中国组织工程研究 ›› 2025, Vol. 29 ›› Issue (18): 3747-3757.doi: 10.12307/2025.704

• 骨组织构建 bone tissue construction • 上一篇    下一篇

骨关节炎中枢纽基因及在免疫浸润中作用的生物信息学分析与鉴定

蔡  溦1,朱御坤2,许建中1   

  1. 1郑州大学第一附属医院骨科,河南省郑州市  450052;2四川大学华西医院重症医学科,四川省成都市  610041
  • 收稿日期:2024-07-24 接受日期:2024-08-29 出版日期:2025-06-28 发布日期:2024-11-27
  • 通讯作者: 许建中,博士,主任医师,郑州大学第一附属医院骨科,河南省郑州市 450052
  • 作者简介:蔡溦,男,2000年生,河南省开封市人,汉族,郑州大学第一附属医院在读硕士,主要从事骨与关节疾病的基础和临床研究。

Bioinformatics analysis and identification of hub genes and their role in immune infiltration in osteoarthritis 

Cai Wei1, Zhu Yukun2, Xu Jianzhong1   

  1. 1Department of Orthopedics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China; 2Department of Critical Care Medicine, West China Hospital of Sichuan University, Chengdu 610041, Sichuan Province, China
  • Received:2024-07-24 Accepted:2024-08-29 Online:2025-06-28 Published:2024-11-27
  • Contact: Xu Jianzhong, MD, Chief physician, Department of Orthopedics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
  • About author:Cai Wei, Master candidate, Department of Orthopedics, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China

摘要:


文题释义:
生物信息学:主要通过综合运用数学和信息科学等多领域的方法对生物信息进行分析和解释,是阐明疾病发病机制的一种重要方法。
枢纽基因:在基因调控和生物过程中起关键作用的基因,常作为药物研究和疾病治疗的靶点。

背景:低度慢性炎症被认为在骨关节炎的发病机制中起着核心作用,但是具体的分子机制目前仍不清楚。
目的:筛选和探讨骨关节炎中的潜在枢纽基因及免疫细胞的浸润情况。 
方法:将来自GPL570平台的GSE206848数据集与来自GPL96平台的GSE55235和GSE55457数据集合形成原始数据集。使用加权基因共表达网络分析去除离群样本,随后鉴定差异表达基因,并对差异表达基因进行功能富集分析。此外,构建差异表达基因的蛋白质-蛋白质相互作用网络,并使用Cytoscape软件中的两种不同算法筛选枢纽基因。利用CIBERSORT算法评估骨关节炎样本与正常对照之间免疫细胞浸润比例的差异。通过对收集到的骨关节炎患者滑膜组织样本进行定量反转录聚合酶链反应(RT-qPCR)实验,并结合来自GPL96测序平台的GSE12021数据集作为独立数据集,验证枢纽基因对骨关节炎的诊断能效。 
结果与结论:①在去除5个离群样本后,共鉴定出340个差异表达基因,其中包括159个上调基因和181个下调基因。通过加权基因共表达网络分析和Cytoscape共获得了6个枢纽基因。②CIBERSORT分析显示,骨关节炎组织中多种类型免疫细胞浸润比例与正常组织相比存在差异,6个枢纽基因的表达水平与骨关节炎中多种免疫细胞的相对比例密切相关。③RT-qPCR结果表明,6个基因的相对表达水平相对于正常组织呈下调趋势,但是NFKBIA和PTGS2的表达差异不显著(P > 0.05);原始和外部数据集中的ROC曲线表明,6个枢纽基因对骨关节炎具有较强的诊断能力(AUC > 0.8)。④结论:最终确定了4个枢纽基因,分别为CDKN1A、MYC、CXC趋化因子配体2和血管内皮生长因子A,可能通过介导免疫应答和炎症反应成为未来治疗骨关节炎的分子靶点。

http://orcid.org/0009-0009-8368-7146(蔡溦)

中国组织工程研究杂志出版内容重点:组织构建;骨细胞;软骨细胞;细胞培养;成纤维细胞;血管内皮细胞;骨质疏松;组织工程

关键词: 骨关节炎, 滑膜组织, 枢纽基因, 免疫浸润, 生物信息学, 加权基因共表达网络分析

Abstract:

BACKGROUND: Low-grade, chronic inflammation is thought to play a central role in the pathogenesis of osteoarthritis. However, the specific molecular mechanisms are still unclear. 
OBJECTIVE: To screen and explore the potential hub genes and immune cell infiltration in osteoarthritis.
METHODS: We merged data from the GSE206848 on the GPL570 and the GSE55235 and GSE55457 on the GPL96 to form the row dataset. Outlier samples were removed using weighted gene co-expression network analysis, followed by the identification of differentially expressed genes, and subsequent functional enrichment analysis of differentially expressed genes. Further, a protein-protein interaction network of differentially expressed genes was constructed, and hub genes were identified using two different algorithms in Cytoscape. Additionally, the CIBERSORT algorithm was employed to assess differences in immune cell infiltration proportions between osteoarthritis samples and normal controls. Finally, the diagnostic efficacy of hub genes for osteoarthritis was validated using quantitative reverse transcription polymerase chain reaction experiments conducted on synovial tissue samples collected from patients with osteoarthritis, in conjunction with the GSE12021 dataset from the GPL96 sequencing platform as an independent dataset. 
RESULTS AND CONCLUSION: After eliminating 5 outlier samples, we identified a total of 340 differentially expressed genes, comprising 159 up-regulated genes and 181 down-regulated genes. Six hub genes were obtained by weighted gene co-expression network analysis and Cytoscape. CIBERSORT analysis revealed a difference in the proportion of multiple types of immune cell infiltration in osteoarthritic tissues compared with normal tissues. Moreover, the expression levels of the six hub genes exhibited strong correlation with the relative proportion of multiple immune cells in osteoarthritis. The results of RT-qPCR indicated that the relative expression levels of the six genes were down-regulated relative to normal tissues. However, there was no significant difference in the expression of NFKBIA and PTGS2 (P > 0.05). Simultaneously, receiver operator characteristic curves in both the original and external datasets demonstrated that the six hub genes exhibited strong diagnostic capabilities for osteoarthritis (area under the curve > 0.8). To conclude, four hub genes, CDKN1A, MYC, C-X-C motif chemokine ligand 2, and vascular endothelial growth factor A, are finally identified and may serve as molecular targets for future treatment by mediating immune response and inflammatory processes.

中国组织工程研究杂志出版内容重点:组织构建;骨细胞;软骨细胞;细胞培养;成纤维细胞;血管内皮细胞;骨质疏松;组织工程

Key words: osteoarthritis, synovial tissue, hub gene, immune infiltration, bioinformatics, weighted gene co-expression network analysis

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