消化系统疾病类器官的构建应用及其与人工智能整合的研究进展

doi:10.3969/j.issn.1674-8115.2026.08.001

上海交通大学学报（医学版） ›› 2026, Vol. 46 ›› Issue (8): 997-1006.doi: 10.3969/j.issn.1674-8115.2026.08.001

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消化系统疾病类器官的构建应用及其与人工智能整合的研究进展

杨蕊馨, 于颖彦()

上海交通大学医学院附属瑞金医院普外科，上海消化外科研究所暨上海市胃肿瘤重点实验室，上海 200025

收稿日期:2026-01-09 接受日期:2026-04-08 出版日期:2026-08-28 发布日期:2026-08-28
通讯作者: 于颖彦，教授，博士；电子信箱：ruijinhospitalyyy@163.com。
作者简介:第一联系人：杨蕊馨撰写并修改论文，于颖彦审阅论文。所有作者均阅读并同意最终稿件的提交。
基金资助:
国家自然科学基金(82473013);上海市科学技术委员会项目(25DZ2200200);上海市科学技术委员会项目(20DZ2201900);上海市科学技术委员会项目(18411953100);教育部-上海市生物医药临床研究与转化协同创新中心项目(CCTS-2022202);教育部-上海市生物医药临床研究与转化协同创新中心项目(CCTS-202302);中国博士后科学基金(2025M782160)

Research progress in the construction and application of digestive system disease organoids and their integration with artificial intelligence

Yang Ruixin, Yu Yingyan()

Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine; Shanghai Institute of Digestive Surgery; Shanghai Key Laboratory for Gastric Neoplasms, Shanghai 200025, China

Received:2026-01-09 Accepted:2026-04-08 Online:2026-08-28 Published:2026-08-28
Contact: Yu Yingyan, E-mail: ruijinhospitalyyy@163.com.
About author:First author contact:The manuscript was drafted and revised by Yang Ruixin, and reviewed by Yu Yingyan. Both authors have read the final version of paper and consented to its submission.
Supported by:
National Natural Science Foundation of China(82473013);Project of Shanghai Municipal Commission of Science and Technology(25DZ2200200);Collaborative Innovation Center for Clinical and Translational Science by Chinese Ministry of Education & Shanghai(CCTS-2022202);China Postdoctoral Science Foundation(2025M782160)

摘要/Abstract

摘要：

类器官作为新型体外三维培养模型，凭借其高度自组织能力，可以很好地还原其来源组织的形态结构、病理生理功能、基因变异谱系以及药物治疗反应性。与传统的动物模型相比，类器官具有制备周期短、成本低、伦理争议小和通量高等特点，目前被广泛应用于生物医药研究。患者源性类器官模型保留了其来源组织的异质性，可以体外长期扩增传代培养，且低温冻存复苏后依然保持细胞活力，这为人类疾病建模提供了宝贵的平台，并在发病机制探索、新型药物筛选及药物敏感性预测中展现出重要的应用价值。而人工智能（artificial intelligence，AI）的介入加速了类器官构建与应用的自动化与标准化进程。该文系统总结了消化系统良恶性疾病相关类器官研究进展，尤其是不同器官组织源性类器官构建的优化方案，并就消化系统肿瘤样本采集方法、新辅助治疗对类器官构建的影响等进行分析。此外，文章还概述了AI算法在类器官构建及应用中的进展，包括AI辅助的类器官形态表征、细胞活力评估、药物敏感性预测，以及整合AI的自动化类器官平台建设现状。展望未来，AI将在消化系统疾病类器官构建与多种应用场景中发挥重要作用，为疾病建模、分子靶点发现、药物筛选、耐药机制解析等转化应用提供强有力的支撑。

关键词: 消化系统疾病, 类器官, 精准医疗, 人工智能, 新方法学

Abstract:

As a novel in vitro three-dimensional culture model, organoids, with their high self-organizing ability, can recapitulate the morphological structures, pathophysiological functions, genetic variation profiles, and drug treatment responsiveness of their parental tissues. Compared with traditional animal models, organoids have the characteristics of short preparation cycle, low cost, less ethical controversy and high throughput. They are currently widely applied in biomedical research. Patient-derived organoid models retain the heterogeneity of their tissues of origin, can be continuously expanded and passaged in vitro over the long term, and maintain cell viability after cryopreservation and resuscitation. These characteristics provide a valuable platform for human disease modeling and demonstrate significant application value in exploring pathogenesis, screening novel drugs, and predicting drug sensitivity. The intervention of artificial intelligence (AI) has further enhanced the automation and standardization in the construction and application of organoids. This article systematically summarizes the research progress of organoids related to benign and malignant diseases of the digestive system, with particular emphasis on optimization strategies for constructing organoids derived from different organs and tissues. The paper also introduces the collection methods for digestive system tumor samples and the impact of neoadjuvant therapy on organoid construction. In addition, the article summarizes the advances of AI algorithms in organoid construction and applications, including AI-assisted organoid morphological characterization, cell viability assessment, drug sensitivity prediction, and the development of AI-integrated automated organoid platforms. In the future, AI will play a significant role in organoid construction for digestive system disease organoids and their diverse application scenarios, providing strong support for translational applications such as disease modeling, molecular target discovery, drug screening, and elucidation of drug resistance mechanisms.

Key words: digestive system disease, organoid, precision medicine, artificial intelligence (AI), new approach methodology (NAM)

中图分类号:

杨蕊馨, 于颖彦. 消化系统疾病类器官的构建应用及其与人工智能整合的研究进展[J]. 上海交通大学学报（医学版）, 2026, 46(8): 997-1006.

Yang Ruixin, Yu Yingyan. Research progress in the construction and application of digestive system disease organoids and their integration with artificial intelligence[J]. Journal of Shanghai Jiao Tong University (Medical Science), 2026,(8): 997-1006.

图/表 3

参考文献 65

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Site	Biomarker	Differential diagnosis
Esophagus	KRT5, KRT14	Squamous epithelium: KRT5 Glandular epithelium (such as Barrett esophagus): CDX2, MUC2, KRT20
Stomach	TFF1, MUC5AC, MUC6, MUC2, KRT8	Surface epithelium: TFF1 Fundic and corpus gland: MUC6, PGC, ATP4A Basal stem cell: LGR5 Endocrine cell: CHGA, CHGB, SCG5
Intestine	EPCAM, KRT20, CDX2	Duodenum: SLC5A1, CYP3A4 Jejunum: SLC6A19, LCT Ileum: IL22RA1, SLC10A2 Colon: CA2, MUC4
Liver and bile duct	KRT7, KRT19	Liver: ALB, AAT, TTR, APOA2 Bile duct: KRT7, KRT19, SOX9
Pancreas	Endocrine and exocrine glands have different marker genes	Exocrine gland: PRSS1, CPA1, KRT19 Endocrine gland: CHGA, INS, GCG

Site	Biomarker	Differential diagnosis
Esophagus	KRT5, KRT14	Squamous epithelium: KRT5 Glandular epithelium (such as Barrett esophagus): CDX2, MUC2, KRT20
Stomach	TFF1, MUC5AC, MUC6, MUC2, KRT8	Surface epithelium: TFF1 Fundic and corpus gland: MUC6, PGC, ATP4A Basal stem cell: LGR5 Endocrine cell: CHGA, CHGB, SCG5
Intestine	EPCAM, KRT20, CDX2	Duodenum: SLC5A1, CYP3A4 Jejunum: SLC6A19, LCT Ileum: IL22RA1, SLC10A2 Colon: CA2, MUC4
Liver and bile duct	KRT7, KRT19	Liver: ALB, AAT, TTR, APOA2 Bile duct: KRT7, KRT19, SOX9
Pancreas	Endocrine and exocrine glands have different marker genes	Exocrine gland: PRSS1, CPA1, KRT19 Endocrine gland: CHGA, INS, GCG

Sample origin	Sample size	Sample collection requirement	Note
Surgical resection	200 mg (approximately the size of 2 soybeans)	Selecting vascular-rich, soft tissue; avoiding central necrotic areas tissues with marked fibrosis, large adipose aggregates, hard consistency, electrocoagulation artifacts, or yellowish-gray discoloration	Optimizing enzymatic digestion time for pancreatic cancer organoids to suppress excessive stromal cell growth
Needle biopsy	3 pieces	Maximizing cell retention and minimizing cell damage during sample processing and digestion	Magnifying endoscopy can be employed for sample collection to ensure high tumor purity and avoid necrotic areas in specimens
Malignant ascites	50‒100 mL	Collecting sufficient ascites, harvesting cells via centrifugation, and preparing a single-cell suspension after red blood cell lysis	Excess immunocytes can be removed by Ficoll gradient centrifugation

Sample origin	Sample size	Sample collection requirement	Note
Surgical resection	200 mg (approximately the size of 2 soybeans)	Selecting vascular-rich, soft tissue; avoiding central necrotic areas tissues with marked fibrosis, large adipose aggregates, hard consistency, electrocoagulation artifacts, or yellowish-gray discoloration	Optimizing enzymatic digestion time for pancreatic cancer organoids to suppress excessive stromal cell growth
Needle biopsy	3 pieces	Maximizing cell retention and minimizing cell damage during sample processing and digestion	Magnifying endoscopy can be employed for sample collection to ensure high tumor purity and avoid necrotic areas in specimens
Malignant ascites	50‒100 mL	Collecting sufficient ascites, harvesting cells via centrifugation, and preparing a single-cell suspension after red blood cell lysis	Excess immunocytes can be removed by Ficoll gradient centrifugation

消化系统疾病类器官的构建应用及其与人工智能整合的研究进展

Research progress in the construction and application of digestive system disease organoids and their integration with artificial intelligence

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