Identification of diagnostic markers for major depressive disorder by cross-validation of data from whole blood samples
Background 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. Methods...
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doaj-c13f1259503e4528832ecff7dff77f242020-11-25T01:40:12ZengPeerJ Inc.PeerJ2167-83592019-06-017e717110.7717/peerj.7171Identification of diagnostic markers for major depressive disorder by cross-validation of data from whole blood samplesHuimei Wang0Mingwei Zhang1Qiqi Xie2Jin Yu3Yan Qi4Qiuyuan Yue5Department 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, ChinaDepartment of Radiation Oncology, First Affiliated Hospital of Fujian Medical University, Fujian, Fuzhou, ChinaDepartment of Orthopaedics, Second Hospital of Lanzhou University, Lanzhou, Gansu, ChinaDepartment 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, ChinaYunnan Provincial Key Laboratory of Traditional Chinese Medicine Clinical Research, First Affiliated Hospital of Yunnan University of Traditional Chinese Medicine, Yunnan, Kunming, ChinaDepartment of Radiology, Fujian Cancer Hospital & Fujian Medical University Cancer Hospital, Fujian, Fuzhou, ChinaBackground 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. Methods 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. Results 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. Conclusions 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.https://peerj.com/articles/7171.pdfDifferentially expressed geneMajor depressive disorderInflammationCorrelation network analysisMitochondrial dysfunctionDiagnostic value |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Huimei Wang Mingwei Zhang Qiqi Xie Jin Yu Yan Qi Qiuyuan Yue |
spellingShingle |
Huimei Wang Mingwei Zhang Qiqi Xie Jin Yu Yan Qi Qiuyuan Yue Identification of diagnostic markers for major depressive disorder by cross-validation of data from whole blood samples PeerJ Differentially expressed gene Major depressive disorder Inflammation Correlation network analysis Mitochondrial dysfunction Diagnostic value |
author_facet |
Huimei Wang Mingwei Zhang Qiqi Xie Jin Yu Yan Qi Qiuyuan Yue |
author_sort |
Huimei Wang |
title |
Identification of diagnostic markers for major depressive disorder by cross-validation of data from whole blood samples |
title_short |
Identification of diagnostic markers for major depressive disorder by cross-validation of data from whole blood samples |
title_full |
Identification of diagnostic markers for major depressive disorder by cross-validation of data from whole blood samples |
title_fullStr |
Identification of diagnostic markers for major depressive disorder by cross-validation of data from whole blood samples |
title_full_unstemmed |
Identification of diagnostic markers for major depressive disorder by cross-validation of data from whole blood samples |
title_sort |
identification of diagnostic markers for major depressive disorder by cross-validation of data from whole blood samples |
publisher |
PeerJ Inc. |
series |
PeerJ |
issn |
2167-8359 |
publishDate |
2019-06-01 |
description |
Background 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. Methods 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. Results 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. Conclusions 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. |
topic |
Differentially expressed gene Major depressive disorder Inflammation Correlation network analysis Mitochondrial dysfunction Diagnostic value |
url |
https://peerj.com/articles/7171.pdf |
work_keys_str_mv |
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