骨质疏松免疫相关标志物的生物信息学分析及其鉴定

Bioinformatics analysis and identification to immune-related markers of osteoporosis

目的 鉴定骨质疏松中与免疫相关的失调机制及潜在的诊断预测标志物。方法 从GSE35958和GSE56815数据集中下载骨质疏松和对照人群的基因表达谱数据。通过筛选差异表达基因(DEG)和免疫学数据库和分析门户(ImmPort)数据库比较获得免疫相关DEG。利用Clusterprofiler软件包对免疫相关DEG进行富集分析。采用检索相互作用基因/蛋白质的搜索工具STRING数据库构建蛋白相互作用网络并鉴定网络内连接度最大的前10个基因作为候选基因。随后利用受试者工作特征(ROC)曲线,logistic回归和列线图评价候选基因的诊断预测作用。最后,PCR和Western blot法检测这些基因在骨质疏松症患者骨髓组织的差异表达。结果 通过交集分析获得138个免疫相关DEG。富集分析结果提示这些基因参与免疫炎症等生物学功能,以及T细胞受体、丝裂原激活蛋白激酶(MAPK)、大鼠肉瘤病毒癌基因同源物(Ras)、破骨细胞分化和B细胞受体等信号通路。另外,在候选基因中,在骨质疏松中上调表达的血管内皮生长因子A(VEGFA)和表皮生长因子受体(EGFR),以及下调表达的蛋白激酶B1(AKT1)、 SRC和JUN原癌基因的连接度最大。其中,VEGFA、 EGFR、 JUN和AKT1的诊断预测价值最佳。结论 免疫相关基因的筛选将有助于对骨质疏松症疾病的理解及免疫治疗靶点开发。

Objective To identify immune-related dysregulation mechanisms and potential diagnostic predictive biomarkers in osteoporosis.Methods Gene expression data for both osteoporosis and control populations were retrieved from the GSE35958 and GSE56815 datasets.Immune-related differentially expressed genes(DEGs)were obtained by screening DEGs and were compared with the immunology database and analysis portal(ImmPort)database.Enrichment analysis of these immune-related DEGs was conducted using the Clusterprofiler software package.A protein-protein interaction network was built with the STRING database,which is a search tool for finding interacting genes/proteins,and the top 10 genes with the highest network connectivity were identified as candidate genes.Subsequently,the diagnostic predictive effect of candidate genes was evaluated using receiver operating characteristic(ROC)curves,logistic regression,and column plots.Finally,PCR and Western blot analysis were applied to detect the differential expression of these genes in bone marrow tissue of patients with osteoporosis.Results A total of 138 immune-related DEGs were obtained through intersection analysis.The results of the enrichment analysis indicated that these genes were involved in biological functions such as immune inflammation and signaling pathways including T cell receptors,mitogen activated protein kinase(MAPK),rat sarcoma virus oncogene homologs(Ras),osteoclast differentiation,and B cell receptors.In addition,among the candidate genes,upregulated vascular endothelial growth factor A(VEGFA)and epidermal growth factor receptor(EGFR)and downregulated AKT1,SRC,and JUN in osteoporosis showed the highest connectivity.Among them,VEGFA,EGFR,JUN,and AKT1 demonstrated the best diagnostic predictive value.Conclusion The screening of immune-related DEGs will enhance the understanding of osteoporosis and facilitate the development of immunotherapy targets.