人工智能驱动的暴露组与多组学整合在肝癌生物标志物发现中的应用：文献综述
Artificial intelligence driven exposome and multi omics integration for biomarker discovery in liver cancer: a literature review.
摘要(中文译文)
原发性肝癌是全球癌症死亡的主要原因之一，肝细胞癌（HCC）是其主要的组织学亚型。本文献综述综合了目前关于暴露组、多组学全景以及基于人工智能（AI）的整合策略在肝癌生物标志物发现中的相关证据，重点关注生物学依据、新兴临床应用及转化局限性。主要病因驱动因素包括病毒性肝炎、酒精相关性肝病和代谢功能障碍相关脂肪性肝病，这些因素均在整个生命过程中与环境暴露相互作用。生物标志物的发现日益依赖于对暴露相关信号与基因组学、表观基因组学、转录组学、蛋白质组学、代谢组学及空间分辨数据的整合评估。肝癌发生涉及慢性肝损伤、环境模式化分子扰动以及动态肿瘤-宿主相互作用的复杂交互。我们强调一种暴露组信息驱动的多模态策略，即可解释的AI模型识别具有临床相关性的特征，用于早期检测、预后分层和治疗指导。当前证据的关键局限性包括暴露评估不完整、数据平台异质性、回顾性研究设计、外部验证有限以及模型透明度不足。新兴方法，包括蛋白质基因组学、脂质组学、单细胞及基于数字病理的建模，显示出前景，但需要在病因学多样化的队列中进一步验证。本综述旨在批判性审视AI如何整合暴露组相关信息与多组学数据以用于肝癌生物标志物的发现。在此，特别关注暴露至生物标志物的序列关系、免疫代谢重塑、液体活检转化以及将高维特征简化为临床可部署的检测方法。
Abstract
Primary liver cancer is a major global cause of cancer death, and hepatocellular carcinoma (HCC) is the predominant histological subtype. This literature review synthesizes current evidence on the exposome, multi-omics landscape, and artificial intelligence (AI)-based integration strategies relevant to biomarker discovery in liver cancer, with a focus on biological rationale, emerging clinical applications, and translational limitations. Key etiologic drivers include viral hepatitis, alcohol-related liver disease, and metabolic dysfunction-associated steatotic liver disease, all of which interact with environmental exposures across the life course. Biomarker discovery increasingly relies on integrated assessment of exposure-related signals together with genomic, epigenomic, transcriptomic, proteomic, metabolomic, and spatially resolved data. Hepatocarcinogenesis involves a complex interplay of chronic liver injury, environmentally patterned molecular perturbation, and dynamic tumor-host interactions. We emphasize an exposome-informed, multimodal strategy in which interpretable AI models identify clinically relevant signatures for early detection, prognostic stratification, and treatment guidance. Critical limitations of current evidence include incomplete exposure assessment, heterogeneous data platforms, retrospective study design, limited external validation, and insufficient model transparency. Emerging approaches, including proteogenomic, lipidomic, single-cell, and digital pathology-based modeling, show promise but require further validation in etiologically diverse cohorts. The purpose of this review is to critically examine how AI can integrate exposome-related information with multi-omics data for biomarker discovery in liver cancer. Here, particular attention is given to the exposure-to-biomarker sequence, immune-metabolic remodeling, liquid-biopsy translation, and the reduction of high-dimensional signatures into clinically deployable assays.
如何引用
AMA
Xuelian Wang, Yunjing Gao, Ran Xu, Xin Lan, Qiushi Huang. Artificial intelligence driven exposome and multi omics integration for biomarker discovery in liver cancer: a literature review.. Frontiers in immunology. 2026; doi:10.3389/fimmu.2026.1866011.
APA
Xuelian Wang, Yunjing Gao, Ran Xu, Xin Lan, Qiushi Huang (2026). Artificial intelligence driven exposome and multi omics integration for biomarker discovery in liver cancer: a literature review.. Frontiers in immunology. https://doi.org/10.3389/fimmu.2026.1866011
GB/T 7714
Xuelian Wang, Yunjing Gao, Ran Xu, Xin Lan, Qiushi Huang. Artificial intelligence driven exposome and multi omics integration for biomarker discovery in liver cancer: a literature review.[J]. Frontiers in immunology, 2026 doi:10.3389/fimmu.2026.1866011.
在 PubMed增强版 阅读全文
翻译全文 · AI 文献问答 · PDF 原文下载 · 引用导出