中国组织工程研究

中国组织工程研究 ›› 2022, Vol. 26 ›› Issue (33): 5342-5349.doi: 10.12307/2022.952

• 骨科植入物相关基础实验 Basic experiments of orthopedic implant • 上一篇    下一篇

差异表达基因联合加权共表达网络分析筛选骨关节炎滑膜中的关键基因

钱晓芬1，曾  平2，刘金富1，王  豪1，周树龙1，潘海达1   

  1. 1广西中医药大学研究生院，广西壮族自治区南宁市   530299；2广西中医药大学第一附属医院仙葫院区骨二科，广西壮族自治区南宁市   530023
  • 收稿日期:2021-03-23 接受日期:2021-04-28 出版日期:2022-11-28 发布日期:2022-03-31
  • 通讯作者: 曾平，博士，科主任，主任医师，广西中医药大学第一附属医院仙葫院区骨二科，广西壮族自治区南宁市 530023
  • 作者简介:钱晓芬，女，1996年生，汉族，湖北省人，广西中医药大学在读硕士，主要从事四肢骨病与创伤的防治研究。
  • 基金资助:
    广西壮族自治区中医药局中医药适宜技术开发与推广项目(GZSY21-14)，项目负责人：曾平

Screening key genes in synovium of osteoarthritis by a combination of differentially expressed genes and weighted co-expression network analysis

Qian Xiaofen1, Zeng Ping2, Liu Jinfu1, Wang Hao1, Zhou Shulong1, Pan Haida1   

  1. 1School of Graduate, Guangxi University of Chinese Medicine, Nanning 530299, Guangxi Zhuang Autonomous Region, China; 2Second Department of Orthopedics, Xianhu Branch, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning 530023, Guangxi Zhuang Autonomous Region, China 
  • Received:2021-03-23 Accepted:2021-04-28 Online:2022-11-28 Published:2022-03-31
  • Contact: Zeng Ping, MD, Chief physician, Second Department of Orthopedics, Xianhu Branch, First Affiliated Hospital, Guangxi University of Chinese Medicine, Nanning 530023, Guangxi Zhuang Autonomous Region, China
  • About author:Qian Xiaofen, Master candidate, School of Graduate, Guangxi University of Chinese Medicine, Nanning 530299, Guangxi Zhuang Autonomous Region, China
  • Supported by:
    Chinese Medicine Appropriate Technology Development and Promotion Project of Guangxi Zhuang Autonomous Region Traditional Chinese Medicine Bureau, No. GZSY21-14 (to ZP)

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摘要：

文题释义：
差异表达基因：是指一个基因在RNA水平处在不同环境压力、时间、空间等方面下，表达有显著性差异的基因。
加权共表达网络分析(Weighted Co-expression Network Construction Analysis，WGCNA)：用以描述不同样品之间基因关联模式的系统生物学方法，可用来鉴定高度协同变化的基因集。

背景：骨关节炎是一种常见的慢性退行性疾病，与年龄具有高度相关性，然而其具体的发病机制尚不明确，在早期诊断和治疗方面存在许多不足。
目的：通过生物信息学方法筛选骨关节炎滑膜中的关键表达基因，为寻找诊断骨关节炎的生物标志物及阐明其潜在的发病机制奠定基础。
方法：从GEO数据库下载4个骨关节炎滑膜数据集(GSE32317、GSE55235、GSE55457、GSE82107)，共纳入27个正常滑膜组织样本和49个骨关节炎滑膜组织样本，运用R语言进行差异表达基因和加权基因共表达网络分析(Weighted Co-expression Network Construction Analysis，WGCNA)交集基因的筛选，对其进行功能分析、蛋白质相互作用网络分析(PPI)以及免疫浸润分析，并使用cytoscape进行网络可视化，用prism对degree排名前10个基因绘制ROC曲线并筛选出关键基因，并用另一组滑膜数据集GSE12021进行验证，最后采用PCR对广西中医药大学第一附属医院仙葫院区骨二科骨关节炎和非骨关节炎(关节创伤)患者各4例滑膜中的关键基因进行检测。
结果与结论：①共筛选出差异表达基因263个，WGCNA关键模块基因1 237个，两者共有98个交集基因；②PPI degree排名前10的基因分别为白细胞介素6、JUN、ATF3、MYC、DUSP1、VEGFA、FOSB、CXCL8、PTGS2、NR4A1；③根据ROC曲线显示ATF3和DUSP1诊断骨关节炎的准确度较高(AUC值>0.8)；④用数据集GSE12021和PCR检测进行验证发现，ATF3和DUSP1在骨关节炎组和对照组中基因表达差异有显著性意义(P < 0.01)，且与网络分析结果一致；⑤上述结果证实，利用生物信息学方法筛选骨关节炎滑膜中的关键表达基因，基于现有的临床样本的验证结果推论，ATF3和DUSP1有可能成为骨关节炎诊断和治疗的潜在生物标志物和治疗靶点。

https://orcid.org/0000-0001-5801-8391 (曾平) 

中国组织工程研究杂志出版内容重点：人工关节；骨植入物；脊柱；骨折；内固定；数字化骨科；组织工程

关键词: 骨关节炎, 滑膜组织, 差异表达基因, 加权共表达网络分析, 细胞应激反应, 信号通路, 激活转录因子3, 双特异性磷酸酶1

Abstract: BACKGROUND: Osteoarthritis is a common chronic degenerative disease with a high correlation with age. However, its specific pathogenesis is still unclear, and there are many deficiencies in early diagnosis and treatment.
OBJECTIVE: To screen the key expressed genes in the synovium of osteoarthritis by bioinformatics method, so as to lay a foundation for finding biomarkers for the diagnosis of osteoarthritis and elucidating its underlying pathogenesis.
METHODS: Four osteoarthritic synovial tissue data sets were downloaded from the Gene Expression Omnibus database (GSE32317, GSE55235, GSE55457, GSE82107), including 27 normal synovial tissue samples and 49 osteoarthritic synovial tissue samples. The intersection genes of differentially expressed genes and Weighted Co-expression Network Construction Analysis results were screened using R language. Functional annotation, protein-protein interaction network, and immune infiltration analyses of the intersection genes were performed, and cytoscape was used for network visualization. Prism was used to draw receiver operating characteristic curve for the top 10 genes for degree ranking and screen out key genes, and then another synovial data set, GSE12021, was used for verification. Finally, the key genes in the synovial samples from four osteoarthritis patients and four non-osteoarthritis patients (joint injury) were further detected by PCR.
RESULTS AND CONCLUSION: (1) In this study, 263 differentially expressed genes and 1 237 key module genes of Weighted Co-expression Network Construction Analysis were identified, with a total of 98 intersection genes. (2) Protein-protein interaction node number of the top 10 most genes included interleukin 6, JUN, ATF3, MYC, DUSP1, VEGFA, FOSB, CXCL8, PTGS2, and NR4A1. (3) According to the receiver operating characteristic curve, ATF3 and DUSP1 had a higher diagnostic accuracy (area under the curve > 0.8). (4) Verification with the data set GSE12021 and PCR detection indicated that the gene expression difference of ATF3 and DUSP1 between the osteoarthritis group and the control group was statistically significant (P < 0.01), which was consistent with the results of network analysis. (5) Through bioinformatics analysis, ATF3 and DUSP1 are considered to have high diagnostic value for osteoarthritis and may be potential biomarkers and therapeutic targets for osteoarthritis diagnosis and treatment.

Key words: osteoarthritis, synovial tissue, differentially expressed genes, weighted co-expression network analysis, cellular stress response, signaling pathway, activating transcription factor 3, dual-specificity phosphatase 1

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