Bu Fan, Ling Zhi-Qiang
Zhejiang Cancer Institute, Zhejiang Cancer Hospital, Hangzhou, Zhejiang, China.
The Second Clinical Medical College of Zhejiang Chinese Medicine University, Hangzhou, People's Republic of China.
Cancer Med. 2026 Feb;15(2):e71578. doi: 10.1002/cam4.71578.
Lung cancer remains the leading cause of cancer-related mortality worldwide, highlighting the urgent need for earlier detection within real-world screening and patient management pathways. Recent advances in multi-omics technologies have created new opportunities for identifying biomarkers associated with early-stage lung cancer, particularly in high-risk populations under clinical surveillance.
This review systematically evaluates early diagnostic biomarkers across multiple omics layers, including genomics, epigenomics, transcriptomics, proteomics, metabolomics and microbiomics. It also summarises the application of artificial intelligence (AI), particularly machine learning and deep learning approaches, for integrating and analysing complex multi-omics datasets to support biomarker discovery and clinical decision-making.
Multi-omics strategies are accelerating the identification of molecular signatures relevant to early lung cancer detection. AI-driven methods enable the extraction of latent patterns from high-dimensional data, facilitating risk stratification, diagnostic refinement, histological subtyping and treatment planning. The review highlights the clinical utility of these biomarkers and their potential incorporation into screening algorithms, as well as the development of AI-based clinical decision support systems (CDSS) aligned with real-world clinical workflows. However, major barriers to clinical translation remain, including multi-centre data heterogeneity, limited model interpretability affecting clinical trust, regulatory and cost-effectiveness challenges and insufficient validation in prospective cohorts.
Emerging technologies, such as single-cell and spatial multi-omics, along with federated learning frameworks, offer promising solutions to bridge the gap between computational discovery and clinical implementation. The integration of AI and multi-omics approaches has the potential to advance risk-adapted and personalised early detection strategies for lung cancer.
肺癌仍然是全球癌症相关死亡的主要原因，凸显了在现实世界的筛查和患者管理途径中尽早检测的迫切需求。多组学技术的最新进展为识别与早期肺癌相关的生物标志物创造了新机会，特别是在临床监测下的高危人群中。
本综述系统评估了多个组学层面的早期诊断生物标志物，包括基因组学、表观基因组学、转录组学、蛋白质组学、代谢组学和微生物组学。它还总结了人工智能（AI）的应用，特别是机器学习和深度学习方法，用于整合和分析复杂的多组学数据集，以支持生物标志物发现和临床决策。
多组学策略正在加速与早期肺癌检测相关的分子特征的识别。人工智能驱动的方法能够从高维数据中提取潜在模式，促进风险分层、诊断细化、组织学亚型分类和治疗规划。该综述强调了这些生物标志物的临床实用性及其纳入筛查算法的潜力，以及与现实世界临床工作流程相一致的基于人工智能的临床决策支持系统（CDSS）的开发。然而，临床转化仍存在主要障碍，包括多中心数据异质性、影响临床信任的有限模型可解释性、监管和成本效益挑战以及前瞻性队列中验证不足。
单细胞和空间多组学等新兴技术，以及联邦学习框架，为弥合计算发现与临床实施之间的差距提供了有前景的解决方案。人工智能与多组学方法的整合有可能推进肺癌的风险适应性和个性化早期检测策略。
