【摘要】 目的 金黄色葡萄球菌（Staphylococcus aureus,SA）诱导的骨髓炎（osteomyelitis,OM）作为骨科常见难治性感染，其早期诊断与免疫微环境解析面临严峻挑战。焦亡相关基因（pyroptosis-related genes,PRGs）的表达谱与SA-OM密切相关，这些基因可能通过线粒体相关通路影响免疫微环境，从而参与疾病进程，且特定基因组合可用于构建高精度诊断模型。本研究旨在整合多组学与机器学习，筛选SA-OM中关键的焦亡相关诊断生物标志物，构建高精度诊断模型，并解析其通过“线粒体-炎症”互作影响免疫微环境的分子机制。方法 基于GEO数据库收录的3个SA-OM数据集（GSE6269/GSE16129/GSE30119），共纳入143例SA-OM患者与79例健康对照样本，通过数据预处理（sva包校正批次效应）、差异表达分析（limma包筛选DE-PRGs,adj. P<0.05且^|log₂FC^|>0.263）、共表达网络构建（WGCNA算法鉴定关键模块基因，softThreshold=5）、多组学交叉验证（Pearson相关性分析筛选MR-PRGs）、机器学习建模（SVM-RFE/LASSO/随机森林交叉验证筛选特征基因，n=9）及诊断模型构建（逻辑回归列线图模型，通过AUC、校准曲线slope与DCA评估效能），结合免疫微环境解析（CIBERSORT/ssGSEA定量分析22种免疫细胞浸润水平）。结果 发现23个DE-PRGs中8个关键基因构成的诊断模型在训练集（AUC=0.89,95%CI:0.83～0.95）与验证集（AUC=0.83,95%CI:0.76～0.90）均展现出优异性能，RT-qPCR实验进一步验证，在SA-OM组中，焦亡通路关键基因Caspase-1与IL-18的mRNA表达水平较对照组显著上调（P<0.05），与生物信息学结论相互印证。且METTL3-MRPL39轴显著富集于代谢通路与线粒体基因表达生物学过程，进一步揭示疾病组Th1/Th17细胞浸润水平较对照组升高3.2倍（P<0.001）,METTL3表达与效应T细胞浸润呈正相关（r=0.65,P=0.008）。结论 研究系统阐明SA-OM中焦亡相关基因的调控网络，构建的诊断模型为早期筛查提供新工具，同时发现的线粒体-炎症互作机制及特异性免疫微环境特征为靶向治疗策略研发奠定理论基础。更多还原

【Abstract】 Objective Staphylococcus aureus（SA）-induced osteomyelitis（OM） is a common refractory orthopedic infection, presenting substantial challenges in early diagnosis and immune microenvironment characterization. Expression profiles of pyroptosis-related genes（PRGs） are closely associated with SA-OM; these genes may influence the immune microenvironment through mitochondrialrelated pathways, thereby participating in disease progression, and specific gene combinations can be utilized to construct high-precision diagnostic models. This study aims to integrate multi-omics and machine learning to screen key pyroptosis-related diagnostic biomarkers in SA-OM, construct a high-precision diagnostic model, and elucidate its molecular mechanism influencing the immune microenvironment through “mitochondriainflammation” crosstalk.Methods Based on 3 SA-OM datasets（GSE6269/GSE16129/GSE30119） retrieved from the GEO database, a total of 143 SA-OM patients and 79 healthy control samples were enrolled. Data preprocessing（batch effect correction using the sva package）, differential expression analysis（DE-PRGs screened via the limma package, adj. P<0. 05 & ^|log₂ FC^| >0. 263）, co-expression network construction（key module genes identified through WGCNA algorithm, softThreshold=5）, multi-omics cross-validation（Pearson correlation analysis for MR-PRGs screening）, machine learning modeling（feature genes selected via SVMRFE/LASSO/random forest cross-validation, n=9）, and diagnostic model construction（logistic regression nomogram model, efficacy evaluated through AUC, calibration curve slope, and DCA） were performed, combined with immune microenvironment analysis（CIBERSORT/ssGSEA quantitative analysis of 22 immune cell infiltration levels）. Results Among 23 DE-PRGs, a diagnostic model comprising 8 key genes demonstrated excellent performance in both the training set（AUC=0. 89, 95%CI: 0. 83 to 0. 95） and validation set（AUC=0. 83, 95%CI: 0. 76 to 0. 90）. RT-qPCR experiments further validated that the mRNA expression levels of the key pyroptosis pathway genes Caspase-1 and IL-18 in the SA-OM group were significantly upregulated compared with the control group（P<0. 05）, corroborating the bioinformatics findings. The METTL3-MRPL39 axis was significantly enriched in “metabolic pathways” and “mitochondrial gene expression” biological processes. Furthermore, Th1/Th17 cell infiltration levels in the disease group were 3. 2-fold higher than those in the control group（P<0. 001）, and METTL3 expression exhibited positive correlation with effector T cell infiltration（r=0. 65, P=0. 008）. Conclusion This study systematically elucidates the regulatory network of pyroptosis-related genes in SA-OM. The constructed diagnostic model provides a novel tool for early screening, while the identified mitochondrial-inflammation interplay mechanisms and specific immune microenvironment characteristics establish a theoretical foundation for the development of targeted therapeutic strategies.更多还原