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通过加权基因共表达网络分析构建共表达模块并识别产后抑郁症的潜在枢纽基因和调控通路。

Co-expression modules construction by WGCNA and identify potential hub genes and regulation pathways of postpartum depression.

作者信息

Deng Zhifang, Cai Wei, Liu Jue, Deng Aiping, Yang Yuan, Tu Jie, Yuan Cheng, Xiao Han, Gao Wenqi

机构信息

Department of Pharmacy, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, 430030 Wuhan, Hubei, China.

Department of Hematology, Wuhan Children's Hospital (Wuhan Maternal and Child Healthcare Hospital), Tongji Medical College, Huazhong University and Technology, 430015 Wuhan, Hubei, China.

出版信息

Front Biosci (Landmark Ed). 2021 Nov 30;26(11):1019-1030. doi: 10.52586/5006.

Abstract

: The purpose of our present study was to, for the first time, identify key genes associated with postpartum depression (PPD) and discovery the potential molecular mechanisms of this condition. : First, microarray expression profiles GSE45603 dataset were acquired from the Gene Expression Omnibus (GEO) in National Center for Biotechnology Information (NCBI). The weighted gene co-expression network analysis (WGCNA) was performed to identify the top three modules from differentially expressed genes (DEGs). Furthermore, cross-validated differential gene expression analysis of the top three modules and DEGs was used to identify the hub genes. Gene set enrichment analysis (GSEA) was conducted to identify the potential functions of the hub genes. We conducted a Receiver Operator Characteristic (ROC) curve to verify the diagnostic efficiencies of the hub genes. Lastly, GSE44132 dataset was used to search the association between the methylation profiles of the hub genes and susceptibility to PPD. : Altogether, 8979 genes were identified as DEGs for WGCNA analysis. The turquoise, yellow, and green functional modules were the most significant modules related to PPD development after WGCNA analysis. The enrichment analysis results of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway demonstrated that hub genes in the three modules were mainly enriched in the neurotrophin signaling pathway, chemokine signaling pathway, Fcγ receptor-mediated phagocytosis, and Mitogen-activated protein kinase (MAPK) signaling pathway. Eight genes (HNRNPA2B1, IL10, RAD51, UBA52, NHP2, RPL13A, FBL, SPI1) were identified as "real" hub genes from cross-validation data of the three modules and DEGs, and possessed diagnostic value in PPD. The GSEA suggested that "OLFACTORY_TRANSDUCTION", "BUTANOATE_METABOLISM", "MELANOMA", "AMINOACYL_TRNA_BIOSYNTHESIS", and "LYSINE_DEGRADATION" were all crucial in the development of PPD. Highly significant differentially methylated positions in the three genes (HNRNPA2B1, RPL13A and UBA52) were identified in the GSE44132. : Using WGCNA analysis of GEO data, our present study, for the first time, may contribute to elucidate the pathophysiology of PPD and provide potential diagnostic biomarkers and therapeutic targets for postpartum depression.

摘要

本研究的目的是首次鉴定与产后抑郁症(PPD)相关的关键基因,并揭示该疾病潜在的分子机制。

首先,从美国国立生物技术信息中心(NCBI)的基因表达综合数据库(GEO)中获取微阵列表达谱GSE45603数据集。采用加权基因共表达网络分析(WGCNA)从差异表达基因(DEG)中识别出前三个模块。此外,对前三个模块和DEG进行交叉验证的差异基因表达分析以识别枢纽基因。进行基因集富集分析(GSEA)以确定枢纽基因的潜在功能。我们绘制了受试者工作特征(ROC)曲线以验证枢纽基因的诊断效率。最后,使用GSE44132数据集研究枢纽基因的甲基化谱与PPD易感性之间的关联。

总共鉴定出8979个基因作为用于WGCNA分析的DEG。在WGCNA分析后,绿松石色、黄色和绿色功能模块是与PPD发展最相关的模块。京都基因与基因组百科全书(KEGG)通路的富集分析结果表明,这三个模块中的枢纽基因主要富集在神经营养因子信号通路、趋化因子信号通路、Fcγ受体介导的吞噬作用和丝裂原活化蛋白激酶(MAPK)信号通路中。从三个模块和DEG的交叉验证数据中鉴定出八个基因(HNRNPA2B1、IL10、RAD51、UBA52、NHP2、RPL13A、FBL、SPI1)为“真正的”枢纽基因,并且在PPD中具有诊断价值。GSEA表明“嗅觉转导”、“丁酸代谢”、“黑色素瘤”、“氨酰-tRNA生物合成”和“赖氨酸降解”在PPD的发展中都至关重要。在GSE44132中鉴定出三个基因(HNRNPA2B1、RPL13A和UBA52)中高度显著的差异甲基化位点。

通过对GEO数据进行WGCNA分析,本研究首次可能有助于阐明PPD的病理生理学,并为产后抑郁症提供潜在的诊断生物标志物和治疗靶点。

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