机构：[1]Department of Integrative Medicine and Neurobiology, State Key Laboratory of Medical Neurobiology, Institute of Brain Science, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China.[2]Department of Radiation Oncology, First Affiliated Hospital of Fujian Medical University, Fujian, Fuzhou, China.[3]Department of Orthopaedics, Second Hospital of Lanzhou University, Lanzhou, Gansu, China.[4]Yunnan Provincial Key Laboratory of Traditional Chinese Medicine Clinical Research, First Affiliated Hospital of Yunnan University of Traditional Chinese Medicine, Yunnan, Kunming, China.[5]Department of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fujian, Fuzhou, China.
Major depressive disorder (MDD) is a severe disease characterized by multiple pathological changes. However, there are no reliable diagnostic biomarkers for MDD. The aim of the current study was to investigate the gene network and biomarkers underlying the pathophysiology of MDD.
In this study, we conducted a comprehensive analysis of the mRNA expression profile of MDD using data from Gene Expression Omnibus (GEO). The MDD dataset (GSE98793) with 128 MDD and 64 control whole blood samples was divided randomly into two non-overlapping groups for cross-validated differential gene expression analysis. The gene ontology (GO) enrichment and gene set enrichment analysis (GSEA) were performed for annotation, visualization, and integrated discovery. Protein-protein interaction (PPI) network was constructed by STRING database and hub genes were identified by the CytoHubba plugin. The gene expression difference and the functional similarity of hub genes were investigated for further gene expression and function exploration. Moreover, the receiver operating characteristic curve was performed to verify the diagnostic value of the hub genes.
We identified 761 differentially expressed genes closely related to MDD. The Venn diagram and GO analyses indicated that changes in MDD are mainly enriched in ribonucleoprotein complex biogenesis, antigen receptor-mediated signaling pathway, catalytic activity (acting on RNA), structural constituent of ribosome, mitochondrial matrix, and mitochondrial protein complex. The GSEA suggested that tumor necrosis factor signaling pathway, Toll-like receptor signaling pathway, apoptosis pathway, and NF-kappa B signaling pathway are all crucial in the development of MDD. A total of 20 hub genes were selected via the PPI network. Additionally, the identified hub genes were downregulated and show high functional similarity and diagnostic value in MDD.
Our findings may provide novel insight into the functional characteristics of MDD through integrative analysis of GEO data, and suggest potential biomarkers and therapeutic targets for MDD.
基金：
Natural Science Foundation of Fujian
Province of China (Grant No. 2018J01169); the Scientific Research Personnel Training
Project of Health and Family Planning Commission of Fujian Province (Grant No. 2017-
02-03); the Science Foundation for Young Scientists of Fujian Health and Family Planning
Commission (Grant No. 2018-2-17); and the Key R & D programs in Shandong
(2016CYJS08A01-1).
语种：
外文
PubmedID：
中科院(CAS)分区：
出版当年[2019]版：
大类|3 区生物
小类|3 区综合性期刊
最新[2023]版：
大类|3 区生物学
小类|3 区综合性期刊
第一作者：
第一作者机构：[1]Department of Integrative Medicine and Neurobiology, State Key Laboratory of Medical Neurobiology, Institute of Brain Science, School of Basic Medical Sciences, Shanghai Medical College, Fudan University, Shanghai, China.
共同第一作者：
通讯作者：
推荐引用方式(GB/T 7714)：
Wang Huimei,Zhang Mingwei,Xie Qiqi,et al.Identification of diagnostic markers for major depressive disorder by cross-validation of data from whole blood samples.[J].PeerJ.2019,7:e7171.doi:10.7717/peerj.7171.
APA：
Wang Huimei,Zhang Mingwei,Xie Qiqi,Yu Jin,Qi Yan&Yue Qiuyuan.(2019).Identification of diagnostic markers for major depressive disorder by cross-validation of data from whole blood samples..PeerJ,7,
MLA：
Wang Huimei,et al."Identification of diagnostic markers for major depressive disorder by cross-validation of data from whole blood samples.".PeerJ 7.(2019):e7171
