不同骨密度人群外周血单核细胞的基因表达谱分析

Gene Expression Analysis of PBMC in Different BMD Groups

目的探讨不同骨密度人群外周血单核细胞基因表达谱的变化。方法运用R语言对公共数据平台NCBI下的基因芯片数据集GSE7158进行差异基因表达分析、DAVID(the database for annotation,visualization and integrated discovery)注释工具对差异基因进行功能注释、Medcalc统计软件进行受试者工作特征(receiver operator characteristic,ROC)曲线分析。结果与高骨密度组相比,低骨密度组外周血单核细胞中61个基因表达改变(倍数大于1.5,P<0.05),且多数基因呈上调模式。表达差异基因分别参与了71个生物学过程(biological process,BP)条目、4个细胞组分(cellular component,CC)条目、6个分子功能(molecular function,MF)条目以及1个KEGG通路。基因CXCL10、IFI44L、IGKV4-1作为生物标志物鉴别两组样本具有较好的特异性、敏感度及准确性,且CXCL10&IGKV4-1基因联合检测能够增加鉴别诊断的准确性。结论运用高通量组学与相关数据库结合有助于全面的解析病理状态下生物样本中的组学改变,为后续骨质疏松症研究提供系统的分子学机制基础。

Objective To explore gene expression profile of peripheral blood mononuclear cells（ PBMC） in different bone mineral density（ BMD） groups. Methods We performed R language to analyze the gene expression profile based on GSE7158 dataset from NCBI; DAVID online tools was utilized for gene functional annotation; Medcalc statistical software was used for ROC evaluation. Results 61 genes were dysregulated and most of them were up-regulated in low BMD compared with high BMD group. The differentiate expressed genes involved in 71 GO-BP,4 GO-CC,6 GO-MF terms and 1 KEGG Pathway. CXCL10,IFI44 L,IGKV4-1 were good markers for differential diagnoses between the two groups,and combination examination of CXCL10 IGKV4-1 was better for ROC evaluation. Conclusion High-throughput dataset and bioinformatics tools is help for genomic analysis under different pathological conditions,which will provide more comprehensive molecular mechanisms for further osteoporosis study.