基于胆管癌类器官的吉西他滨耐药基因特征构建与验证

doi:10.16352/j.issn.1001-6325.2026.07.0904

基础医学与临床 ›› 2026, Vol. 46 ›› Issue (7): 904-912.doi: 10.16352/j.issn.1001-6325.2026.07.0904

基于胆管癌类器官的吉西他滨耐药基因特征构建与验证

陈子然, 黄容, 卢艳, 李凯, 宋伟^*

中国医学科学院北京协和医学院基础医学研究所重大疾病共性机制研究全国重点实验室,北京 100005

收稿日期:2026-03-10 修回日期:2026-05-19 发布日期:2026-06-23
通讯作者: ^*songwei@ibms.pumc.edu.cn
基金资助:
国家重点研发计划(2022YFA0806302);国家自然科学基金(32571012);中国医学科学院医学与健康科技创新工程(2025-I2M-TS-03,2025-I2M-XHXX-065)

Construction and validation of a gemcitabine resistance gene signature based on cholangiocarcinoma organoids

CHEN Ziran, HUANG Rong, LU Yan, LI Kai, SONG Wei^*

State Key Laboratory of Common Mechanism Research for Major Diseases, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100005, China

Received:2026-03-10 Revised:2026-05-19 Published:2026-06-23
Contact: ^*songwei@ibms.pumc.edu.cn

摘要/Abstract

摘要： 目的利用胆管癌患者来源类器官(PDOs)吉西他滨(GEM)药敏差异构建耐药转录特征,并验证其生物学一致性与药敏相关性。方法基于4例胆管癌PDOs转录组及GEM药敏数据,分析Hallmark通路及预排序基因集富集分析(GSEA),构建23基因耐药特征(signature)。在TCGA-CHOL与基因表达综合数据库队列(GSE236894)验证通路相关模式,在CCLE/PharmacoDB数据库评估其与药敏指标的关联,并结合细胞实验进行验证。结果 GEM耐药信号主要富集于细胞周期及E2F靶基因通路。构建的23基因特征在PDOs中与药敏剂量-反应曲线下面积(AUC)正相关;在TCGA及GEO队列中,该特征与增殖标志物(TOP2A、MKI67等)及细胞周期通路活性显著正相关。数据库验证显示,特征分数与GEM药敏曲线上面积重算值(GEM AAC_recomputed)呈显著正相关(ρ=0.149,P＜0.001),高特征组耐药程度显著更高。实验证实,该特征相关基因在耐药细胞中维持表达而在敏感细胞中被抑制。结论本研究构建的23基因特征能稳定反映胆管癌GEM耐药状态,具有良好的生物学可解释性与跨队列泛化能力。

关键词: 胆管癌, 患者来源类器官, 吉西他滨耐药, 基因特征, 生物信息学分析

Abstract: Objective To develop a transcriptional signature of gemcitabine (GEM) resistance using patient-derived organoids (PDOs) from cholangiocarcinoma and to validate its biological consistency and predictive relevance. Methods Transcriptomic profiles and drug sensitivity data from four PDOs were collected and analyzed through Hallmark pathway enrichment and pre-ranked GSEA in order to construct a 23-gene resistance signature. The associated pathway patterns were validated in TCGA-CHOL and GSE236894 cohorts, while correlations with drug response metrics were assessed with CCLE and PharmacoDB, complemented by experimental validation in cell models. Results GEM resistance was primarily enriched in cell cycle and E2F target pathways. The 23-gene signature was positively correlated with AUC in PDOs and significantly associated with proliferation markers (e.g., TOP2A, MKI67) and cell cycle activity in independent cohorts. Database analyses further demonstrated a significant positive correlation between the signature score and GEM AAC_recomputed (ρ=0.149, P＜0.001), with higher scores which indicated a stronger resistance. Experimental findings confirmed that signature-related genes remained expressed in resistant cells but were suppressed in sensitive cells. Conclusions This signature provides a robust and biologically interpretable indicator of GEM resistance in cholangiocarcinoma, with strong generalizability across datasets.

Key words: cholangiocarcinoma, patient-derived organoids, gemcitabine resistance, gene signature, bioinformatics analysis

中图分类号:

陈子然, 黄容, 卢艳, 李凯, 宋伟. 基于胆管癌类器官的吉西他滨耐药基因特征构建与验证[J]. 基础医学与临床, 2026, 46(7): 904-912.

CHEN Ziran, HUANG Rong, LU Yan, LI Kai, SONG Wei. Construction and validation of a gemcitabine resistance gene signature based on cholangiocarcinoma organoids[J]. Basic & Clinical Medicine, 2026, 46(7): 904-912.

参考文献

[1] Bañales JM, Marin JJG, Lamarca A, et al. Cholangiocarcinoma 2020: the next horizon in mechanisms and management[J]. Nat Rev Gastroenterol Hepatol, 2020, 17: 557-588. doi: 10.1038/s41575-020-0310-z.
[2] Maier CF, Zhu L, Nanduri LK, et al. Patient-derived organoids of cholangiocarcinoma[J]. Int J Mol Sci, 2021, 22: 8675. doi: 10.3390/ijms22168675.
[3] Ren X, Huang M, Weng W, et al. Personalized drug screening in patient-derived organoids of biliary tract cancer and its clinical application[J]. Cell Rep Med, 2023, 4: 101277. doi: 10.1016/j.xcrm.2023.101277.
[4] 王靖宇. 胰腺癌与胆管癌类器官的建立及药物敏感性检测研究[D]. 呼和浩特: 内蒙古医科大学, 2024: 58-59.
[5] Liberzon A, Birger C, Thorvaldsdóttir H, et al. The molecular signatures database (MSigDB) hallmark gene set collection[J]. Cell Syst, 2015, 1: 417-425. doi: 10.1016/j.cels.2015.12.004.
[6] Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data[J]. BMC Bioinformatics, 2013, 14: 7. doi: 10.1186/1471-2105-14-7.
[7] Ghandi M, Huang FW, Jané-Valbuena J, et al. Next-generation characterization of the Cancer Cell Line Encyclopedia[J]. Nature, 2019, 569: 503-508. doi: 10.1038/s41586-019-1186-3.
[8] Smirnov P, Feizi N, Nair SK, et al. PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis[J]. Nucleic Acids Res, 2024, 52: D1402-D1409. doi: 10.1093/nar/gkad1063.
[9] National Cancer Institute. Genomic data vommons (GDC) documentation: mRNA analysis pipeline[EB/OL]. [2026-02-09]. https://docs.gdc.cancer.gov/Data/Bioinformatics_Pipelines/Expression_mRNA_Pipeline/.
[10] National Cancer Institute. Genomic data commons (GDC): TCGA code tables-sample type codes[EB/OL]. [2026-02-09]. https://gdc.cancer.gov/resources-tcga-users/tcga-code-tables/sample-type-codes.
[11] Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2[J]. Genome Biol, 2014, 15: 550. doi: 10.1186/s13059-014-0550-8.
[12] Subramanian A, Tamayo P, Mootha VK, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles[J]. Proc Natl Acad Sci U S A, 2005, 102: 15545-15550. doi: 10.1073/pnas.0506580102.
[13] James G, Witten D, Hastie T, et al. An introduction to statistical learning[M]. New York: Springer, 2013: 176-178.
[14] Simon R, Radmacher MD, Dobbin K, et al. Pitfalls in the use of DNA microarray data for diagnostic and prognostic classification[J]. J Natl Cancer Inst, 2003, 95: 14-18. doi: 10.1093/jnci/95.1.14.
[15] PharmacoGx Bioconductor Team. PharmacoGx: analysis of pharmacogenomic data[EB/OL]. [2026-02-09]. https://bioconductor.org/packages/release/bioc/html/Phar-macoGx.html.

基于胆管癌类器官的吉西他滨耐药基因特征构建与验证

Construction and validation of a gemcitabine resistance gene signature based on cholangiocarcinoma organoids

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 9

编辑推荐

Metrics

本文评价

[1]	鄢雯, 李囡, 古同男, 孔祥照. 从GEO数据库筛选结肠癌差异关键基因及验证[J]. 基础医学与临床, 2023, 43(11): 1685-1692.
[2]	陈秋霞, 杨黎星, 马瑞, 赵永晶, 王钰铖, 鞠瑞, 郭磊. 羧胺三唑乳清酸盐抑制人胰腺癌吉西他滨耐药细胞株增殖及线粒体能量代谢[J]. 基础医学与临床, 2022, 42(4): 570-576.
[3]	姚涵, 徐文彬, 王冬来. 抗肿瘤药物FTY720对p53依赖的基因转录调控[J]. 基础医学与临床, 2021, 41(3): 311-317.
[4]	闫晓俊, 徐文彬, 王冬来. 羧基末端乙酰化修饰调控抑癌蛋白p53转录特异性[J]. 基础医学与临床, 2021, 41(11): 1577-1582.
[5]	王迪, 李晴, 佟伟民, 牛亚梅. 胆管癌中异柠檬酸脱氢酶基因突变潜在靶基因的鉴定[J]. 基础医学与临床, 2020, 40(4): 512-517.
[6]	邢书娟, 董明清, 杨力侠, 罗颖. 两种肺动脉高压模型小鼠的基因表达谱分析[J]. 基础医学与临床, 2020, 40(11): 1462-1467.
[7]	钱建升李宇窦建卫. miR-26a对人肝癌细胞系SMMC-7721增殖和迁移的影响[J]. 基础医学与临床, 2016, 36(10): 1335-1340.
[8]	何江丁剑冰余伍忠邹红云. 多肿瘤标记物蛋白芯片对胆管癌诊断的临床评价 [J]. 基础医学与临床, 2011, 31(8): 900-904.
[9]	吴良洪程南生杨帆熊先泽魏大鹏夏庆杰. RGZ对PPARgamma在裸小鼠移植瘤内表达的调控研究[J]. 基础医学与临床, 2008, 28(2): 128-132.