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目的:分析抑郁症患者较健康人各脑区转录组基因的差异表达情况。方法:在基因表达谱数据库(gene expression omnibus，GEO)中搜索抑郁症病例-对照设计并含有原始数据的研究，设定物种为人。以各数据集所有转录组基因为观察变量，利用在线软件Network Analyst进行主成分分析(principal component analysis，PCA)，整体评估抑郁症患者各个脑区(包括前额叶、前扣带回、杏仁核、海马)相对正常人各脑区的转录组改变差异的程度。利用limma算法分别筛选倍数变化为1.2倍、1.5倍、2.0倍的差异基因(differentially expressed gene，DEG)，n P<0.05为差异有统计学意义。对来源于不同数据集不同脑区的差异基因进行维恩分析，以获取该脑区稳定的差异表达基因。利用DAVID在线软件对稳定差异表达的基因进行功能富集分析，探讨抑郁症患者不同脑区的GO功能及KEGG通路的改变情况。n 结果:抑郁症患者各个脑区(前额叶、前扣带回、杏仁核)相对健康对照各个脑区的转录组整体水平改变程度较轻，基于转录组基因表达水平进行的主成分分析无法有效区分健康-疾病状态；几乎不存在倍数变化大于2.0倍的差异表达基因；维恩分析结果提示不同脑区的多个数据集差异基因无交集；倍数变化大于1.2倍相对稳定的差异表达基因功能与抑郁症无直接关联。结论:抑郁症疾病状态下，患者脑组织转录组水平改变微乎其微，因多方面限制识别抑郁症易感特定基因仍具有挑战性。“,”Objective:To investigate the differential expression of transcriptome genes in various brain regions of depression patients compared with normal people.Methods:Major depressive disorder (MDD) case-control design with transcriptomic studies in gene expression omnibus (GEO) were retrieved and selected in this study, by setting the species as human.To explore the expression distance of brain transcriptome between MDD patients and healthy control subjects, the study first carried out principal component analysis (PCA) based on the variables of the whole genes in each set using network analyst web-based software.Next, differentially expressed gene (DEG) analysis was performed using Limma algorithm and the cut-off values were set as 1.2, 1.5, 2.0-fold changes, along with n P<0.05 was considered statistically significant.In order to obtain each brain robust DEGs, the study performed Venn analysis to reveal the common DEGs among independent datasets.Last, DAVID online software was used to perform functional enrichment analysis of stable and differentially expressed genes, and explore the changes of GO function and KEGG pathway in different brain regions of patients with depression.n Results:Compared with the control group, the whole expression level of transcriptome in each brain region (anterior cingulate gyrus, prefrontal lobe, amygdala) of MDD patients was slightly changed, since PCA plots could not distinguish the health controls from MDD patients based on the whole gene expression level.There were almost no differentially expressed genes with multiple variation more than 2.0-fold changes.The results of Venn analysis indicated that there was no overlappped gene in each independent brain regions.Functional enrichment analysis to these DEGs with fold changes ≥ 1.2 showed that these genes were enriched in some GO terms and KEGG pathways which were not directly associated with MDD, further implying that gene expression changes were not potential forces driving the onset of depression.Conclusion:These results based on the integration of multiple datasets highlight that gene expression levels exhibit a small fluctuation between MDD and normal brains.Thus, it is still challenging to recognize the specific genes of depression susceptibility due to various limitations.