scholarly journals Construction and Analysis of a Diagnostic Model Based on Differential Expression Genes in Patients With Major Depressive Disorder

2021 ◽  
Vol 12 ◽  
Author(s):  
Qing Long ◽  
Rui Wang ◽  
Maoyang Feng ◽  
Xinling Zhao ◽  
Yilin Liu ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Network Analysis ◽  
Depressive Disorder ◽  
Operating Characteristic ◽  
Diagnostic Value ◽  
Hub Genes ◽  
Major Depressive ◽  
Pathway Enrichment ◽  
Differential Expression Genes ◽  
Functional Pathway

Background: Major depressive disorder (MDD) is a common and severe psychiatric disorder with a heavy burden on the individual and society. However, the prevalence varies significantly owing to the lack of auxiliary diagnostic biomarkers. To identify the shared differential expression genes (DEGs) with potential diagnostic value in both the hippocampus and whole blood, a systematic and integrated bioinformatics analysis was carried out.Methods: Two datasets from the Gene Expression Omnibus database (GSE53987 and GSE98793) were downloaded and analyzed separately. A weighted gene co-expression network analysis was performed to construct the co-expression gene network of DEGs from GSE53987, and the most disease-related module was extracted. The shared DEGs from the module and GSE98793 were identified using a Venn diagram. Functional pathway prediction was used to identify the most disease-related DEGs. Finally, several DEGs were chosen, and their potential diagnostic value was determined by receiver operating characteristic curve analysis.Results: After weighted gene co-expression network analysis, the most MDD-related module (MEgrey) was identified, and 623 DEGs were extracted from this module. The intersection between MEgrey and GSE98793 was calculated, and 163 common DEGs were identified. The co-expression network of 163 DEGs from these was then reconstructed. All hub genes were identified based on the connective degree of the reconstructed co-expression network. Based on the results of functional pathway enrichment, 17 candidate hub genes were identified. Finally, logistic regression and receiver operating characteristic curves showed that three candidate hub genes (CEP350, SMAD5, and HSPG2) had relatively high auxiliary value in the diagnosis of MDD.Conclusion: Our results showed that the combination of CEP350, SMAD5, and HSPG2 has a relatively high diagnostic value for MDD. Pathway enrichment analysis also showed that these genes may play an important role in the pathogenesis of MDD. These results suggest a potentially important role for this gene combination in clinical practice.

scholarly journals Identification of diagnostic markers for major depressive disorder by cross-validation of data from whole blood samples

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7171 ◽  
Author(s):  
Huimei Wang ◽  
Mingwei Zhang ◽  
Qiqi Xie ◽  
Jin Yu ◽  
Yan Qi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Signaling Pathway ◽  
Diagnostic Value ◽  
Ppi Network ◽  
Hub Genes ◽  
Major Depressive ◽  
Blood Samples ◽  
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 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.

Identification of Crucial Genes and Diagnostic Value Analysis in Major Depressive Disorder Using Bioinformatics Analysis

2020 ◽  
Vol 23 ◽  
Author(s):  
Yao Gao ◽  
Huiliang Zhao ◽  
Teng Xu ◽  
Junsheng Tian ◽  
Xuemei Qin
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Signaling Pathway ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Major Depressive ◽  
Diagnostic Ability ◽  
Value Analysis

Aim and Objective:: Despite the prevalence and burden of major depressive disorder (MDD), our current understanding of the pathophysiology is still incomplete. Therefore, this paper aims to explore genes and evaluate their diagnostic ability in the pathogenesis of MDD. Methods:: Firstly, the expression profiles of mRNA and microRNA were downloaded from the gene expression database and analyzed by the GEO2R online tool to identify differentially expressed genes (DEGs) and differentially expressed microRNAs (DEMs). Then, the DAVID tool was used for functional enrichment analysis. Secondly, the comprehensive protein- protein interaction (PPI) network was analyzed using Cytoscape, and the network MCODE was applied to explore hub genes. Thirdly, the receiver operating characteristic (ROC) curve of the core gene was drawn to evaluate clinical diagnostic ability. Finally, mirecords was used to predict the target genes of DEMs. Results:: A total of 154 genes were identified as DEGs, and 14 microRNAs were identified as DEMs. Pathway enrichment analysis showed that DEGs were mainly involved in hematopoietic cell lineage, PI3K-Akt signaling pathway, cytokinecytokine receptor interaction, chemokine signaling pathway, and JAK-STAT signaling pathway. Three important modules are identified and selected by the MCODE clustering algorithm. The top 12 hub genes including CXCL16, CXCL1, GNB5, GNB4, OPRL1, SSTR2, IL7R, MYB, CSF1R, GSTM1, GSTM2, and GSTP1 were identified as important genes for subsequent analysis. Among these important hub genes, GSTM2, GNB4, GSTP1 and CXCL1 have good diagnostic ability. Finally, by combining these four genes, the diagnostic ability of MDD can be improved to 0.905, which is of great significance for the clinical diagnosis of MDD. Conclusion:: Our results indicate that GSTM2, GNB4, GSTP1 and CXCL1 have potential diagnostic markers and are of great significance in clinical research and diagnostic application of MDD. This result needs a large sample study to further confirm the pathogenesis of MDD.

Network Analysis of Depressive Symptomatology in Elderly Patients with Major Depressive Disorder

2021 ◽  
Vol 17 ◽  
Author(s):  
Seon-Cheol Park
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Symptoms ◽  
Network Analysis ◽  
Depressive Disorder ◽  
Elderly Patients ◽  
Rating Scale ◽  
Depressive Symptomatology ◽  
Depressive Mood ◽  
Major Depressive ◽  
Using Data

Background: A novel psychopathological approach is the application of network analysis, as it is proposed that symptoms and their interconnections constitute a disease itself, rather than simply being components or outcome factors of disease. Objective: Using data from the Clinical Research Center for Depression (CRESCEND) Study, this study examined depressive symptoms in elderly patients with major depressive disorder using a network analysis approach. Methods: Among 135 elderly patients with major depressive disorder who were recruited from the CRESCEND study, we created a network based on individual items from the Hamilton Depression Rating Scale (HAMD), with the nodes being each item (symptom) and the edges being the strength of the association between the items (interconnection). By calculating measures of centrality of each of the nodes, we were able to determine which depressive symptoms were most central (influential) in the network. Results: The insight item was completely unconnected with other items and it was excluded in terms of network analysis. Thus, a network analysis of the 16 HAMD items estimated that the anxiety psychic item was the most central domain, followed by insomnia (middle of the night), depressive mood, and insomnia (early hours of the morning) items. On the contrary, the retardation item was the most poorly interconnected with the network. Conclusion: We suggest that our study makes a significant contribution to the literature because we have found that anxiety, depressed mood, and insomnia are most central to the network, indicating that they are the most influential symptoms in major depression in elderly individuals.

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