scholarly journals Identification of Differentially Expressed Genes and Signaling Pathways in Placenta Tissue of Early-Onset and Late-Onset Pre-Eclamptic Pregnancies by Integrated Bioinformatics Analysis

2020 ◽  
Vol 26 ◽  
Author(s):  
Jing Liu ◽  
Guang Song ◽  
Tao Meng ◽  
Ge Zhao
Keyword(s):  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Early Onset ◽  
Bioinformatics Analysis ◽  
Late Onset ◽  
Differentially Expressed

scholarly journals Identification of differentially expressed genes and signaling pathways in ovarian cancer by integrated bioinformatics analysis

2018 ◽  
Vol Volume 11 ◽  
pp. 1457-1474 ◽  
Author(s):  
Xiao Yang ◽  
ShaoMing Zhu ◽  
Li Li ◽  
Li Zhang ◽  
Shu Xian ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Differentially Expressed

scholarly journals Identification of differentially expressed genes and signaling pathways in neutrophils during sepsis-induced immunosuppression via bioinformatics analysis

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Yong-Xin Zheng ◽  
Zhi-Hui Zhang ◽  
Yong-Bo Huang ◽  
Jin-Ping Wang ◽  
Ling Sang ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Differentially Expressed

Identification of key factors associated with early- and late-onset ovarian serous cystadenocarcinoma

2020 ◽  
Vol 16 (34) ◽  
pp. 2821-2833
Author(s):  
Shuang Ma ◽  
Yang Zheng ◽  
Chengwei Fei
Keyword(s):  
Overall Survival ◽  
Differentially Expressed Genes ◽  
Early Onset ◽  
Molecular Mechanisms ◽  
Late Onset ◽  
Differentially Expressed ◽  
Key Factors ◽  
Factors Associated ◽  
Serous Cystadenocarcinoma ◽  
Key Genes

Aim: To uncover the molecular mechanisms of early-onset ovarian serous cystadenocarcinoma (EOOSC; patients <50 years old) and late-onset ovarian serous cystadenocarcinoma (LOOSC; patients ≥50 years old). Materials & methods: Bioinformatics was utilized to identify the key factors. Results: 478 EOOSC and 899 LOOSC individual differentially expressed genes were identified and enriched in different pathways. The expression of key genes LAG3, LRRC63 and MT1B significantly influenced the overall survival of EOOSC patients. The expression of key genes RDH12, NTSR1, ZSCAN16, CT45A3 and EPPIN_WFDC6 significantly affected the overall survival of LOOSC patients. Conclusions: The molecular mechanisms of EOOSC and LOOSC appear to be different, so that patients might be treated individually in respect of age.

scholarly journals Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis

2020 ◽  
Author(s):  
Yanzhi Ge ◽  
Li Zhou ◽  
Zuxiang Chen ◽  
Yingying Mao ◽  
Ting Li ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Immune Infiltration ◽  
Significant Difference

Abstract Background: The disability rate associated with rheumatoid arthritis (RA) ranks high among inflammatory joint diseases. However, the cause and potential molecular events are as yet not clear. Here, we aimed to identify differentially expressed genes (DEGs), pathways and immune infiltration involved in RA utilizing integrated bioinformatics analysis and investigating potential molecular mechanisms. Materials and methods: The expression profiles of GSE55235, GSE55457, GSE55584 and GSE77298 were downloaded from the Gene Expression Omnibus database, which contained 76 synovial membrane samples, including 49 RA samples and 27 normal controls. The microarray datasets were consolidated and DEGs were acquired and further analyzed by bioinformatics techniques. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using R (version 3.6.1) software, respectively. The protein-protein interaction (PPI) network of DEGs were developed utilizing the STRING database. Finally, the CIBERSORT was used to evaluate the infiltration of immune cells in RA. Results: A total of 828 DEGs were recognized, with 758 up-regulated and 70 down-regulated. GO and KEGG pathway analyses demonstrated that these DEGs focused primarily on cytokine receptor activity and relevant signaling pathways. The 30 most firmly related genes among DEGs were identified from the PPI network. The principal component analysis showed that there was a significant difference between the two tissues in infiltration immune. Conclusion: This study shows that screening for DEGs, pathways and immune infiltration utilizing integrated bioinformatics analyses could aid in the comprehension of the molecular mechanisms involved in RA development. Besides, our study provides valuable data related to DEGs, pathways and immune infiltration of RA and may provide new insight into the understanding of molecular mechanisms.

scholarly journals Identification of differentially expressed genes and signaling pathways in rheumatoid arthritis by integrated bioinformatics analysis

2020 ◽  
Author(s):  
Yanzhi Ge ◽  
Li Zhou ◽  
Zuxiang Chen ◽  
Yingying Mao ◽  
Ting Li ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Effective Prevention ◽  
Ppi Networks

Abstract Background The disability rate associated with rheumatoid arthritis (RA) ranks high among inflammatory joint diseases. However, the cause and potential molecular events are as yet not clear. Here, we aimed to identify key genes and pathways involved in RA utilizing integrated bioinformatics analysis and uncover underlying molecular mechanisms. Materials and methods The expression profiles of GSE55235, GSE55457, GSE55584 and GSE77298 were downloaded from the Gene Expression Omnibus database, which contained 76 synovial membrane samples, including 49 RA samples and 27 controls. The microarray datasets were consolidated and differentially expressed genes (DEGs) were acquired and further analyzed by bioinformatics techniques. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using R (version 3.6.1), respectively. The protein-protein interaction (PPI) networks of DEGs were developed utilizing the STRING database. Results A total of 828 DEGs were recognized, with 758 up-regulated and 70 down-regulated. GO and KEGG pathway analyses demonstrated that these DEGs focused primarily on multifactorial binding, transcription activity, cytokin-cytokin receptor interaction and relevant signaling pathways. The 30 most firmly related genes among DEGs were identified from the PPI network. Conclusion This study shows that screening for DEGs and pathways utilizing integrated bioinformatics analyses could aid in the comprehension of the molecular mechanisms involved in RA development. In addition, our study provides valuable data for the effective prevention, diagnosis, treatment and rehabilitation of RA patients as well as providing potential targets for the treatment of RA.

scholarly journals Identification of differentially expressed genes and signaling pathways involved in endometriosis by integrated bioinformatics analysis

2019 ◽  
Author(s):  
Fang‑Fang Dai ◽  
An‑Yu Bao ◽  
Bing Luo ◽  
Zi‑Hang Zeng ◽  
Xiao‑Li Pu ◽  
...  
Keyword(s):  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Differentially Expressed

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.

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