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已发表论文
多组学整合鉴定出 FGF1 为骨关节炎的诊断生物标志物及 RAS-MAPK 驱动的致病因子
Authors Yan Y, Wang C , Zhang M, Jiang X, Cheng W
Received 13 July 2025
Accepted for publication 15 December 2025
Published 20 December 2025 Volume 2025:18 Pages 17803—17824
DOI https://doi.org/10.2147/JIR.S553461
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 4
Editor who approved publication: Dr Ujjwol Risal
Yiqun Yan,1,2 Chen Wang,1,2 Mingjun Zhang,1,2 Xuemei Jiang,3 Wendan Cheng1,2
1Department of Orthopaedics, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230601, People’s Republic of China; 2Institute of Orthopaedics, Research Center for Translational Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230601, People’s Republic of China; 3Department of Orthopaedics, Huoqiu County First People’s Hospital, Luan, Anhui, 237400, People’s Republic of China
Correspondence: Wendan Cheng, Email chenwendan@ahmu.edu.cn
Background: Osteoarthritis (OA) is a chronic inflammatory disease characterized by cartilage damage, but its pathogenesis remains unclear.
Objective: This study aims to identify potential therapeutic targets for OA and explore associated mechanistic pathways.
Methods: OA-related data from GWAS, eQTLGen, and GEO databases were analyzed. Differential expression analysis, WGCNA, and PPI network analysis identified OA-associated genes. Machine learning algorithms determined diagnostic markers, validated by ROC curve analysis. Mendelian randomization assessed causal relationships, and single-cell sequencing explored gene dynamics in OA cartilage. In vitro and in vivo experiments validated the findings.
Results: We identified 282 differentially expressed genes (DEGs) associated with OA, with 52 hub genes, including FGF1, as a key candidate. Machine learning identified FGF1 as a diagnostic biomarker, validated by ROC curve analysis (AUCs up to 1.000 in the training set, and 0.790 and 0.761 in validation sets). Mendelian randomization suggested a potential causal relationship between FGF1 expression and OA risk (95% CI = 1.002– 1.081, p = 0.041). Single-cell sequencing explored the dynamics of diagnostic marker genes in OA cartilage progression. In vitro and in vivo experiments confirmed FGF1’s role in inflammation and matrix degradation via the RAS-MAPK pathway.
Conclusion: This study confirms FGF1 as a diagnostic biomarker for OA, with a key role in pathogenesis through RAS-MAPK pathway activation. MR analysis provides suggestive causal evidence. FGF1 induces a pro-inflammatory and catabolic state in chondrocytes by upregulating MMP13 and TNFα, while inhibiting Aggrecan synthesis, driving irreversible cartilage matrix destruction. These findings support targeting FGF1 as a novel therapeutic strategy for OA.
Keywords: mendelian randomization, eQTL, osteoarthritis, WGCNA, RF, LASSO, single-cell sequencing
1Department of Orthopaedics, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230601, People’s Republic of China; 2Institute of Orthopaedics, Research Center for Translational Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, 230601, People’s Republic of China; 3Department of Orthopaedics, Huoqiu County First People’s Hospital, Luan, Anhui, 237400, People’s Republic of China
Correspondence: Wendan Cheng, Email chenwendan@ahmu.edu.cn
Background: Osteoarthritis (OA) is a chronic inflammatory disease characterized by cartilage damage, but its pathogenesis remains unclear.
Objective: This study aims to identify potential therapeutic targets for OA and explore associated mechanistic pathways.
Methods: OA-related data from GWAS, eQTLGen, and GEO databases were analyzed. Differential expression analysis, WGCNA, and PPI network analysis identified OA-associated genes. Machine learning algorithms determined diagnostic markers, validated by ROC curve analysis. Mendelian randomization assessed causal relationships, and single-cell sequencing explored gene dynamics in OA cartilage. In vitro and in vivo experiments validated the findings.
Results: We identified 282 differentially expressed genes (DEGs) associated with OA, with 52 hub genes, including FGF1, as a key candidate. Machine learning identified FGF1 as a diagnostic biomarker, validated by ROC curve analysis (AUCs up to 1.000 in the training set, and 0.790 and 0.761 in validation sets). Mendelian randomization suggested a potential causal relationship between FGF1 expression and OA risk (95% CI = 1.002– 1.081, p = 0.041). Single-cell sequencing explored the dynamics of diagnostic marker genes in OA cartilage progression. In vitro and in vivo experiments confirmed FGF1’s role in inflammation and matrix degradation via the RAS-MAPK pathway.
Conclusion: This study confirms FGF1 as a diagnostic biomarker for OA, with a key role in pathogenesis through RAS-MAPK pathway activation. MR analysis provides suggestive causal evidence. FGF1 induces a pro-inflammatory and catabolic state in chondrocytes by upregulating MMP13 and TNFα, while inhibiting Aggrecan synthesis, driving irreversible cartilage matrix destruction. These findings support targeting FGF1 as a novel therapeutic strategy for OA.
Keywords: mendelian randomization, eQTL, osteoarthritis, WGCNA, RF, LASSO, single-cell sequencing