Identification and Validation of Fibroblast-Associated Genes in Osteoarthritis Based on High-Dimensional Weighted Gene Coexpression Network Analysis.

IF 3.6 3区 医学 Q2 IMMUNOLOGY
Journal of Immunology Research Pub Date : 2025-09-28 eCollection Date: 2025-01-01 DOI:10.1155/jimr/5547701
Juan Xiao, Wei Lei, Hao Zhang, Feng Niu, Qunhai Wu, Honglin Pi, Poorani Gurumallesh
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引用次数: 0

Abstract

Background: Osteoarthritis (OA) is a degenerative joint disease with articular cartilage destruction, triggering a pro-inflammatory response. The aim of this study was to screen key genes associated with fibroblasts based on single-cell transcriptomic data and explore their potential value in OA diagnosis. Methods: We obtained RNA sequencing (RNA-seq) and single-cell RNA-seq (scRNA-seq) data of OA from the Gene Expression Omnibus (GEO) database. The CellChat package for cell-to-cell communication analysis and identification of possible ligand-receptor pairs. High-dimensional weighted gene coexpression network analysis (hdWGCNA) was applied to identify the gene modules, and the key genes in the modules were identified and subjected to functional enrichment analysis. Subsequently, limma packages were used to screen for differentially expressed genes (DEGs) between OA and its control samples. Finally, the R package multipleROC was used to test the diagnostic potential of the screened key genes and to construct an OA diagnostic model using the rms package. Result: Eight cell populations were identified and annotated based on scRNA-seq and the percentage of fibroblasts was the highest. The cell-cell communication analysis has suggested that the highest communication probability was seen between mesenchymal cells/T cells and fibroblasts through the pairs of CD99-CD99. The hdWGCNA analysis suggested that genes of modules M3, M4, M5, M6, and M8 (50 genes in total) were highly expressed in fibroblasts. Thereafter, we obtained 394 DEGs in OA and its control samples and took intersections with 50 modular genes and identified seven central genes (including apolipoprotein D [APOD], biglycan [BGN], MXRA5, THY1, C1QTNF3, dermatopontin [DPT], and osteoglycin [OGN]). The constructed diagnostic models showed good predictive performance with all area under the curve (AUC) values >0.8. Finally, a satisfactory diagnostic model was established using these seven genes, and the differences in mRNA expression levels of these genes in OA and normal tissues were verified. Conclusion: For the first time, our study systematically screened and validated key genes with diagnostic potential based on fibroblast-specific single-cell data in combination with hdWGCNA, providing a new theoretical basis and research direction for molecular typing and diagnosis of OA.

基于高维加权基因共表达网络分析的骨关节炎成纤维细胞相关基因鉴定与验证。
背景:骨关节炎(OA)是一种退行性关节疾病,伴有关节软骨破坏,引发促炎反应。本研究的目的是基于单细胞转录组学数据筛选与成纤维细胞相关的关键基因,并探讨其在OA诊断中的潜在价值。方法:从Gene Expression Omnibus (GEO)数据库中获取OA的RNA测序(RNA-seq)和单细胞RNA测序(scRNA-seq)数据。CellChat软件包用于细胞间通信分析和可能的配体-受体对的鉴定。采用高维加权基因共表达网络分析(High-dimensional weighted gene co - expression network analysis, hdWGCNA)对基因模块进行鉴定,并对模块中的关键基因进行功能富集分析。随后,使用limma包装筛选OA与其对照样品之间的差异表达基因(DEGs)。最后,利用R包multieroc对筛选的关键基因进行诊断潜力测试,并利用rms包构建OA诊断模型。结果:基于scRNA-seq鉴定并注释了8个细胞群,成纤维细胞比例最高。细胞间通讯分析表明,间充质细胞/T细胞和成纤维细胞之间通过CD99-CD99对进行通讯的可能性最高。hdWGCNA分析显示,在成纤维细胞中,M3、M4、M5、M6和M8模块(共50个基因)高表达。随后,我们从OA及其对照样本中获得394个DEGs,并与50个模块基因进行交叉,鉴定出7个中心基因(载脂蛋白D [APOD]、biglycan [BGN]、MXRA5、THY1、C1QTNF3、皮桥蛋白[DPT]、骨胰素[OGN])。所构建的诊断模型预测效果良好,曲线下面积(AUC)均为>0.8。最后,利用这7个基因建立了满意的诊断模型,并验证了这些基因在OA和正常组织中mRNA表达水平的差异。结论:本研究首次基于成纤维细胞特异性单细胞数据结合hdWGCNA系统筛选并验证了具有诊断潜力的关键基因,为OA分子分型与诊断提供了新的理论依据和研究方向。
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来源期刊
CiteScore
6.90
自引率
2.40%
发文量
423
审稿时长
15 weeks
期刊介绍: Journal of Immunology Research is a peer-reviewed, Open Access journal that provides a platform for scientists and clinicians working in different areas of immunology and therapy. The journal publishes research articles, review articles, as well as clinical studies related to classical immunology, molecular immunology, clinical immunology, cancer immunology, transplantation immunology, immune pathology, immunodeficiency, autoimmune diseases, immune disorders, and immunotherapy.
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