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首页> 外文期刊>Neuropsychiatric Disease and Treatment >Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression
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Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression

机译：加权基因共同表达网络分析识别与主要抑郁有关的特定模块和轮毂基因

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Purpose: Despite advances in characterizing the neurobiology of emotional disorders, there is still a significant lack of scientific understanding of the pathophysiological mechanisms governing major depressive disorder (MDD). This study attempted to elucidate the molecular circuitry of MDD and to identify more potential genes associated with the pathogenesis of the disease. Patients and Methods: Microarray data from the GSE98793 dataset were downloaded from the NCBI Gene Expression Omnibus (GEO) database, including 128 patients with MDD and 64 healthy controls. Weighted gene coexpression network analysis (WGCNA) was performed to find modules of differentially expressed genes (DEGs) with high correlations followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to obtain further biological insight into the top three key modules. The protein-protein interaction (PPI) network, the modules from the PPI network, and the gene annotation enrichment of modules were analyzed, as well. Results: We filtered 3276 genes that were considered significant DEGs for further WGCNA analysis. By performing WGCNA, we found that the turquoise, blue and brown functional modules were all strongly correlated with MDD development, including immune response, neutrophil degranulation, ribosome biogenesis, T cell activation, glycosaminoglycan biosynthetic process, and protein serine/threonine kinase activator activity. Hub genes were identified in the key functional modules that might have a role in the progression of MDD. Functional annotation showed that these modules primarily enriched such KEGG pathways as the TNF signaling pathway, T cell receptor signaling pathway, primary immunodeficiency, Th1, Th2 and Th17 cell differentiation, autophagy and RNA degradation and oxidative phosphorylation. These results suggest that these genes are closely related to autophagy and cellular immune function. Conclusion: The results of this study may help to elucidate the pathophysiology of MDD development at the molecular level and explore the potential molecular mechanisms for new interventional strategies.
机译：目的：尽管表征情绪障碍的神经生物学进行了进展，但仍然存在对治疗重大抑郁症（MDD）的病理生理机制的科学理解。该研究试图阐明MDD的分子电路，并鉴定与疾病发病机制相关的更多潜在基因。患者和方法：从GSE98793数据集中下载来自GSE98793数据集的微阵列数据，包括128例MDD和64例健康对照。进行加权基因共抑制网络分析（WGCNA）以找到具有高相关性的差异表达基因（DEG）的模块，然后是基因本体（GO）和基因组（KEGG）途径富集分析，以获得进一步的生物洞察前三个关键模块。分析了蛋白质 - 蛋白质相互作用（PPI）网络，来自PPI网络的模块，以及模块的基因注释富集的基因。结果：我们过滤了3276个基因，被认为是进一步的WGCNA分析的显着参数。通过执行WGCNA，我们发现绿松石，蓝色和棕色功能模块都与MDD发育强烈相关，包括免疫应答，中性粒细胞脱粒，核糖体生物发生，T细胞活化，糖胺聚糖生物合成过程和蛋白质丝氨酸/苏氨酸激酶活化剂活性。在关键功能模块中鉴定了集线器基因，其在MDD的进展中可能具有作用。功能注释显示，这些模块主要富集为TNF信号通路，T细胞受体信号通路，一次免疫缺陷，TH1，TH2和TH17细胞分化，自噬和RNA降解和氧化磷酸化。这些结果表明这些基因与自噬和细胞免疫功能密切相关。结论：本研究的结果可能有助于阐明分子水平的MDD发育的病理生理学，并探讨新的介入策略的潜在分子机制。

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