基于生物信息学分析识别骨关节炎和肌肉减少症患者共同的关键基因和通路（英文）

Identification of shared key genes and pathways in osteoarthritis and sarcopenia patients based on bioinformatics analysis

摘要

目的：骨关节炎(osteoarthritis,OA)和肌肉减少症是老年人中的重要健康问题，严重影响老年人的日常活动和生活质量，二者之间的关系仍不明确。本研究旨在探究与OA和肌肉减少症相关的共同关键基因和生物通路。方法：从基因表达综合数据库(Gene Expression Omnibus,GEO)获取OA和肌肉减少症的基因表达谱。利用R软件分别比较OA和肌肉减少症样本与正常对照组的差异表达基因(differentially expressed genes,DEGs)，并通过维恩图分析提取共同的DEGs。对共同的DEGs进行基因本体论(Gene Ontology,GO)和京都基因与基因组数据库(Kyoto Encyclopedia of Genes and Genomes,KEGG)功能富集分析，识别与2种疾病相关的生物学通路。通过蛋白-蛋白相互作用(protein-protein interaction,PPI)网络分析和最大集团中心性(maximal clique centrality,MCC)算法对每个基因的关键性进行排序，筛选出候选枢纽基因。根据在2个验证数据集中的基因差异表达分析结果进一步筛选OA和肌肉减少症共同的关键基因，并通过受试者操作特征曲线(receiver operating characteristic,ROC)曲线分析关键基因在预测OA和肌肉减少症方面的潜在价值。随后，构建OA和肌肉减少症小鼠模型，采用苏木精和伊红染色、番红O/固绿染色验证OA小鼠模型，转棒实验和测量股四头肌的肌肉质量验证肌肉减少症小鼠模型。通过实时反转录聚合酶链反应(real-time reverse transcription PCR,real-time RT-PCR)检测选定的关键基因在OA和肌肉减少症小鼠中的mRNA表达水平。最后进行基因集富集分析(gene set enrichment analysis,GSEA)来识别2种疾病中与所筛选的共同关键基因相关的信号通路。结果：在OA和肌肉减少症基因表达谱中共筛选出了89个共同的差异表达基因，包含76个共同上调的基因和13个共同下调的基因。89个DEGs在蛋白质消化和吸收、PI3K-Akt信号通路及细胞外基质-受体相互作用中显著富集。通过对89个共同DEGs的PPI网络分析和MCC算法筛选出了排名前17的候选枢纽基因，根据17个候选枢纽基因在验证数据集中的差异表达分析结果，最终筛选出AEBP1和COL8A2作为这2种疾病共同的关键基因，二者在疾病组中都显著上调(均P<0.05)。在OA和肌肉减少症相关数据集中，AEBP1和COL8A2的表达水平预测的ROC曲线的曲线下面积(area under the curve,AUC)均大于0.7，表明AEBP1和COL8A2在预测OA和肌肉减少症方面均具有潜在价值。Real-time RT-PCR结果表明AEBP1和COL8A2的m RNA表达水平在疾病组中显著上调(均P<0.05)，与生物信息学分析的结果一致。GSEA结果显示：在OA和肌肉减少症中，AEBP1和COL8A2与细胞外基质-受体相互作用、核糖体、氧化磷酸化密切相关。结论：AEBP1和COL8A2有潜力作为OA和肌肉减少症的共同生物标志物。细胞外基质-受体相互作用这一重要通路可能成为预防和治疗OA和肌肉减少症的潜在靶点。

Abstract

Objective 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, AEBP1 and COL8A2 were ultimately selected as the common key genes for both diseases, both of which showed a significant upregulation trend in the disease groups (all P<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 AEBP1 and COL8A2 were significantly upregulated in the disease groups (all P<0.05), consistent with the results observed in the bioinformatics analysis. GSEA revealed that AEBP1 and COL8A2 were closely related to extracellular matrix-receptor interaction, ribosome, and oxidative phosphorylation in OA and sarcopenia. Conclusion AEBP1 and COL8A2 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.

关键词

骨关节炎 / 肌肉减少症 / 生物信息学 / 细胞外基质-受体相互作用 / 关键基因

Key words

osteoarthritis / sarcopenia / bioinformatics / extracellular matrix-receptor interaction / key genes