scholarly journals Identification of major depressive disorder disease-related genes and functional pathways based on system dynamic changes of network connectivity

2020 ◽  
Author(s):  
Ruijie Geng ◽  
Xiao Huang
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Pathway Analysis ◽  
Healthy Subjects ◽  
Kegg Pathway ◽  
Network Connectivity ◽  
Differentially Expressed ◽  
Therapeutic Drug ◽  
Major Depressive ◽  
Kegg Pathway Analysis

Abstract Objective: Major depressive disorder (MDD) is a leading psychiatric disorder that involves complex abnormal biological functions and neural networks. This study aimed to compare the changes in the network connectivity of different brain tissues under different pathological conditions, analyzed the biological pathways and genes that are significantly related to disease progression, and further predicted the potential therapeutic drug targets.Methods: Expression of differentially expressed genes (DEGs) were analyzed with postmortem cingulate cortex (ACC) and prefrontal cortex (PFC) mRNA expression profile datasets downloaded from the Gene Expression Omnibus (GEO) database, including 76 MDD patients and 76 healthy subjects in ACC and 63 MDD patients and 63 healthy subjects in PFC. The co-expression network construction was based on system network analysis. The function of the genes was annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Human Protein Reference Database (HPRD, http://www.hprd.org/) was used for gene interaction relationship mapping.Results: We filtered 586 DEGs in ACC and 616 DEGs in PFC for further analysis. By constructing the Co-expression network, we found that the gene connectivity was significantly reduced under disease conditions (P=0.04 in PFC and P=1.227e-09 in ACC). Crosstalk analysis showed that CD19, PTDSS2 and NDST2 were significantly differentially expressed in ACC and PFC of MDD patients. Among them, CD19 and PTDSS2 have been targeted by several drugs in the Drugbank database. KEGG pathway analysis demonstrated that the function of CD19 and PTDSS2 were enriched with the pathway of Glycerophospholipid metabolism and T cell receptor signaling pathway. Conclusion: Co-expression network and tissue comparing analysis can identify signaling pathways and cross talk genes related to MDD, which may provide novel insight for understanding the molecular mechanisms of MDD.

scholarly journals Identification of major depressive disorder disease-related genes and functional pathways based on system dynamic changes of network connectivity

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Ruijie Geng ◽  
Xiao Huang
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Pathway Analysis ◽  
Healthy Subjects ◽  
Kegg Pathway ◽  
Network Connectivity ◽  
Differentially Expressed ◽  
Therapeutic Drug ◽  
Major Depressive ◽  
Kegg Pathway Analysis

Abstract Background Major depressive disorder (MDD) is a leading psychiatric disorder that involves complex abnormal biological functions and neural networks. This study aimed to compare the changes in the network connectivity of different brain tissues under different pathological conditions, analyzed the biological pathways and genes that are significantly related to disease progression, and further predicted the potential therapeutic drug targets. Methods Expression of differentially expressed genes (DEGs) were analyzed with postmortem cingulate cortex (ACC) and prefrontal cortex (PFC) mRNA expression profile datasets downloaded from the Gene Expression Omnibus (GEO) database, including 76 MDD patients and 76 healthy subjects in ACC and 63 MDD patients and 63 healthy subjects in PFC. The co-expression network construction was based on system network analysis. The function of the genes was annotated by Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Human Protein Reference Database (HPRD, http://www.hprd.org/) was used for gene interaction relationship mapping. Results We filtered 586 DEGs in ACC and 616 DEGs in PFC for further analysis. By constructing the co-expression network, we found that the gene connectivity was significantly reduced under disease conditions (P = 0.04 in PFC and P = 1.227e−09 in ACC). Crosstalk analysis showed that CD19, PTDSS2 and NDST2 were significantly differentially expressed in ACC and PFC of MDD patients. Among them, CD19 and PTDSS2 have been targeted by several drugs in the Drugbank database. KEGG pathway analysis demonstrated that the function of CD19 and PTDSS2 were enriched with the pathway of Glycerophospholipid metabolism and T cell receptor signaling pathway. Conclusion Co-expression network and tissue comparing analysis can identify signaling pathways and cross talk genes related to MDD, which may provide novel insight for understanding the molecular mechanisms of MDD.

scholarly journals Identification of Core Genes and Pathways in Major Depressive Disorder Applying Bioinformatics

2020 ◽  
Author(s):  
Wenshan Yang ◽  
Hong Yin ◽  
Yichen Wang ◽  
Ping Liu ◽  
Yuan Hu
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Signaling Pathway ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Ppi Network ◽  
Biological Mechanisms ◽  
Major Depressive ◽  
Kegg Pathway Analysis ◽  
Depressive Patients

Abstract Background: Although extensive study efforts on major depressive disorder (MDD), the pathogenesis related to the biological factors are not fully understood and present therapeutic regimen are ineffective in some depressive patients. This study aims to identify key genes and pathways associated with the molecular biological mechanisms of major depressive disorder through bioinformatics analysis in the Gene Expression Omnibus (GEO) public database of the National Center for Biotechnology Information (NCBI) website.Materials and methods: The whole-transcriptome brain expression profile dataset (GSE101521) was obtained from the GEO database. Differentially-expressed genes (DEGs) in normal group (non-psychiatric human) and MDD group (depressive patients) were identified applying Networkanalyst online database. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to function annotation and enrichment analysis. After that, STRING online database was conducted to protein–protein interaction (PPI) network, and Cytoscape.3.7.2 software was performed to module analysis. Results: Out of the 41 DEGs identified from normal tissue samples and MDD, 39 were upregulated and 2 were downregulated. GO enrichment analysis discovered that DEGs were primarily involved in inflammatory response, and KEGG pathway analysis suggested that the most chiefly pathway related to MDD were IL-17 signaling pathway, TNF signaling pathway and NOD-like receptor signaling pathway. Six hub genes (IL6, CXCL8, IL1B, FOS, CCL2 and CXCL2) were identified by PPI network and module analysis. Conclusion: Our current study detected novel markers and targets involved immune system, which are involved in pivotal biological mechanisms related to the pathogenesis of major depression. Looking forward, these findings still need to be validated in future experimental studies.

Higher order thalamic nuclei resting network connectivity in early schizophrenia and major depressive disorder

2018 ◽  
Vol 272 ◽  
pp. 7-16 ◽  
Author(s):  
Jacob Penner ◽  
Elizabeth A. Osuch ◽  
Betsy Schaefer ◽  
Jean Théberge ◽  
Richard W.J. Neufeld ◽  
...  
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Network Connectivity ◽  
Higher Order ◽  
Thalamic Nuclei ◽  
Major Depressive ◽  
Early Schizophrenia

Prolactin in patients with major depressive disorder and in healthy subjects

1988 ◽  
Vol 24 (3) ◽  
pp. 286-298 ◽  
Author(s):  
Andreas Baumgartner ◽  
Klaus-jürgen Gräf ◽  
Irene Kürten
Keyword(s):  
Major Depressive Disorder ◽  
Depressive Disorder ◽  
Healthy Subjects ◽  
Major Depressive

scholarly journals Bioinformatic Analysis of Circular RNA-Associated ceRNA Network Associated with Hepatocellular Carcinoma

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Jiacheng Wu ◽  
Shui Liu ◽  
Yien Xiang ◽  
Xianzhi Qu ◽  
Yingjun Xie ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Pathway Analysis ◽  
Target Genes ◽  
Kegg Pathway ◽  
Interaction Network ◽  
Bioinformatic Analysis ◽  
Differentially Expressed ◽  
Qrt Pcr ◽  
Cerna Network ◽  
Kegg Pathway Analysis

Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide and is associated with a high mortality rate and poor treatment efficacy. In an attempt to investigate the mechanisms involved in the pathogenesis of HCC, bioinformatic analysis and validation by qRT-PCR were performed. Three circRNA GEO datasets and one miRNA GEO dataset were selected for this purpose. Upon combined biological prediction, a total of 11 differentially expressed circRNAs, 15 differentially expressed miRNAs, and 560 target genes were screened to construct a circRNA-related ceRNA network. GO analysis and KEGG pathway analysis were performed for the 560 target genes. To further screen key genes, a protein-protein interaction network of the target genes was constructed using STRING, and the genes and modules with higher degree were identified by MCODE and CytoHubba plugins of Cytoscape. Subsequently, a module was screened out and subjected to GO enrichment analysis and KEGG pathway analysis. This module included eight genes, which were further screened using TCGA. Finally, UBE2L3 was selected as a key gene and the hsa_circ_0009910–miR-1261–UBE2L3 regulatory axis was established. The relative expression of the regulatory axis members was confirmed by qRT-PCR in 30 pairs of samples, including HCC tissues and adjacent nontumor tissues. The results suggested that hsa_circ_0009910, which was upregulated in HCC tissues, participates in the pathogenesis of HCC by acting as a sponge of miR-1261 to regulate the expression of UBE2L3. Overall, this study provides support for the possible mechanisms of progression in HCC.

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