组学
个性化医疗
精密医学
表观遗传学
蛋白质组学
基因组学
计算机科学
计算生物学
数据科学
医学
生物信息学
生物
基因组
病理
基因
基因表达
生物化学
DNA甲基化
作者
Xiaohui Wen,Yaran Wang,Chao Su,Yanyi You,Ziqing Jiang,Daoqi Zhu,Qin Fan
标识
DOI:10.1093/qjmed/hcaf103
摘要
Abstract Cancer remains a formidable global health challenge owing to its complexity, including tumor heterogeneity and intricate regulatory networks. Traditional Chinese medicine (TCM) offers unique multi-targeted therapeutic approaches with demonstrated benefits such as improved prognosis, reduced side effects, and long-term tumor stabilization. This review explores the convergence of multi-omics technologies—genomics, transcriptomics, proteomics, metabolomics, and epigenomics—with TCM to elucidate the molecular mechanisms underlying its anti-cancer effects. By integrating omics data, researchers can uncover regulatory networks, identify therapeutic targets, and validate the efficacy of TCM. Advances in single-cell omics, spatial omics, and machine learning are creating new opportunities for personalized TCM-based therapies. However, translating these findings into clinical applications remains challenging. This review highlights the potential of omics-integrated TCM in addressing cancer complexity and proposes actionable strategies for overcoming research and application barriers, thereby facilitating the development of innovative and effective treatment options.
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