中国组织工程研究

中国组织工程研究 ›› 2026, Vol. 30 ›› Issue (13): 3331-3342.doi: 10.12307/2026.162

• 干细胞培养与分化 stem cell culture and differentiation • 上一篇    下一篇

慢性粒细胞白血病伊马替尼耐药核心基因的生物信息学筛选及实验验证

周  曼1，2，龙梅婷1，2，辛国燕1，黄梦君1，2，姚正联1，2，赵华娟1，2，申林强1，2，吴西军3，杨小燕1，2   

  1. 1贵州医科大学，贵州省贵阳市   550004；贵州医科大学附属医院，2儿童血液科，3科研处，贵州省贵阳市   550004
  • 接受日期:2025-08-30 出版日期:2026-05-08 发布日期:2025-12-25
  • 通讯作者: 杨小燕，博士，主任医师，贵州医科大学，贵州省贵阳市 550004；贵州医科大学附属医院儿童血液科，贵州省贵阳市 550004； 共同通讯作者：吴西军，博士，副主任技师，贵州医科大学附属医院科研处，贵州省贵阳市 550004
  • 作者简介:周曼，女，1997年生，贵州医科大学在读硕士，主要从事小儿血液疾病及肿瘤基础与临床研究。
  • 基金资助:
    贵州省科学技术厅项目(黔科合基础-2k[2023]-一般366)，项目负责人：杨小燕；贵州省留学人才创新创业择优资助项目[(2022)10号]，项目负责人：杨小燕；贵州医科大学校级重点实验室建设任务[校重点实验室[2024]004号]，项目参与人：杨小燕；贵阳市科技计划项目(筑科合同[2021]43-26号)，项目负责人：吴西军；2024年度贵州省卫生健康委科学技术基金项目(gzwkj2024-131)，项目负责人：吴西军

Bioinformatics screening and experimental verification of core genes in chronic myeloid leukemia and imatinib resistance

Zhou Man1, 2, Long Meiting1, 2, Xin Guoyan1, Huang Mengjun1, 2, Yao Zhenglian1, 2, Zhao Huajuan1, 2, Shen Linqiang1, 2, Wu Xijun3, Yang Xiaoyan1, 2   

  1. 1Guizhou Medical University, Guiyang 550004, Guizhou Province, China; 2Department of Pediatric Hematology, 3Department of Scientific Research, Affiliated Hospital of Guizhou Medical University, Guiyang 550004, Guizhou Province, China
  • Accepted:2025-08-30 Online:2026-05-08 Published:2025-12-25
  • Contact: ang Xiaoyan, MD, Chief physician, Guizhou Medical University, Guiyang 550004, Guizhou Province, China; Department of Pediatric Hematology, Affiliated Hospital of Guizhou Medical University, Guiyang 550004, Guizhou Province, China; Co-corresponding author: Wu Xijun, MD, Associate chief technician, Department of Scientific Research, Affiliated Hospital of Guizhou Medical University, Guiyang 550004, Guizhou Province, China
  • About author:Zhou Man, Master candidate, Guizhou Medical University, Guiyang 550004, Guizhou Province, China; Department of Pediatric Hematology, Affiliated Hospital of Guizhou Medical University, Guiyang 550004, Guizhou Province, China
  • Supported by:
    Science and Technology Department of Guizhou Province, No. 2k[2023]366 (to YXY); Guizhou Province Overseas Talents Innovation and Entrepreneurship Project, No. (2022)10 (to YXY); Key Laboratory Construction Task of Guizhou Medical University, No. [2024]004 (to YXY); Guiyang Municipal Science and Technology Plan Project, No. [2021]43-26 (to WXJ); Guizhou Provincial Health Commission Science and Technology Fund Project for 2024, No. gzwkj2024-131 (to WXJ) 

PDF

11

摘要：

文题释义：

慢性粒细胞白血病：是一种起源于造血干细胞的骨髓增生性疾病，主要涉及粒细胞系列的晚幼粒细胞，表现为持续性、进行性外周血白细胞总数增高，常伴有脾脏的肿大。慢性粒细胞白血病的发病机制主要在于BCR-ABL融合基因的形成，其编码的蛋白展现出异常的酪氨酸激酶活性，导致细胞增殖失控、凋亡抑制以及造血功能紊乱。
生物信息学：是一门交叉学科，汇集了生物学、计算机科学和数学等学科的研究方法，致力于应用计算技术和数据分析工具来捕获、管理、分析和解释生物数据。在药物开发、疾病诊断和精准医疗等领域，生物信息学显示出巨大的应用潜力和重要性。

摘要
背景：慢性粒细胞白血病起源于克隆性造血干细胞，以骨髓细胞异常增殖为特征，大多由BCR-ABL1融合基因引起。尽管伊马替尼显著提升了慢性粒细胞白血病患者的生存率，但其耐药性仍是治疗的主要障碍。
目的：利用生物信息学分析手段，针对基因表达综合数据库内的基因表达资料进行研究，目的在于筛选出慢性粒细胞白血病对伊马替尼耐药的相关基因，并探索耐药机制。
方法：使用由美国国家生物技术信息中心创建和维护的基因表达综合数据库，从该数据库下载GSE267522和GSE174800两个数据集，分别包含3个伊马替尼耐药样本和3个伊马替尼敏感样本。首先基于GEO2R工具筛选出两个数据集中共同的差异基因，借助DAVID平台对相关基因实施京都基因与基因组百科全书通路富集及基因本体功能注释，利用STRING数据库搭建蛋白相互作用网络框架，再通过Cytoscape软件从网络中筛选出连接度值排名靠前的10个枢纽基因。同时运用加权基因共表达网络分析算法获得关键模块特征基因，将这些基因与前述10个枢纽基因进行维恩分析取交集基因作为核心基因。最后，构建K562伊马替尼耐药模型，采用实时荧光定量PCR及蛋白质免疫印迹进行验证性分析。

结果与结论：①两数据集中共筛选出273个差异基因，其中81个基因下调，192个基因上调。②基因本体富集分析揭示差异基因参与免疫反应和T细胞受体信号传导等生物过程；聚焦于细胞组分层面，质膜外侧、质膜及细胞外泌体等区域呈现出显著富集；分子功能分析表明，差异基因涉及跨膜受体蛋白和肌动蛋白的相互作用。③京都基因与基因组百科全书富集分析表明，差异基因显著富集于造血细胞谱系、磷脂酰肌醇3激酶/蛋白激酶B信号通路、癌症通路等。④Cytoscape软件筛选出连接度值排名前10的差异表达基因与加权基因共表达网络分析算法获得关键模块特征基因取交集，获得的交集基因包括IRS1、CD52、CD53、CORO1A、KIT、LAPTM5、PECAM1。⑤成功构建K562伊马替尼耐药株，实时荧光定量PCR结果显示，与K562组相比，K562伊马替尼耐药组CD52、CD53、CORO1A、PECAM1的mRNA表达显著增加(P < 0.05)，IRS1的mRNA表达显著降低(P < 0.05)。此外，蛋白质免疫印迹结果显示，K562伊马替尼耐药株中CD52、CD53、CORO1A、PECAM1蛋白表达增加(P < 0.05)，IRS1蛋白表达下降(P < 0.05)，与实时荧光定量PCR结果一致。⑥K562伊马替尼耐药核心基因表达的差异可能为日后了解慢性粒细胞白血病对伊马替尼耐药的机制提供新见解。

https://orcid.org/0009-0004-1356-3731 (周曼) 

关键词: 慢性粒细胞白血病, 酪氨酸激酶抑制剂, 伊马替尼, 耐药基因, 基因表达, 生物信息学, 加权基因共表达网络分析, 蛋白互作网络

Abstract: BACKGROUND: The origin of chronic myeloid leukemia lies in clonal hematopoietic stem cells, and it is marked by uncontrollable growth of myeloid cells, mostly caused by the BCR-ABL1 fusion gene. Although imatinib has significantly improved patient survival, drug resistance remains a major obstacle to treatment.
OBJECTIVE: To identify genes associated with imatinib resistance in chronic myeloid leukemia using bioinformatics analysis within the Gene Expression Omnibus database and to explore the mechanisms of resistance.
METHODS: The present study utilized the Gene Expression Omnibus database, which was created and is currently maintained by the National Center for Biotechnology Information. Two datasets, GSE267522 and GSE174800, were retrieved from the Gene Expression Omnibus database, and each of the two datasets comprised three imatinib-resistant samples and three imatinib-sensitive samples. First, the GEO2R tool was used to screen the differential genes, and the DAVID tool was used to analyze them for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment. A protein interaction network framework was constructed using the STRING database, and the top 10 hub genes with the highest connectivity values were identified from the network using Cytoscape software. Furthermore, a weighted gene co-expression network analysis algorithm was used to identify key module feature genes. Venn diagram analysis was performed with these genes and the aforementioned 10 hub genes, and the intersection genes were selected as core genes. Finally, a K562 imatinib resistance model was constructed and validated using real-time fluorescence quantitative PCR and western blotting.
RESULTS AND CONCLUSION: (1) A total of 273 differential genes were screened between the two datasets, of which 81 were downregulated and 192 were upregulated. (2) Gene ontology enrichment analysis revealed that the differential genes were involved in immune response and T-cell receptor signaling. Focusing on the cellular component level, cell components like the outer and inner plasma membranes and cellular exosome showed significant enrichment. Molecular function analysis indicated that the differential genes were involved in interactions between transmembrane receptor proteins and actin. (3) Kyoto Encyclopedia of Genes and Genomes enrichment analysis showed that the differential genes were enriched in the hematopoietic cell lineage, phosphatidylinositol 3-kinase/protein kinase B signaling pathway and cancer pathway. (4) Cytoscape software screened out the top 10 differentially expressed genes by connectivity and intersected them with key module signature genes obtained using a weighted gene co-expression network analysis algorithm. The resulting intersection genes obtained included IRS1, CD52, CD53, CORO1A, KIT, LAPTM5, and PECAM1. (5) The K562 Imatinib resistance beads were successfully constructed. The real-time fluorescence quantitative PCR results showed that compared with the K562 group, the mRNA expression of CD52, CD53, CORO1A, and PECAM1 was significantly increased in the imatinib-resistant K562 group (P < 0.05), while the mRNA expression of IRS1 was significantly decreased (P < 0.05). Furthermore, western blotting revealed increased expression of CD52, CD53, CORO1A, and PECAM1 proteins (P < 0.05), and decreased expression of IRS1 protein (P < 0.05) in the imatinib-resistant K562 strain (consistent with the real-time fluorescence quantitative PCR results). (6) Differences in the expression of K562 imatinib resistance core genes may provide new insights into the mechanisms of imatinib resistance in chronic myeloid leukemia in the future.

Key words: chronic myeloid leukemia, tyrosine kinase inhibitor, imatinib, drug resistance gene, gene expression, bioinformatics, weighted gene co-expression network analysis, protein interaction network

中图分类号: