MXRA5和MYC作为肥胖和骨关节炎的诊断标志物和免疫浸润特征

目的 利用生物信息学和机器学习鉴定肥胖和骨关节炎(osteoarthritis，OA)的关键基因与免疫浸润细胞的相关性。方法 从基因表达数据库(gene expression omnibus，GEO)中筛选GSE55235、GSE44000和GSE151839 3个数据集，通过R软件获得差异表达基因(differentially expressed genes，DEGs)，并通过基因本体功能(gene ontology，GO)和京都基因与基因组百科全书(Kyoto encyclopedia of genes and genomes，KEGG)信号通路富集分析，探索其潜在的生物学功能。采用最小绝对收缩和选择算子（least absolute shrinkage and selection operator，LASSO）回归算法结合（support vector machine，SVM）筛选特征基因，并利用受试者操作特征（receiver operating characteristic，ROC）曲线验证关键基因的诊断价值，并使用CIBERSORT算法评估免疫浸润，通过NetworkAnalyst数据库预测靶miRNA和Cytoscape软件构建mRNA-miRNA调控网络，分析关键基因与免疫浸润的相关性。结果 GO基因富集分析获得99个差异表达基因（differentially expressed genes，DEGs）。免疫系统和免疫应答中的细胞活化被大量富集。KEGG通路分析显示，白细胞介素（interleukin，IL）-17、核因子κB（nuclear factor-κB，NF-κB）、B细胞受体和趋化因子信号通路显著富集。基于机器学习鉴定出两个关键诊断基因(MXRA5和MYC)。免疫浸润分析显示MXRA5与静息和活化的CD4记忆T细胞、活化的NK细胞、静息和活化的肥大细胞、M0巨噬细胞有关。此外，MYC与静息和活化的CD4记忆T细胞、浆细胞、活化的NK细胞、静息和活化的肥大细胞、M2巨噬细胞和嗜酸性粒细胞有关。CD4+细胞、NK细胞和肥大细胞与这2个枢轴基因有显著相关性。结论 通过生物信息学分析鉴定出2个免疫相关的关键基因，可能为肥胖相关性OA的治疗提供新的靶点。

MXRA5 and MYC as diagnostic markers and immune infiltrative features in obesity and osteoarthritis

Objective Bioinformatics and machine learning were used to identify associations between key genes in obesity and osteoarthritis (OA) and immune infiltrating cells. Methods Three datasets GSE55235, GSE44000 and GSE151839 were screened from the gene expression omnibus (GEO) database, and differentially expressed genes (DEGs) were obtained by R software, and their potential biological functions were explored through gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) signaling pathway enrichment analysis. The minimum absolute contraction and selection operator (LASSO) regression algorithm combined with support vector machine (SVM) was used to screen characteristic genes, the diagnostic value of key genes was verified by receiver operating characteristic (ROC) curve, and the immune infiltration was assessed by CIBERSORT algorithm. The mRNA-miRNA regulatory network was constructed using NetworkAnalyst database to predict target miRNA and Cytoscape software, and the correlation between key genes and immune infiltration was analyzed. Results GO gene enrichment analysis obtained 99 DEGs. Cellular activation in the immune system and immune response is highly enriched. KEGG pathway analysis showed significant enrichment of interleukin (IL-17), nuclear factor-κB (NF-κB), B-cell receptor and chemokine signaling pathways. Two key diagnostic genes (MXRA5 and MYC) were identified based on machine learning. Immunoinfiltration analysis showed that MXRA5 was associated with resting and activated CD4 memory T cells, activated NK cells, resting and activated mast cells, and M0 macrophages. In addition, MYC is associated with resting and activated CD4 memory T cells, plasma cells, activated NK cells, resting and activated mast cells, M2 macrophages, and eosinophils. CD4+ cells, NK cells and mast cells were significantly associated with these two pivot genes. Conclusion Two key immune-related genes were identified through bioinformatics analysis, which may provide new targets for the treatment of obesity-related OA.