Qingpeng Sun, Feng Niu, Li Wang, Honglin Pi, Chongtao Han, Jun Gao
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Employing the Monocle2 package, the progression trajectories of OA were analyzed based on the dynamic gene expression changes in the fibroblast subtypes. Finally, single sample GSEA (ssGSEA) and differential expression analysis were combined to screen diagnostic biomarkers for OA, and their diagnostic efficacy was assessed by receiver operating characteristic (ROC) curves and principal component analysis (PCA). <b>Results:</b> Using the scRNA-seq data of OA samples, we identified five different cell types (fibroblasts, endothelial cells [ECs], lymphoid cells, mural cells, and myeloid cells), with fibroblasts accounting for the highest proportion. Then, we found that Fibroblast subtype 3 was notably enriched in fibrosis-related pathways. The pseudotime trajectory analysis showed that genes associated with extracellular matrix and cell adhesion were significantly upregulated during the transformation from healthy status to OA. Additionally, the enrichment score of Fibroblast 3 in the OA tissue was higher than that in healthy tissue, which indicated that fibroblast 3 may promote the development of OA. Finally, four genes (<i>MTUS2</i>, <i>GPR1</i>, <i>GABRA4</i>, and <i>SGCA</i>) with strong diagnostic performance were identified as the biomarkers for OA. <b>Conclusion:</b> The fibroblast subtypes identified by the present research played a critical role in the pathogenesis of OA, and the four biomarkers may serve as new targets for the early diagnosis and treatment of OA.</p>","PeriodicalId":18371,"journal":{"name":"Mediators of Inflammation","volume":"2025 ","pages":"7066432"},"PeriodicalIF":4.2000,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12483737/pdf/","citationCount":"0","resultStr":"{\"title\":\"Decoding Fibroblast Heterogeneity in Osteoarthritis: Identification of a Fibrosis-Associated Subtype and Novel Diagnostic Biomarkers.\",\"authors\":\"Qingpeng Sun, Feng Niu, Li Wang, Honglin Pi, Chongtao Han, Jun Gao\",\"doi\":\"10.1155/mi/7066432\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p><b>Background:</b> Fibroblasts are key contributors to extracellular matrix remodeling and fibrosis, thereby playing a crucial role in the pathogenesis of osteoarthritis (OA). 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Finally, single sample GSEA (ssGSEA) and differential expression analysis were combined to screen diagnostic biomarkers for OA, and their diagnostic efficacy was assessed by receiver operating characteristic (ROC) curves and principal component analysis (PCA). <b>Results:</b> Using the scRNA-seq data of OA samples, we identified five different cell types (fibroblasts, endothelial cells [ECs], lymphoid cells, mural cells, and myeloid cells), with fibroblasts accounting for the highest proportion. Then, we found that Fibroblast subtype 3 was notably enriched in fibrosis-related pathways. The pseudotime trajectory analysis showed that genes associated with extracellular matrix and cell adhesion were significantly upregulated during the transformation from healthy status to OA. Additionally, the enrichment score of Fibroblast 3 in the OA tissue was higher than that in healthy tissue, which indicated that fibroblast 3 may promote the development of OA. Finally, four genes (<i>MTUS2</i>, <i>GPR1</i>, <i>GABRA4</i>, and <i>SGCA</i>) with strong diagnostic performance were identified as the biomarkers for OA. <b>Conclusion:</b> The fibroblast subtypes identified by the present research played a critical role in the pathogenesis of OA, and the four biomarkers may serve as new targets for the early diagnosis and treatment of OA.</p>\",\"PeriodicalId\":18371,\"journal\":{\"name\":\"Mediators of Inflammation\",\"volume\":\"2025 \",\"pages\":\"7066432\"},\"PeriodicalIF\":4.2000,\"publicationDate\":\"2025-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12483737/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mediators of Inflammation\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1155/mi/7066432\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q2\",\"JCRName\":\"CELL BIOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mediators of Inflammation","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1155/mi/7066432","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0
摘要
背景:成纤维细胞是细胞外基质重塑和纤维化的关键贡献者,因此在骨关节炎(OA)的发病机制中起着至关重要的作用。然而,它们在OA中的异质性和功能亚型仍然知之甚少。方法:从Gene Expression Omnibus (GEO)数据库下载OA单细胞RNA测序(scRNA-seq)数据和两个独立数据集。随后,使用Seurat包对scRNA-seq数据进行规范化和精简,并对不同的细胞类型进行分类。基因集富集分析(GSEA)用于鉴定每个细胞簇中特异性的显著富集的生物过程(bp)。利用Monocle2包,基于成纤维细胞亚型中基因的动态表达变化,分析OA的进展轨迹。最后,结合单样本GSEA (ssGSEA)和差异表达分析筛选OA诊断生物标志物,并通过受试者工作特征(ROC)曲线和主成分分析(PCA)评估其诊断效果。结果:利用OA样本的scRNA-seq数据,我们鉴定出五种不同的细胞类型(成纤维细胞、内皮细胞、淋巴样细胞、壁细胞和髓样细胞),其中成纤维细胞所占比例最高。然后,我们发现成纤维细胞亚型3在纤维化相关通路中显著富集。伪时间轨迹分析显示,在健康状态向OA转变的过程中,与细胞外基质和细胞粘附相关的基因显著上调。此外,成纤维细胞3在OA组织中的富集评分高于健康组织,这表明成纤维细胞3可能促进OA的发展。最后,确定了4个具有较强诊断性能的基因(MTUS2、GPR1、GABRA4和SGCA)作为OA的生物标志物。结论:本研究鉴定的成纤维细胞亚型在OA的发病机制中发挥了关键作用,这4种生物标志物可能成为OA早期诊断和治疗的新靶点。
Decoding Fibroblast Heterogeneity in Osteoarthritis: Identification of a Fibrosis-Associated Subtype and Novel Diagnostic Biomarkers.
Background: Fibroblasts are key contributors to extracellular matrix remodeling and fibrosis, thereby playing a crucial role in the pathogenesis of osteoarthritis (OA). However, their heterogeneity and functional subtypes in OA remain poorly understood. Methods: The single-cell RNA sequencing (scRNA-seq) data of OA and two independent datasets were downloaded from the Gene Expression Omnibus (GEO) database. Subsequently, the Seurat package was utilized to normalize and downscale the scRNA-seq data and classify different cell types. Gene set enrichment analysis (GSEA) was applied to identify significantly enriched biological processes (BPs) specific in each cell cluster. Employing the Monocle2 package, the progression trajectories of OA were analyzed based on the dynamic gene expression changes in the fibroblast subtypes. Finally, single sample GSEA (ssGSEA) and differential expression analysis were combined to screen diagnostic biomarkers for OA, and their diagnostic efficacy was assessed by receiver operating characteristic (ROC) curves and principal component analysis (PCA). Results: Using the scRNA-seq data of OA samples, we identified five different cell types (fibroblasts, endothelial cells [ECs], lymphoid cells, mural cells, and myeloid cells), with fibroblasts accounting for the highest proportion. Then, we found that Fibroblast subtype 3 was notably enriched in fibrosis-related pathways. The pseudotime trajectory analysis showed that genes associated with extracellular matrix and cell adhesion were significantly upregulated during the transformation from healthy status to OA. Additionally, the enrichment score of Fibroblast 3 in the OA tissue was higher than that in healthy tissue, which indicated that fibroblast 3 may promote the development of OA. Finally, four genes (MTUS2, GPR1, GABRA4, and SGCA) with strong diagnostic performance were identified as the biomarkers for OA. Conclusion: The fibroblast subtypes identified by the present research played a critical role in the pathogenesis of OA, and the four biomarkers may serve as new targets for the early diagnosis and treatment of OA.
期刊介绍:
Mediators of Inflammation is a peer-reviewed, Open Access journal that publishes original research and review articles on all types of inflammatory mediators, including cytokines, histamine, bradykinin, prostaglandins, leukotrienes, PAF, biological response modifiers and the family of cell adhesion-promoting molecules.