Long Xinrui, Sun Kai, Lai Sicen, Liu Yuancheng, Su Juan, Chen Wangqing, Liu Ruhan, He Xiaoyu, Zhao Shuang, Huang Kai
Department of Dermatology, Xiangya Hospital, Central South University, Changsha 410028, China.
Xiangya School of Medicine, Central South University, Changsha 410031, China.
Acta Pharm Sin B. 2025 Jul;15(7):3355-3371. doi: 10.1016/j.apsb.2025.05.009. Epub 2025 May 21.
Drug resistance is one of the key factors affecting the effectiveness of cancer treatment methods, including chemotherapy, radiotherapy, and immunotherapy. Its occurrence is related to factors such as mRNA expression and methylation within cancer cells. If drug resistance in patients can be accurately identified early, doctors can devise more effective treatment plans, which is of great significance for improving patients' survival rates and quality of life. Cancer drug resistance prediction based on artificial intelligence (AI) technology has emerged as a current research hotspot, demonstrating promising application prospects in guiding clinical individualized and precise medication for cancer patients. This review aims to comprehensively summarize the research progress in utilizing AI algorithms to analyze multi-omics data including genomics, transcriptomics, epigenomics, proteomics, metabolomics, radiomics, and histopathology, for predicting cancer drug resistance. It provides a detailed exposition of the processes involved in data processing and model construction, examines the current challenges faced in this field and future development directions, with the aim of better advancing the progress of precision medicine.
耐药性是影响癌症治疗方法有效性的关键因素之一，这些治疗方法包括化疗、放疗和免疫疗法。其发生与癌细胞内的mRNA表达和甲基化等因素有关。如果能够早期准确识别患者的耐药性，医生就能制定出更有效的治疗方案，这对于提高患者的生存率和生活质量具有重要意义。基于人工智能（AI）技术的癌症耐药性预测已成为当前的研究热点，在指导癌症患者临床个体化精准用药方面展现出广阔的应用前景。本综述旨在全面总结利用AI算法分析包括基因组学、转录组学、表观基因组学、蛋白质组学、代谢组学、放射组学和组织病理学在内的多组学数据以预测癌症耐药性的研究进展。它详细阐述了数据处理和模型构建所涉及的过程，审视了该领域当前面临的挑战和未来发展方向，旨在更好地推动精准医学的进展。
