医学分子生物学杂志 ›› 2026, Vol. 23 ›› Issue (5): 563-570.doi: 10.3870/j.issn.1672-8009.2026.05.011

• 论著 • 上一篇    下一篇

基于生物信息学筛选动静脉移植物内瘘中内膜增生相关细胞外基质蛋白及其分子机制

蒋利华1, 金爱莲2, 龙艳红1, 别子瑞3   

  1. 1天门市第一人民医院血液净化中心 湖北省天门市, 431700;
    2仙桃市第一人民医院血液净化中心 湖北省仙桃市, 433000;
    3武汉科技大学临床医学院 武汉市, 430080
  • 收稿日期:2025-12-02 出版日期:2026-09-30 发布日期:2026-09-30
  • 通讯作者: 金爱莲(Email:lq123_123@163.com)

Identification of Neointimal Hyperplasia-Associated Extracellular Matrix Proteins in Arteriovenous Graft Fistula Using Bioinformatics and Analysis of Molecular Mechanisms

JIANG Lihua1, JIN Ailian2, LONG Yanhong1, BIE Zirui3   

  1. 1Department of Blood Purification Center, First People's Hospital of Tianmen, Tianmen, Hubei, 431700, China;
    2Department of Blood Purification Center, First People's Hospital of Xiantao, Xiantao, Hubei, 433000, China;
    3Clinical Medicine, Wuhan University of Science and Technology, Wuhan, 430080, China
  • Received:2025-12-02 Online:2026-09-30 Published:2026-09-30
  • Contact: JIN Ailian (Email:lq123_123@163.com)

摘要: 目的 通过生物信息学方法筛选动静脉移植物内瘘中内膜增生(neointimal hyperplasia, NH)相关的细胞外基质(extracellular matrix, ECM)蛋白,探讨其分子机制,为临床治疗提供新靶点与药物候选。方法 从GEO数据库获取数据集GSE97377,以AVG建模后第14天转录组数据为训练集、第5天数据为验证集。应用limma包分析得到差异表达基因(differentially expressed genes, DEGs)。通过Uniprot和HPA数据库提取ECM蛋白,并与DEGs取交集得到ECM蛋白差异表达基因(ECM protein- DEGs,EP-DEGs)。对EP-DEGs进行GO和KEGG通路分析;构建蛋白-蛋白互作网络筛选枢纽基因。应用GeneMANIA数据库对EP-DEGs进行功能分析,通过DSigDB和CB-DOCK2数据库分别进行药物预测和分子对接。结果 共鉴定出79个EP-DEGs与AVG内瘘后NH病理发展有关,其中IL1A、CXCL12、CD163和SPP1为关键枢纽基因。GO和KEGG分析显示这些基因主要参与炎症反应和ECM重塑;发现在构建AVG后5 d及14 d静脉血管中,CXCL12、CD163及SPP1表达水平一致。GeneMANIA分析提示CXCL12是调控炎性因子的关键因子。靶蛋白药物预测分析发现辛伐他汀、非洛地平、山柰酚和伏立诺他等可能通过作用于枢纽基因发挥治疗作用。分子对接分析说明靶蛋白与候选药物存在较强的相互作用。结论 通过系统鉴定与AVG内瘘后NH相关的ECM蛋白,预测了多个潜在分子靶点和候选药物,为理解NH分子机制及开发新治疗策略提供了理论依据。

关键词: 维持性血液透析, 动静脉移植物, 内膜增生, 细胞外基质蛋白, 分子对接

Abstract: Objective To screen extracellular matrix (ECM) proteins associated with neointimal hyperplasia (NH) in arteriovenous graft (AVG) fistula using bioinformatics methods, explore the underlying molecular mechanisms, and provide novel molecular targets and drug candidates for clinical treatment. Methods The dataset GSE97377 was obtained from the GEO database. Transcriptomic data on day 14 after AVG modeling served as the training set, and data on day 5 served as the validation set. Differentially expressed genes (DEGs) were identified using the limma package. ECM proteins were extracted from the Uniprot and HPA databases, and intersection with DEGs was performed to obtain ECM protein-related DEGs (EP-DEGs). GO and KEGG pathway analyses were conducted on EP-DEGs. A protein-protein interaction network was constructed to screen for hub genes. Functional analysis of EP-DEGs was performed using the GeneMANIA database. Drug prediction and molecular docking were carried out using the DSigDB and CB-DOCK2 databases, respectively. Results A total of 79 EP-DEGs associated with NH development after AVG fistula were identified, among which IL1A, CXCL12, CD163, and SPP1 were identified as key hub genes. GO and KEGG analyses indicated that these genes were primarily involved in inflammatory response and ECM remodeling. The expression levels of CXCL12, CD163, and SPP1 were consistent in the venous vasculature on both day 5 and day 14 after AVG modeling. GeneMANIA analysis suggested that CXCL12 is a key regulator of inflammatory factors. Drug prediction analysis revealed that simvastatin, felodipine, kaempferol, and vorinostat may exert therapeutic effects by targeting the hub genes. Molecular docking analysis demonstrated strong interactions between the target proteins and candidate drugs. Conclusion This study systematically identified ECM proteins associated with NH after AVG fistula and predicted multiple potential molecular targets and candidate drugs, providing a theoretical basis for understanding the molecular mechanisms of NH and developing novel therapeutic strategies.

Key words: maintenance hemodialysis, arteriovenous hemodialysis graft, neointimal hyperplasia, extracellular matrix proteins, molecular docking

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