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可解释的机器学习揭示了全凋亡在非酒精性脂肪性肝病诊断和亚型分类中的作用。

Interpretable Machine Learning reveals the Role of PANoptosis in the Diagnosis and Subtyping of NAFLD.

作者信息

Tian Feng, Zhao Yuqi, He Xinyang, Zhang Yu, Hu Minxuan, Liang Yiwei, Tian Ziyou, Gao Yaxian, Wang Yongwei

机构信息

Clinical College of Chengde Medical University, Chengde 067000,China.

Traditional Chinese Medicine College of Chengde Medical University, Chengde 067000,China.

出版信息

Immunobiology. 2025 May;230(3):152909. doi: 10.1016/j.imbio.2025.152909. Epub 2025 Apr 26.

DOI:10.1016/j.imbio.2025.152909
PMID:40311345
Abstract

Non-alcoholic fatty liver disease (NAFLD) is a global health challenge characterized by complex pathogenesis and limited therapeutic options. Emerging evidence highlights PANoptosis-a coordinated interplay of pyroptosis, apoptosis, and necroptosis-as a critical driver of metabolic and immune dysregulation in NAFLD. Here, we integrated multiple datasets and interpretable machine learning to unravel the role of PANoptosis in NAFLD diagnosis, subtyping, and immune microenvironment remodeling. By intersecting differentially expressed genes and PANoptosis-related genes, we identified 9 hub genes (e.g., TRADD, CASP6, TNFRSF1A and TNFAIP3) and constructed a robust diagnostic model (AUC = 0.976) validated via SHAP analysis and nomogram. Unsupervised consensus clustering stratified NAFLD patients into two PANoptosis-driven subtypes (C1/C2 and CA/CB), revealing distinct immune cell infiltration patterns and pathway activation. Single-cell sequencing further localized hub genes to immune cells and revealed their cell communication, implicating their roles in the progression of NAFLD. Molecular docking studies identified fostamatinib and minocycline as potential therapeutic candidates, while pan-cancer analysis revealed that TNFRSF1A overexpression is associated with poor prognosis across multiple cancer types. This study enhances the understanding of PANoptosis as a crucial diagnostic and therapeutic target in NAFLD, providing novel insights into immune-mediated pathogenesis and paving the way for treatment strategies.

摘要

非酒精性脂肪性肝病(NAFLD)是一项全球性的健康挑战,其发病机制复杂,治疗选择有限。新出现的证据表明,PAN细胞焦亡(一种由细胞焦亡、凋亡和坏死性凋亡协调相互作用而成的过程)是NAFLD中代谢和免疫失调的关键驱动因素。在这里,我们整合了多个数据集和可解释的机器学习方法,以阐明PAN细胞焦亡在NAFLD诊断、亚型分类和免疫微环境重塑中的作用。通过交叉分析差异表达基因和PAN细胞焦亡相关基因,我们鉴定出9个核心基因(如TRADD、CASP6、TNFRSF1A和TNFAIP3),并构建了一个通过SHAP分析和列线图验证的强大诊断模型(AUC = 0.976)。无监督一致性聚类将NAFLD患者分为两种由PAN细胞焦亡驱动的亚型(C1/C2和CA/CB),揭示了不同的免疫细胞浸润模式和通路激活情况。单细胞测序进一步将核心基因定位到免疫细胞,并揭示了它们的细胞通讯,暗示了它们在NAFLD进展中的作用。分子对接研究确定了 fostamatinib和米诺环素为潜在的治疗候选药物,而泛癌分析显示TNFRSF1A的过表达与多种癌症类型的不良预后相关。这项研究加深了我们对PAN细胞焦亡作为NAFLD关键诊断和治疗靶点的理解,为免疫介导的发病机制提供了新的见解,并为治疗策略铺平了道路。

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