index: an artificial intelligence-driven predictive model and multitarget drug discovery framework for hepatocellular carcinoma patients" .NPJ PRECISION ONCOLOGY 10,1(2026).
入库时间：

4/8/2026 9:50:11 PM
更新时间：

4/8/2026 9:50:11 PM

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