Table3_Identification of mitophagy-related biomarkers in human osteoporosis based on a machine learning model.XLSX

Background: Osteoporosis (OP) is a chronic bone metabolic disease and a serious global public health problem. Several studies have shown that mitophagy plays an important role in bone metabolism disorders; however, its role in osteoporosis remains unclear. Methods: The Gene Expression Omnibus (GEO) database was used to download GSE56815, a dataset containing low and high BMD, and differentially expressed genes (DEGs) were analyzed. Mitochondrial autophagy-related genes (MRG) were downloaded from the existing literature, and highly correlated MRG were screened by bioinformatics methods. The results from both were taken as differentially expressed (DE)-MRG, and Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. Protein-protein interaction network (PPI) analysis, support vector machine recursive feature elimination (SVM-RFE), and Boruta method were used to identify DE-MRG. A receiver operating characteristic curve (ROC) was drawn, a nomogram model was constructed to determine its diagnostic value, and a variety of bioinformatics methods were used to verify the relationship between these related genes and OP, including GO and KEGG analysis, IP pathway analysis, and single-sample Gene Set Enrichment Analysis (ssGSEA). In addition, a hub gene-related network was constructed and potential drugs for the treatment of OP were predicted. Finally, the specific genes were verified by real-time quantitative polymerase chain reaction (RT-qPCR). Results: In total, 548 DEGs were identified in the GSE56815 dataset. The weighted gene co-expression network analysis(WGCNA) identified 2291 key module genes, and 91 DE-MRG were obtained by combining the two. The PPI network revealed that the target gene for AKT1 interacted with most proteins. Three MRG (NELFB, SFSWAP, and MAP3K3) were identified as hub genes, with areas under the curve (AUC) 0.75, 0.71, and 0.70, respectively. The nomogram model has high diagnostic value. GO and KEGG analysis showed that ribosome pathway and cellular ribosome pathway may be the pathways regulating the progression of OP. IPA showed that MAP3K3 was associated with six pathways, including GNRH Signaling. The ssGSEA indicated that NELFB was highly correlated with iDCs (cor = −0.390, p < 0.001). The regulatory network showed a complex relationship between miRNA, transcription factor(TF) and hub genes. In addition, 4 drugs such as vinclozolin were predicted to be potential therapeutic drugs for OP. In RT-qPCR verification, the hub gene NELFB was consistent with the results of bioinformatics analysis. Conclusion: Mitophagy plays an important role in the development of osteoporosis. The identification of three mitophagy-related genes may contribute to the early diagnosis, mechanism research and treatment of OP.背景：骨质疏松症（Osteoporosis, OP）是一种慢性骨代谢疾病，亦是严峻的全球性公共卫生问题。既往多项研究表明，线粒体自噬在骨代谢紊乱中发挥重要作用，但其在骨质疏松症中的具体作用仍未明确。 方法：本研究通过基因表达综合数据库（Gene Expression Omnibus, GEO）下载数据集GSE56815，该数据集包含低骨密度与高骨密度样本；随后分析筛选差异表达基因（differentially expressed genes, DEGs）。从已发表文献中获取线粒体自噬相关基因（mitophagy-related genes, MRG），并通过生物信息学方法筛选与骨代谢高度相关的MRG。将上述两组基因的交集作为差异表达线粒体自噬相关基因（DE-MRG），进而开展基因本体（Gene Ontology, GO）富集分析与京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）富集分析。通过蛋白质相互作用网络（protein-protein interaction network, PPI）分析、支持向量机递归特征消除（support vector machine recursive feature elimination, SVM-RFE）以及Boruta算法筛选核心DE-MRG。绘制受试者工作特征曲线（receiver operating characteristic curve, ROC），构建列线图模型以评估其诊断价值，并采用多种生物信息学方法验证上述相关基因与骨质疏松症的关联，包括GO与KEGG富集分析、IP通路分析以及单样本基因集富集分析（single-sample Gene Set Enrichment Analysis, ssGSEA）。此外，构建核心基因相关调控网络，并预测潜在的骨质疏松症治疗药物。最终通过实时定量聚合酶链反应（real-time quantitative polymerase chain reaction, RT-qPCR）对核心基因进行实验验证。 结果：本研究在GSE56815数据集中共筛选得到548个DEGs。加权基因共表达网络分析（weighted gene co-expression network analysis, WGCNA）鉴定出2291个关键模块基因，结合两组基因后共获得91个DE-MRG。PPI网络分析显示，AKT1靶基因与多数蛋白质存在相互作用。最终筛选得到3个核心MRG：NELFB、SFSWAP与MAP3K3，其受试者工作特征曲线下面积（areas under the curve, AUC）分别为0.75、0.71与0.70。列线图模型具有较高的诊断价值。GO与KEGG富集分析结果表明，核糖体通路及细胞核糖体相关通路可能参与调控骨质疏松症的进展。IP通路分析显示，MAP3K3与包括GnRH信号通路在内的6条通路密切相关。ssGSEA分析结果显示，NELFB与未成熟树突状细胞（immature dendritic cells, iDCs）呈显著负相关（cor = −0.390, p < 0.001）。调控网络揭示了微小RNA（miRNA）、转录因子（transcription factor, TF）与核心基因之间的复杂调控关系。此外，本研究预测得到包括烯菌酮（vinclozolin）在内的4种药物为骨质疏松症潜在治疗药物。RT-qPCR验证结果显示，核心基因NELFB的表达变化与生物信息学分析结果一致。 结论：线粒体自噬在骨质疏松症的发生发展中发挥重要作用。本研究鉴定得到的3个线粒体自噬相关基因可为骨质疏松症的早期诊断、机制研究及治疗策略开发提供潜在靶点。