Zhang Chi, Cheng Min, Dong Naifu, Sun Dongjie, Ma Haichun
Department of Anesthesiology, The First Hospital of Jilin University, Changchun, China.
College of Basic Medical Sciences, Jilin University, Changchun, China.
Front Aging Neurosci. 2022 May 27;14:918217. doi: 10.3389/fnagi.2022.918217. eCollection 2022.
Depression currently affects 4% of the world's population; it is associated with disability in 11% of the global population. Moreover, there are limited resources to treat depression effectively. Therefore, we aimed to identify a promising novel therapeutic target for depression using bioinformatic analysis. The GSE54568, GSE54570, GSE87610, and GSE92538 gene expression data profiles were retrieved from the Gene Expression Omnibus (GEO) database. We prepared the four GEO profiles for differential analysis, protein-protein interaction (PPI) network construction, and weighted gene co-expression network analysis (WGCNA). Gene Ontology functional enrichment and Kyoto Encyclopedia of Genes and Genomes metabolic pathway analyses were conducted to determine the key functions of the corresponding genes. Additionally, we performed correlation analyses of the hub genes with transcription factors, immune genes, and N6-methyladenosine (m6A) genes to reveal the functional landscape of the core genes associated with depression. Compared with the control samples, the depression samples contained 110 differentially expressed genes (DEGs), which comprised 56 downregulated and 54 upregulated DEGs. Moreover, using the WGCNA and PPI clustering analysis, the blue module and cluster 1 were found to be significantly correlated with depression. was the only common gene identified using the differential analysis and WGCNA; thus, it was used as the hub gene. According to the enrichment analyses, GTF2F2 was predominantly involved in the cell cycle and JAK-STAT, PI3K-Akt, and p53 signaling pathways. Furthermore, differential and correlation analyses revealed that 9 transcription factors, 12 immune genes, and 2 m6A genes were associated with GTF2F2 in depression samples. GTF2F2 may serve as a promising diagnostic biomarker and treatment target of depression, and this study provides a novel perspective and valuable information to explore the molecular mechanism of depression.
抑郁症目前影响着全球4%的人口；在全球11%的人口中，抑郁症与残疾有关。此外，有效治疗抑郁症的资源有限。因此，我们旨在通过生物信息学分析确定一个有前景的抑郁症新治疗靶点。从基因表达综合数据库（GEO）中检索了GSE54568、GSE54570、GSE87610和GSE92538基因表达数据概况。我们准备了这四个GEO概况用于差异分析、蛋白质-蛋白质相互作用（PPI）网络构建和加权基因共表达网络分析（WGCNA）。进行基因本体功能富集分析和京都基因与基因组百科全书代谢途径分析，以确定相应基因的关键功能。此外，我们对枢纽基因与转录因子、免疫基因和N6-甲基腺苷（m6A）基因进行了相关性分析，以揭示与抑郁症相关的核心基因的功能格局。与对照样本相比，抑郁症样本包含110个差异表达基因（DEG），其中56个DEG下调，54个DEG上调。此外，通过WGCNA和PPI聚类分析，发现蓝色模块和聚类1与抑郁症显著相关。GTF2F2是通过差异分析和WGCNA鉴定出的唯一共同基因；因此，它被用作枢纽基因。根据富集分析，GTF2F2主要参与细胞周期以及JAK-STAT、PI3K-Akt和p53信号通路。此外，差异分析和相关性分析表明，在抑郁症样本中，9个转录因子、12个免疫基因和2个m6A基因与GTF2F2相关。GTF2F2可能是一个有前景的抑郁症诊断生物标志物和治疗靶点，本研究为探索抑郁症的分子机制提供了新的视角和有价值的信息。
