Identification of gene ontology and pathways implicated in suicide behavior: Systematic review and enrichment analysis of GWAS studies

González‐Castro, Thelma Beatriz; Tovilla‐Zárate, Carlos Alfonso; Genis‐Mendoza, Alma Delia; Juárez‐Rojop, Isela Esther; Nicolini, Humberto; López‐Narváez, María Lilia; Martínez‐Magaña, José Jaime

doi:10.1002/ajmg.b.32731

Cited by 24 publications

(23 citation statements)

References 82 publications

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“…To further analyze the potential biological process (BP), molecular function (MF), and cellular component (CC), the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the overlapping DEGs between two the groups was submitted to the online Database of Enrichr (http://amp.pharm.mssm.edu/ Enrichr/) to conduct functional and pathway enrichment analysis in this study. Enrichr is a useful online tool for annotating genes (25)(26)(27), which provides the functionality to perform simultaneous GO and KEGG analysis. P < 0.05 was considered to indicate a statistically significant difference.…”

Section: Gene Ontology and Kegg Pathway Analysismentioning

confidence: 99%

Identification of Key Genes and the Pathophysiology Associated With Major Depressive Disorder Patients Based on Integrated Bioinformatics Analysis

Zhang

et al. 2020

Front. Psychiatry

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Background: At present, laboratory blood tests to support major depressive disorder (MDD) diagnosis are not available. This study aimed to screen potential mRNAs for peripheral blood biomarkers and novel pathophysiology of MDD. Methods: The present study utilized public data from two mRNA microarray datasets to analyze the hub genes changes related to MDD. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of differentially expressed genes (DEGs) were performed. Finally, some potential mRNA quality biomarkers for hub gene expression in blood were identified. Results: A total of 25 significantly co-upregulated DEGs and 98 co-downregulated DEGs were obtained from two datasets. The pathway enrichment analyses showed that co-upregulated genes were significantly enriched in the regulation of cell-matrix adhesion and mitochondrial membrane permeability which were involved in the apoptotic process. Co-downregulated genes were mainly involved in the neutrophil activation which in turn was involved in the immune response, degranulation and cell-mediated immunity, positive regulation of immune response, the Toll-like receptor signaling pathway, and the NOD-like receptor signaling pathway. From the PPI network, 14 hub genes were obtained. Among them, the subnetworks of PLCG1, BCL2A1, TLR8, FADD, and TLR4 screened out from our study have been shown to play a role in immune and inflammation responses. Discussion: The potential molecular mechanisms that have been identified simultaneously include innate immunity, neuroinflammation, and neurotrophic factors for synapse function and development.

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Section: Gene Ontology and Kegg Pathway Analysismentioning

confidence: 99%

Identification of Key Genes and the Pathophysiology Associated With Major Depressive Disorder Patients Based on Integrated Bioinformatics Analysis

Zhang

et al. 2020

Front. Psychiatry

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“…GO and KEGG analysis of module core genes. GO and KEGG analysis are methods used to identify the function and pathways the genes of interest are involved in (15). Therefore, the genes identified in the modules following WGCNA were subsequently analyzed to identify the pathways they are enriched in.…”

Section: Identification Of Good Samples and Good Genes Inmentioning

confidence: 99%

Identification of 40S ribosomal protein S8 as a novel biomarker for alcohol‑associated hepatocellular carcinoma using weighted gene co‑expression network analysis

Sun²,

Lei³

et al. 2020

Oncol Rep

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Alcohol-associated hepatocellular carcinoma (HCC) is a subtype of HCC with poor prognosis. The present study aimed to identify key biomarkers for alcohol-associated HCC. The gene data profiles and corresponding clinical traits of patients with alcohol-associated HCC were downloaded from The Cancer Genome Atlas (TCGA) database. Firstly, good genes and good samples were identified, which were subsequently used to conduct weighted gene co-expression network analysis (WGCNA). Hub genes in the significant modules were selected following Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, and from constructing a protein-protein interaction (PPI) network. Real hub genes among hub genes were determined following progression, survival analysis and gene set enrichment analysis (GSEA), as well as reverse transcription-quantitative PCR and immunohistochemical staining of non-alcohol-and alcohol-associated HCC samples. In total, 64 good samples of alcohol-associated HCC with height score <160 were selected, from which 15,195 good genes were identified and used to conduct WGCNA; 8 gene co-expressed modules were identified using WGCNA, while 3 modules (including pink, magenta and turquoise) were significantly associated with Child-Pugh score, T-stage and body weight. Following GO and KEGG analysis and construction of the PPI network, a total of 30 hub genes were identified in the aforementioned 3 gene co-expressed modules, while 16 hub genes (including AURKB,

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“…pharm.mssm.edu/Enrichr/) to conduct functional and pathway enrichment analysis in this study. Enrichr is a useful online tool for annotating genes, [25][26][27] which provides the functionality to perform simultaneous GO and KEGG pathway analysis. P-values < 0.01 were considered to indicate significant differences.…”

Section: Functional Enrichment Analysis Of Genes In Three Key Modulesmentioning

confidence: 99%

<p>Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression</p>

Zhang¹,

Xu²,

Yuan³

et al. 2020

NDT

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Purpose: Despite advances in characterizing the neurobiology of emotional disorders, there is still a significant lack of scientific understanding of the pathophysiological mechanisms governing major depressive disorder (MDD). This study attempted to elucidate the molecular circuitry of MDD and to identify more potential genes associated with the pathogenesis of the disease. Patients and Methods: Microarray data from the GSE98793 dataset were downloaded from the NCBI Gene Expression Omnibus (GEO) database, including 128 patients with MDD and 64 healthy controls. Weighted gene coexpression network analysis (WGCNA) was performed to find modules of differentially expressed genes (DEGs) with high correlations followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses to obtain further biological insight into the top three key modules. The protein-protein interaction (PPI) network, the modules from the PPI network, and the gene annotation enrichment of modules were analyzed, as well. Results: We filtered 3276 genes that were considered significant DEGs for further WGCNA analysis. By performing WGCNA, we found that the turquoise, blue and brown functional modules were all strongly correlated with MDD development, including immune response, neutrophil degranulation, ribosome biogenesis, T cell activation, glycosaminoglycan biosynthetic process, and protein serine/threonine kinase activator activity. Hub genes were identified in the key functional modules that might have a role in the progression of MDD. Functional annotation showed that these modules primarily enriched such KEGG pathways as the TNF signaling pathway, T cell receptor signaling pathway, primary immunodeficiency, Th1, Th2 and Th17 cell differentiation, autophagy and RNA degradation and oxidative phosphorylation. These results suggest that these genes are closely related to autophagy and cellular immune function. Conclusion: The results of this study may help to elucidate the pathophysiology of MDD development at the molecular level and explore the potential molecular mechanisms for new interventional strategies.

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Identification of gene ontology and pathways implicated in suicide behavior: Systematic review and enrichment analysis of GWAS studies

Cited by 24 publications

References 82 publications

Identification of Key Genes and the Pathophysiology Associated With Major Depressive Disorder Patients Based on Integrated Bioinformatics Analysis

Identification of Key Genes and the Pathophysiology Associated With Major Depressive Disorder Patients Based on Integrated Bioinformatics Analysis

Identification of 40S ribosomal protein S8 as a novel biomarker for alcohol‑associated hepatocellular carcinoma using weighted gene co‑expression network analysis

<p>Weighted Gene Coexpression Network Analysis Identifies Specific Modules and Hub Genes Related to Major Depression</p>

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