DOI: 10.57760/sciencedb.j00217.11821
title: Bioinformatics-Based Identification of Key Fibroblast Module Biomarkers and Potential Drug Candidates in Osteoarthritis
title: 基于生物信息学挖掘骨关节炎中成纤维细胞模块关键生物标志物及潜在药物候选物

description: Objective To investigate synovial fibroblast–associated molecular features in osteoarthritis (OA), identify core genes with diagnostic value and functional relevance, and preliminarily explore potential small-molecule interactions.Methods Single-cell RNA sequencing data and three bulk synovial transcriptome datasets were integrated. Cell type annotation, cell–cell communication analysis, and high-dimensional weighted gene co-expression network analysis (hdWGCNA) were performed to identify OA-related fibroblast modules. Module genes intersected with upregulated differentially expressed genes were defined as fibroblast-related gene sets, and core genes were screened using multiple machine learning algorithms. Diagnostic performance was evaluated using ROC analysis and validated in an external cohort. Functional and structural relevance was further explored through single-gene GSEA, virtual knockout analysis, and molecular docking.Results Seven synovial cell types were identified by single-cell analysis, with fibroblasts exhibiting enhanced intercellular communication in OA. HdWGCNA identified two OA-associated fibroblast modules, yielding 39 candidate genes mainly related to extracellular matrix organization. Five core genes were ultimately identified, among which SSR4 and PRDX4 were significantly upregulated in OA and demonstrated strong diagnostic performance in both training and validation cohorts (AUC &gt; 0.90). Functional analyses suggested their involvement in energy metabolism, extracellular matrix remodeling, and inflammatory signaling pathways. Molecular docking indicated that several small molecules, including vitamin E, L-methionine, and streptozotocin, showed stable binding conformations with SSR4 and PRDX4, with vitamin E exhibiting relatively favorable binding potential.Conclusion This study systematically characterizes fibroblast-driven molecular signatures in OA and identifies SSR4 and PRDX4 as potential diagnostic biomarkers and candidate intervention targets, providing a foundation for future mechanistic and drug discovery studies.
description: Objective To investigate synovial fibroblast–associated molecular features in osteoarthritis (OA), identify core genes with diagnostic value and functional relevance, and preliminarily explore potential small-molecule interactions.Methods Single-cell RNA sequencing data and three bulk synovial transcriptome datasets were integrated. Cell type annotation, cell–cell communication analysis, and high-dimensional weighted gene co-expression network analysis (hdWGCNA) were performed to identify OA-related fibroblast modules. Module genes intersected with upregulated differentially expressed genes were defined as fibroblast-related gene sets, and core genes were screened using multiple machine learning algorithms. Diagnostic performance was evaluated using ROC analysis and validated in an external cohort. Functional and structural relevance was further explored through single-gene GSEA, virtual knockout analysis, and molecular docking.Results Seven synovial cell types were identified by single-cell analysis, with fibroblasts exhibiting enhanced intercellular communication in OA. HdWGCNA identified two OA-associated fibroblast modules, yielding 39 candidate genes mainly related to extracellular matrix organization. Five core genes were ultimately identified, among which SSR4 and PRDX4 were significantly upregulated in OA and demonstrated strong diagnostic performance in both training and validation cohorts (AUC &gt; 0.90). Functional analyses suggested their involvement in energy metabolism, extracellular matrix remodeling, and inflammatory signaling pathways. Molecular docking indicated that several small molecules, including vitamin E, L-methionine, and streptozotocin, showed stable binding conformations with SSR4 and PRDX4, with vitamin E exhibiting relatively favorable binding potential.Conclusion This study systematically characterizes fibroblast-driven molecular signatures in OA and identifies SSR4 and PRDX4 as potential diagnostic biomarkers and candidate intervention targets, providing a foundation for future mechanistic and drug discovery studies.