Identification of Key Genes and Signaling Pathways in Male Androgenetic Alopecia by Bioinformatics Analysis

2020 ◽  
Author(s):  
Mengzhi Zhang ◽  
Yujia Chen ◽  
Liancheng Guan ◽  
Wen Li ◽  
Yunzhi Chen
Keyword(s):  
Signaling Pathways ◽  
Bioinformatics Analysis ◽  
Androgenetic Alopecia ◽  
Key Genes

scholarly journals Identification of Differentially Expressed Genes for Disc Degeneration Using Gene Expression Profiling and Bioinformatics Analysis

2021 ◽  
Author(s):  
Ning Fan ◽  
Shuo Yuan ◽  
Yong Hai ◽  
Peng Du ◽  
Jian Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Intervertebral Disc ◽  
Signaling Pathways ◽  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Disc Degeneration ◽  
Bioinformatics Analysis ◽  
Differentially Expressed ◽  
Key Genes

Abstract BackgroundInflammatory processes exacerbated by IL-1β are believed to be key mediators of disc degeneration and low back pain. However, the underlying mechanism remains unclear. We performed a bioinformatics analysis to identify the key genes that were differentially expressed between degenerative intervertebral disc cells with and without exposure to interleukin (IL)-1β, and explore the related signaling pathways and interaction networks.MethodsThe microarray data were downloaded from the Gene Expression Omnibus (GSE 27494). Then, analyses of the gene ontology, signaling pathways, and interaction networks for the differentially expressed genes (DEGs) were conducted using tools including the Database for Annotation, Visualization, and Integrated Discovery (DAVID), Metascape, Gene Set Enrichment Analysis (GSEA), Search Tool for the Retrieval of Interacting Genes (STRING), Cytoscape, the Venn method, and packages of the R computing language.ResultsA total of 260 DEGs were identified, including 161 upregulated genes and 99 down-regulated genes. Gene Ontology (GO) annotation analysis showed that these DEGs were mainly associated with the extracellular region, chemotaxis, taxis, cytokine activity, and cytokine receptor binding. A Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis showed that these DEGs were mainly involved in the interactions of cytokine-cytokine receptor interaction, rheumatoid arthritis, tumor necrosis factor (TNF) signaling pathway, salmonella infection, and chemokine signaling pathway. The interaction network analysis indicated that 10 hub genes, including CXCL8, CXCL1, CCL20, CXCL2, CXCL5, CXCL3, CXCL6, C3, PF4, and GPER1 may play key roles in intervertebral disc degeneration.ConclusionsBioinformatic analysis showed that CXCL8 and other 9 key genes may play a role in the development of disc degeneration induced by inflammatory reactions, and can be used to identify the potential therapeutic target genes.

scholarly journals Identification of potential key genes for SARS-CoV-2 infected human bronchial organoids based on bioinformatics analysis

2020 ◽  
Author(s):  
Hanming Gu ◽  
Gongsheng Yuan
Keyword(s):  
Signaling Pathways ◽  
Bioinformatics Analysis ◽  
Homo Sapiens ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Receptor Interaction ◽  
Functional Categories ◽  
P53 Signaling ◽  
Key Genes ◽  
P53 Signaling Pathway

AbstractThere is an urgent need to understand the pathogenesis of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) that leads to COVID-19 and respiratory failure. Our study is to discover differentially expressed genes (DEGs) and biological signaling pathways by using a bioinformatics approach to elucidate their potential pathogenesis. The gene expression profiles of the GSE150819 datasets were originally produced using an Illumina NextSeq 500 (Homo sapiens). KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) were utilized to identify functional categories and significant pathways. KEGG and GO results suggested that the Cytokine-cytokine receptor interaction, P53 signaling pathway, and Apoptosis are the main signaling pathways in SARS-CoV-2 infected human bronchial organoids (hBOs). Furthermore, NFKBIA, C3, and CCL20 may be key genes in SARS-CoV-2 infected hBOs. Therefore, our study provides further insights into the therapy of COVID-19.

scholarly journals Identification of key genes and pathways associated with Crohn’s disease by bioinformatics analysis

2019 ◽  
Author(s):  
Zheng Wang ◽  
Jie Zhu ◽  
Lixian Ma
Keyword(s):  
Gene Expression ◽  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Signaling Pathways ◽  
Bioinformatics Analysis ◽  
Integrated Analysis ◽  
Functional Modules ◽  
Disease Pathogenesis ◽  
Black Module ◽  
Key Genes

AbstractCrohn’s disease is a type of inflammatory bowel disease posing a significant threat to human health all over the world. Genome-wide gene expression profiles of mucosal colonic biopsies have provided some insight into the molecular mechanisms of Crohn’s disease. However, the exact pathogenesis is unclear. This study aimed to identify key genes and significant signaling pathways associated with Crohn’s disease by bioinformatics analysis. To identify key genes, an integrated analysis of gene expression signature was conducted using a robust rank aggregation approach. A total of 179 Crohn’s disease patients and 94 healthy controls from nine public microarray datasets were included. MMP1 and CLDN8 were two key genes screened from the differentially expressed genes. Connectivity Map predicted several small molecules as possible adjuvant drugs to treat CD. Besides, we used weighted gene co-expression network analysis to explore the co-expression modules associated with Crohn’s disease pathogenesis. Seven main functional modules were identified, of which black module showed the highest correlation with Crohn’s disease. The genes in black module mainly enriched in Interferon Signaling and defense response to virus. Blue module was another important module and enriched in several signaling pathways, including extracellular matrix organization, inflammatory response and blood vessel development. There were also several other meaningful functional modules which enriched in many biological processes. The present study identified a number of key genes and pathways correlated with Crohn’s disease and potential drugs to combat it, which might offer insights into Crohn’s disease pathogenesis and provide a clue to potential treatments.

Identifying the potential role of IL-1β in molecular mechanisms of disc degeneration using gene expression profiling and bioinformatics analysis

2021 ◽  
Author(s):  
Ning Fan ◽  
Shuo Yuan ◽  
Yong Hai ◽  
Peng Du ◽  
Jian Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Intervertebral Disc ◽  
Signaling Pathways ◽  
Signaling Pathway ◽  
Disc Degeneration ◽  
Bioinformatics Analysis ◽  
Interaction Networks ◽  
Differentially Expressed ◽  
Key Genes

Abstract Background: Inflammatory processes exacerbated by IL-1β are believed to be key mediators of disc degeneration and low back pain. However, the underlying mechanism remains unclear. We performed a bioinformatics analysis to identify the key genes that were differentially expressed between degenerative intervertebral disc cells with and without exposure to interleukin (IL)-1β, and explore the related signaling pathways and interaction networks.Methods: The microarray data were downloaded from the Gene Expression Omnibus (GSE 27494). Then, analyses of the gene ontology, signaling pathways, and interaction networks for the differentially expressed genes (DEGs) were conducted using tools including the Database for Annotation, Visualization, and Integrated Discovery (DAVID), Metascape, Gene Set Enrichment Analysis (GSEA),Search Tool for the Retrieval of Interacting Genes (STRING), Cytoscape, the Venn method, and packages of the R computing language.Results: A total of 260 DEGs were identified, including 161 upregulated genes and 99 down-regulated genes. Gene Ontology (GO) annotation analysis showed that these DEGs were mainly associated with the extracellular region, chemotaxis, taxis, cytokine activity, and cytokine receptor binding. A Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis showed that these DEGs were mainly involved in the interactions of cytokine-cytokine receptor interaction, rheumatoid arthritis, tumor necrosis factor (TNF) signaling pathway, salmonella infection, and chemokine signaling pathway. The interaction network analysis indicated that 10 hub genes, including CXCL8, CXCL1, CCL20, CXCL2, CXCL5, CXCL3, CXCL6, C3, PF4, and GPER1 may play key roles in intervertebral disc degeneration.Conclusions: Bioinformatic analysis showed that CXCL8 and other 9 key genes may play a role in the development of disc degeneration induced by inflammatory reactions, and can be used to identify the potential therapeutic target genes.

scholarly journals AB0005 IDENTIFICATION OF KEY GENES AND PATHWAYS FOR PSORIASIS BASED ON GEO DATABASES BY BIOINFORMATICS ANALYSIS

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 1037.2-1038
Author(s):  
X. Sun ◽  
S. X. Zhang ◽  
S. Song ◽  
T. Kong ◽  
C. Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Shanxi Province ◽  
Receptor Interaction ◽  
Term Therapy ◽  
Pathway Enrichment Analysis ◽  
Key Genes

Background:Psoriasis is an immune-mediated, genetic disease manifesting in the skin or joints or both, and also has a strong genetic predisposition and autoimmune pathogenic traits1. The hallmark of psoriasis is sustained inflammation that leads to uncontrolled keratinocyte proliferation and dysfunctional differentiation. And it’s also a chronic relapsing disease, which often necessitates a long-term therapy2.Objectives:To investigate the molecular mechanisms of psoriasis and find the potential gene targets for diagnosis and treating psoriasis.Methods:Total 334 gene expression data of patients with psoriasis research (GSE13355 GSE14905 and GSE30999) were obtained from the Gene Expression Omnibus database. After data preprocessing and screening of differentially expressed genes (DEGs) by R software. Online toll Metascape3 was used to analyze Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs. Interactions of proteins encoded by DEGs were discovered by Protein-protein interaction network (PPI) using STRING online software. Cytoscape software was utilized to visualize PPI and the degree of each DEGs was obtained by analyzing the topological structure of the PPI network.Results:A total of 611 DEGs were found to be differentially expressed in psoriasis. GO analysis revealed that up-regulated DEGs were mostly associated with defense and response to external stimulus while down-regulated DEGs were mostly associated with metabolism and synthesis of lipids. KEGG enrichment analysis suggested they were mainly enriched in IL-17 signaling, Toll-like receptor signaling and PPAR signaling pathways, Cytokine-cytokine receptor interaction and lipid metabolism. In addition, top 9 key genes (CXCL10, OASL, IFIT1, IFIT3, RSAD2, MX1, OAS1, IFI44 and OAS2) were identified through Cytoscape.Conclusion:DEGs of psoriasis may play an essential role in disease development and may be potential pathogeneses of psoriasis.References:[1]Boehncke WH, Schon MP. Psoriasis. Lancet 2015;386(9997):983-94. doi: 10.1016/S0140-6736(14)61909-7 [published Online First: 2015/05/31].[2]Zhang YJ, Sun YZ, Gao XH, et al. Integrated bioinformatic analysis of differentially expressed genes and signaling pathways in plaque psoriasis. Mol Med Rep 2019;20(1):225-35. doi: 10.3892/mmr.2019.10241 [published Online First: 2019/05/23].[3]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

Sign in / Sign up

Export Citation Format

Share Document