[1]饶亚妮,黄新周,卫永鲲.基于网络毒理学和生物信息学探索双酚A对骨性关节炎发生的潜在影响及关键基因筛选的分析研究[J].现代检验医学杂志,2026,41(02):160-166.[doi:10.3969/j.issn.1671-7414.2026.02.027]
　RAO Yani,HUANG Xinzhou,WEI Yongkun.Analytical Study of Bisphenol A Potential Impact on Osteoarthritis Development and Key Gene Screening Based on Network Toxicology and Bioinformatics[J].Journal of Modern Laboratory Medicine,2026,41(02):160-166.[doi:10.3969/j.issn.1671-7414.2026.02.027]
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基于网络毒理学和生物信息学探索双酚A对骨性关节炎发生的潜在影响及关键基因筛选的分析研究()

《现代检验医学杂志》[ISSN:/CN:]

卷:
第41卷
期数:
2026年02期
页码:
160-166
栏目:
论著
出版日期:
2026-03-15

文章信息/Info

Title:
Analytical Study of Bisphenol A Potential Impact on Osteoarthritis Development and Key Gene Screening Based on Network Toxicology and Bioinformatics
文章编号:
1671-7414（2026）02-160-07
作者:
饶亚妮，黄新周，卫永鲲
西安交通大学附属汉中三二〇一医院骨科，陕西汉中 723000
Author(s):
RAO Yani，HUANG Xinzhou，WEI Yongkun
Department of Orthopaedics, Hanzhong 3201 Hospital, Xi’an Jiaotong University, Shaanxi Hanzhong 723000, China
关键词:
网络毒理学; 生物信息学; 双酚A; 骨性关节炎; 机器学习
分类号:
R684.3；R446
DOI:
10.3969/j.issn.1671-7414.2026.02.027
文献标志码:
A
摘要:
目的运用网络毒理学和生物信息学方法,深入探究双酚A(BPA)对骨性关节炎(OA)发生发展的潜在影响,并从中筛选出关键基因。方法从美国国家生物技术信息中心(NCBI)基因表达综合(GEO)数据库中下载3个OA数据集,采用R语言鉴定出差异表达基因(DEGs)。从比较毒理基因组学(CTD)数据库获取BPA诱导OA的靶基因。借助韦恩图对两数据库的相关基因取交集,随后进行基因本体(GO)分析和京都基因与基因组百科全书(KEGG)富集分析。采用6种机器学习模型[LASSO回归、支持向量机(SVM)、Boruta算法、极端梯度提升(XGBoost)、轻量级梯度提升(LightGBM)和自适应提升(AdaBoost)方法]确定OA的生物标志物。进一步构建疾病风险列线图,并通过校准曲线、临床影响曲线和受试者操作特征(ROC)曲线评估有效性,同时验证靶点的表达量。此外,通过分子对接评估BPA与靶点的结合潜力。在DGIdb数据库中获得潜在的靶标药物。通过外部数据集验证OA和正常组中枢纽基因的表达。结果整合数据集后,共获得15个交集基因。富集分析显示,这些基因与有丝分裂核分裂、受体结合调控、p53信号轴、白细胞介素(IL)-17介导的激活通路及肿瘤坏死因子(TNF)传导机制等功能及信号通路密切相关。机器学习算法进一步确定胰岛素样生长因子结合蛋白1(IGFBP1)、钙黏蛋白2(CDH2)和MKI67是关键基因。ROC曲线表明这些基因具有很高的诊断效能(AUC=0.926),且在OA组中显著高表达。这3个基因的表达水平在GSE51588数据集中进行了外部验证。分子对接证明BPA与靶点稳定而牢固地结合。最后,提取13种靶向IGFBP1的药物,美沙酮盐酸盐和盐酸美沙酮靶向CDH2,阿贝西利和舒尼替尼靶向MKI67。结论该研究成功揭示了BPA对OA发生的潜在影响机制,并确定了IGFBP1、CDH2与MKI67三大生物标志,这些标志可能成为OA潜在的生物标志物和治疗靶点。
Abstract:
Objective Network toxicology and bioinformatics methods were applied to investigate in-depth the potential ef-fects of bisphenol A (BPA) on the development of osteoarthritis (OA) and identify key genes in the process. Methods Three OA datasets were downloaded from the Gene Expression Omnibus (GEO) database at the National Center for Biotechnol-ogy Information (NCBI), and differentially expressed genes (DEGs) were identified using R software. The target genes for BPA-induced OA were obtained from the Comparative Toxicogenomics Database (CTD). The intersection of relevant genes from the two databases was identified using Venn diagrams, followed by Gene Ontology (GO) analysis, and Kyoto Encyclo-pedia of Genes and Genomes (KEGG) enrichment analysis. Six machine learning models (Lasso regression, support vector machine, Boruta algorithm, XGBoost, LightGBM, and AdaBoost methods) were employed to identify biomarkers for OA. Disease risk nomogram was further constructed, and its validity was assessed by calibration curves, clinical impact curves and ROC curves. The expression levels of the targets were validated. In addition, the binding potential between BPA and targets was assessed by molecular docking. Potential target drugs were identified from the DGIdb database. Expression of hub genes in OA and normal groups was verified by external datasets. Results After integrating the dataset, a total of 15 intersecting genes were identified. Enrichment analysis showed that these genes were associated with functional pathways such as mitotic nuclear division, receptor binding regulation, p53 signaling axis, IL-17 mediated activation pathways and TNF transduction mechanisms. Machine learning algorithms further identified IGFBP1, CDH2 and MKI67 as key genes. Receiver operating characteristic (ROC) curves demonstrated that these genes had high diagnostic efficacy (AUC=0.926) and were significantly overexpressed in the OA group. Expression levels of these three genes were externally validated in the GSE51588 dataset. Mo-lecular docking confirmed stable and robust binding between BPA and its targets. Finally, 13 drugs targeting IGFBP1 were identi-fied along with methadone hydrochloride and methadone hydrochloride targeting CDH2, and identified abemaciclib and sunitinib targeting MKI67. These compounds represent potential therapeutic agents for OA treatment. Conclusions This study successful-ly elucidates the potential mechanism by which BPA influences OA development and identifies IGFBP1, CDH2 and MKI67 as three major biomarkers. These markers may serve as potential biomarkers and therapeutic targets for OA.

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备注/Memo

备注/Memo:
基金项目：国家临床重点专科建设项目［陕卫医函（2023）325 号］。
作者简介：饶亚妮（1986-），女，本科，主管护师，研究方向：运动医学与骨关节相关研究，E-mail: raoyani00@163.com。
通讯作者：卫永鲲（1972-），男，硕士，主任医师，研究方向：运动医学与骨关节相关研究，E-mail: 309958564@qq.com。
更新日期/Last Update: 2026-03-15