内科

目的应用加权基因共表达网络分析（WGCNA）联合差异表达基因（DEG）分析法鉴定重度抑郁症（MDD）的关键基因。方法从基因表达数据库中获取MDD患者和健康志愿者死后脑组织mRNA微阵列数据。应用WGCNA和DEG分析法筛选与MDD最显著相关的模块和DEG，对WGCNA模块基因和DEG进行基因本体论富集分析和京都基因与基因组百科全书通路富集分析，取WGCNA模块基因与DEG的交集，绘制受试者工作特征曲线评估交集基因诊断MDD的能力。结果最终获得10个基因（FLJ20021、FLJ30058、NNAT、MRPS12、KCNK4、TROVE2、MESP1、MIF、TMEM93、SYNGR1），均具有良好诊断MDD的潜力。结论 FLJ20021、FLJ30058、NNAT、MRPS12、KCNK4、TROVE2、MESP1、MIF、TMEM93、SYNGR1可作为辅助诊断MDD的生物标志物。

Objective To identify hub genes for major depressive disorder (MDD) by weighted gene co-expression network analysis (WGCNA) combined with differential expressions gene (DEG) analysis. Methods The mRNA microarray data of postmortem brain tissues from MDD patients and healthy volunteers were obtained from the Gene Expression Omnibus. Both WGCNA and DEG analysis were used to screen the modules most significantly associated with MDD and DEGs, Gene Ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis were performed on the WGCNA module genes and DEGs, intersection genes were obtained by matching DEGs to WGCNA module genes, and receiver operating characteristic curves were plotted to assess the abilities of the intersection genes to diagnose MDD. Results At the end, 10 genes were obtained, including FLJ20021, FLJ30058, NNAT, MRPS12, KCNK4, TROVE2, MESP1, MIF, TMEM93, and SYNGR1; all of which had the potential to diagnose MDD effectively.Conclusion FLJ20021, FLJ30058, NNAT, MRPS12, KCNK4, TROVE2, MESP1, MIF, TMEM93, and SYNGR1 can be used as biomarkers to assist in the diagnosis of MDD.

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