Sun Yuyan, Luo Ziyu, Ling Huixian, Wu Sha, Shen Hongwei, Fu Yuanyuan, Ngo Thainamanh, Wang Wen, Kong Ying
Department of Rehabilitation, Second Xiangya Hospital, Central South University, Changsha 410011.
Medical Research Center, Second Xiangya Hospital, Central South University, Changsha 410011, China.
Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2025 Mar 28;50(3):430-446. doi: 10.11817/j.issn.1672-7347.2025.240669.
OBJECTIVES: Osteoarthritis (OA) and sarcopenia are significant health concerns in the elderly, substantially impacting their daily activities and quality of life. However, the relationship between them remains poorly understood. This study aims to uncover common biomarkers and pathways associated with both OA and sarcopenia. METHODS: Gene expression profiles related to OA and sarcopenia were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between disease and control groups were identified using R software. Common DEGs were extracted via Venn diagram analysis. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify biological processes and pathways associated with shared DEGs. Protein-protein interaction (PPI) networks were constructed, and candidate hub genes were ranked using the maximal clique centrality (MCC) algorithm. Further validation of hub gene expression was performed using 2 independent datasets. Receiver operating characteristic (ROC) curve analysis was used to evaluate the predictive value of key genes for OA and sarcopenia. Mouse models of OA and sarcopenia were established. Hematoxylin-eosin and Safranin O/Fast Green staining were used to validate the OA model. The sarcopenia model was validated via rotarod testing and quadriceps muscle mass measurement. Real-time reverse transcription PCR (real-time RT-PCR) was employed to assess the mRNA expression levels of candidate key genes in both models. Gene set enrichment analysis (GSEA) was conducted to identify pathways associated with the selected shared key genes in both diseases. RESULTS: A total of 89 common DEGs were identified in the gene expression profiles of OA and sarcopenia, including 76 upregulated and 13 downregulated genes. These 89 DEGs were significantly enriched in protein digestion and absorption, the PI3K-Akt signaling pathway, and extracellular matrix-receptor interaction. PPI network analysis and MCC algorithm analysis of the 89 common DEGs identified the top 17 candidate hub genes. Based on the differential expression analysis of these 17 candidate hub genes in the validation datasets, and were ultimately selected as the common key genes for both diseases, both of which showed a significant upregulation trend in the disease groups (all <0.05). The value of area under the curve (AUC) for AEBP1 and COL8A2 in the OA and sarcopenia datasets were all greater than 0.7, indicating that both genes have potential value in predicting OA and sarcopenia. Real-time RT-PCR results showed that the mRNA expression levels of and were significantly upregulated in the disease groups (all <0.05), consistent with the results observed in the bioinformatics analysis. GSEA revealed that and were closely related to extracellular matrix-receptor interaction, ribosome, and oxidative phosphorylation in OA and sarcopenia. CONCLUSIONS: and have the potential to serve as common biomarkers for OA and sarcopenia. The extracellular matrix-receptor interaction pathway may represent a potential target for the prevention and treatment of both OA and sarcopenia.
目的：骨关节炎（OA）和肌肉减少症是老年人面临的重大健康问题，对他们的日常活动和生活质量有重大影响。然而，它们之间的关系仍知之甚少。本研究旨在揭示与OA和肌肉减少症相关的共同生物标志物和通路。 方法：从基因表达综合数据库（GEO）中检索与OA和肌肉减少症相关的基因表达谱。使用R软件识别疾病组和对照组之间的差异表达基因（DEG）。通过维恩图分析提取共同的DEG。进行基因本体（GO）和京都基因与基因组百科全书（KEGG）富集分析，以识别与共享DEG相关的生物学过程和通路。构建蛋白质-蛋白质相互作用（PPI）网络，并使用最大团中心性（MCC）算法对候选枢纽基因进行排名。使用2个独立数据集对枢纽基因表达进行进一步验证。采用受试者工作特征（ROC）曲线分析评估关键基因对OA和肌肉减少症的预测价值。建立OA和肌肉减少症的小鼠模型。苏木精-伊红和番红O/固绿染色用于验证OA模型。通过转棒试验和股四头肌质量测量验证肌肉减少症模型。采用实时逆转录PCR（实时RT-PCR）评估两种模型中候选关键基因的mRNA表达水平。进行基因集富集分析（GSEA）以识别与两种疾病中选定的共享关键基因相关的通路。 结果：在OA和肌肉减少症的基因表达谱中总共鉴定出89个共同的DEG，包括76个上调基因和13个下调基因。这89个DEG在蛋白质消化和吸收、PI3K-Akt信号通路以及细胞外基质-受体相互作用中显著富集。对这89个共同的DEG进行PPI网络分析和MCC算法分析，确定了前17个候选枢纽基因。基于这17个候选枢纽基因在验证数据集中的差异表达分析，最终选择AEBP1和COL8A2作为两种疾病的共同关键基因，二者在疾病组中均呈现显著上调趋势（均P<0.05）。AEBP1和COL8A2在OA和肌肉减少症数据集中的曲线下面积（AUC）值均大于0.7，表明这两个基因在预测OA和肌肉减少症方面均具有潜在价值。实时RT-PCR结果显示，疾病组中AEBP1和COL8A2的mRNA表达水平显著上调（均P<0.05），与生物信息学分析结果一致。GSEA显示，AEBP1和COL8A2与OA和肌肉减少症中的细胞外基质-受体相互作用、核糖体和氧化磷酸化密切相关。 结论：AEBP1和COL8A2有可能作为OA和肌肉减少症的共同生物标志物。细胞外基质-受体相互作用通路可能是预防和治疗OA和肌肉减少症的潜在靶点。
Zhong Nan Da Xue Xue Bao Yi Xue Ban. 2025-3-28
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