[1]孙大系,魏鹏飞.基于内质网应激相关的类风湿性关节炎疾病基因筛选[J].医学信息,2025,38(05):1-7,15.[doi:10.3969/j.issn.1006-1959.2025.05.001]
　SUN Daxi,WEI Pengfei.Screening of Endoplasmic Reticulum Stress-related Genes in Rheumatoid Arthritis[J].Journal of Medical Information,2025,38(05):1-7,15.[doi:10.3969/j.issn.1006-1959.2025.05.001]
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基于内质网应激相关的类风湿性关节炎疾病基因筛选()
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医学信息[ISSN:1006-1959/CN:61-1278/R]

卷:
38卷
期数:
2025年05期
页码:
1-7,15
栏目:
生物信息学
出版日期:
2025-03-01

文章信息/Info

Title:
Screening of Endoplasmic Reticulum Stress-related Genes in Rheumatoid Arthritis
文章编号:
1006-1959（2025）05-0001-08
作者:
孙大系 1; 2; 魏鹏飞 1; 2
1.滨州医学院药学院，山东 烟台 264003；2.山东省分子靶向智能诊疗技术创新中心，山东 烟台 264003
Author(s):
SUN Daxi1; 2;  WEI Pengfei1; 2
1.School of Pharmacy, Binzhou Medical University, Yantai 264003, Shandong, China;2.Shandong Technology Innovation Center of Molecular Targeting and Intelligent Diagnosis and Treatment, Yantai 264003, Shandong, China
关键词:
内质网应激; 机器学习; 类风湿性关节炎; 药物靶点
Keywords:
Endoplasmic reticulum stress;  Machine learning;  Rheumatoid arthritis;  Drug targets
分类号:
R593.22
DOI:
10.3969/j.issn.1006-1959.2025.05.001
文献标志码:
A
摘要:
目的 探索类风湿性关节炎（RA）与内质网应激（ERS）相关的疾病基因特征。方法 从基因表达数据库（GEO）获得RA患者和健康人群的滑膜组织基因表达矩阵进行GSEA分析；运用加权基因共表达网络分析（WGCNA）和显著性表达差异基因（DEGs）分析识别RA与ERS相关的关键模块基因。运用不同机器学习算法，筛选与RA相关的ERS特征基因，并进行相关基因的药物靶点预测。结果 共鉴定出109个DEGs，GSEA富集分析揭示了与RA病理损伤相关的生物学途径。基于WGCNA模块分析以及SVM/LASSO算法进一步筛选得到3个ERS相关的RA核心基因-SPP1、FABP4和ADIPOQ。ROC曲线分析发现3个核心基因均具有较高的诊断价值。基因药物表达网络预测分析表明，多种药物以核心靶基因SPP1、FABP4为有效靶点。结论 内质网应激相关核心基因的筛选为RA的诊断以及相关治疗靶点的开发提供了新的线索。
Abstract:
Objective To explore the disease gene characteristics of rheumatoid arthritis (RA) associated with endoplasmic reticulum stress (ERS). Methods The gene expression matrices of synovial tissues from RA patients and healthy individuals were obtained from the gene expression database (GEO) for GSEA analysis. The weighted gene co-expression network analysis (WGCNA) and significant differentially expressed genes (DEGs) analysis were used to identify key module genes associated with RA disease and ERS. Different machine learning algorithms were used to screen genes related to ERS in rheumatoid arthritis, and gene-related target prediction analysis was performed. Results A total of 109 DEGs were identified in this study. GSEA enrichment analysis revealed biological pathways associated with RA pathology. Based on WGCNA module analysis and further screening using SVM/LASSO algorithms, three ERS-related RA core genes-SPP1, FABP4, and ADIPOQ were identified. ROC curve analysis showed that the three core genes had high diagnostic value. Network pharmacology prediction analysis indicated that multiple drugs targeted the core target genes SPP1 and FABP4. Conclusion The screening of core genes associated with endoplasmic reticulum stress provides new clues for the diagnosis of rheumatoid arthritis and the development of relevant therapeutic targets.

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更新日期/Last Update: 1900-01-01