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.

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

scholarly journals Identification of Differentially Expressed Genes and Signaling Pathways in Acute Myocardial Infarction Based on Integrated Bioinformatics Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Da-Qiu Chen ◽  
Xiang-Sheng Kong ◽  
Xue-Bin Shen ◽  
Mao-Zhi Huang ◽  
Jian-Ping Zheng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Differentially Expressed ◽  
High Morbidity ◽  
Hub Genes ◽  
Module Analysis

Background. Acute myocardial infarction (AMI) is a common disease with high morbidity and mortality around the world. The aim of this research was to determine the differentially expressed genes (DEGs), which may serve as potential therapeutic targets or new biomarkers in AMI. Methods. From the Gene Expression Omnibus (GEO) database, three gene expression profiles (GSE775, GSE19322, and GSE97494) were downloaded. To identify the DEGs, integrated bioinformatics analysis and robust rank aggregation (RRA) method were applied. These DEGs were performed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses by using Clusterprofiler package. In order to explore the correlation between these DEGs, the interaction network of protein-protein internet (PPI) was constructed using the STRING database. Utilizing the MCODE plug-in of Cytoscape, the module analysis was performed. Utilizing the cytoHubba plug-in, the hub genes were screened out. Results. 57 DEGs in total were identified, including 2 down- and 55 upregulated genes. These DEGs were mainly enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and so on. The module analysis filtered out 18 key genes, including Cxcl5, Arg1, Cxcl1, Spp1, Selp, Ptx3, Tnfaip6, Mmp8, Serpine1, Ptgs2, Il6, Il1r2, Il1b, Ccl3, Ccr1, Hmox1, Cxcl2, and Ccl2. Ccr1 was the most fundamental gene in PPI network. 4 hub genes in total were identified, including Cxcl1, Cxcl2, Cxcl5, and Mmp8. Conclusion. This study may provide credible molecular biomarkers in terms of screening, diagnosis, and prognosis for AMI. Meanwhile, it also serves as a basis for exploring new therapeutic target for AMI.

scholarly journals Identification of Key Modules and Hub Genes of Annulus Fibrosus in Intervertebral Disc Degeneration

2021 ◽  
Vol 11 ◽  
Author(s):  
Hantao Wang ◽  
Wenhui Liu ◽  
Bo Yu ◽  
Xiaosheng Yu ◽  
Bin Chen
Keyword(s):  
Gene Expression ◽  
Intervertebral Disc ◽  
Differentially Expressed Genes ◽  
Disc Degeneration ◽  
Pathway Analysis ◽  
Annulus Fibrosus ◽  
Intervertebral Disc Degeneration ◽  
Differentially Expressed ◽  
Clinical Samples ◽  
Hub Genes

Background: Intervertebral disc degeneration impairs the quality of patients lives. Even though there has been development of many therapeutic strategies, most of them remain unsatisfactory due to the limited understanding of the mechanisms that underlie the intervertebral disc degeneration.Questions/purposes: This study is meant to identify the key modules and hub genes related to the annulus fibrosus in intervertebral disc degeneration (IDD) through: (1) constructing a weighted gene co-expression network; (2) identifying key modules and hub genes; (3) verifying the relationships of key modules and hub genes with IDD; and (4) confirming the expression pattern of hub genes in clinical samples.Methods: The Gene Expression Omnibus provided 24 sets of annulus fibrosus microarray data. Differentially expressed genes between the annulus fibrosus of degenerative and non-degenerative intervertebral disc samples have gone through the Gene Ontology (GO) and pathway analysis. The construction of a gene network and classification of genes into different modules were conducted through performing Weighted Gene Co-expression Network Analysis. The identification of modules and hub genes that were most related to intervertebral disc degeneration was proceeded. In order to verify the relationships of the module and hub genes with intervertebral disc degeneration, Ingenuity Pathway Analysis was operated. Clinical samples were adopted to help verify the hub gene expression profile.Results: One thousand one hundred ninety differentially expressed genes were identified. Terms and pathways associated with intervertebral disc degeneration were presented by GO and pathway analysis. The construction of a Weighted Gene Coexpression Network was completed and clustering differentially expressed genes into four modules was also achieved. The module with the lowest P-value and the highest absolute correlation coefficient was selected and its relationship with intervertebral disc degeneration was confirmed by Ingenuity Pathway Analysis. The identification of hub genes and the confirmation of their expression profile were also realized.Conclusions: This study generated a comprehensive overview of the gene networks underlying annulus fibrosus in intervertebral disc degeneration.Clinical Relevance: Modules and hub genes identified in this study are highly associated with intervertebral disc degeneration, and may serve as potential therapeutic targets for intervertebral disc degeneration.

scholarly journals Key Genes and Prognostic Analysis in HER2+ Breast Cancer

2021 ◽  
Vol 20 ◽  
pp. 153303382098329
Author(s):  
Yujie Weng ◽  
Wei Liang ◽  
Yucheng Ji ◽  
Zhongxian Li ◽  
Rong Jia ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Differentially Expressed ◽  
Protein Kinase Activity ◽  
Protein Protein Interaction ◽  
Risk Of Death ◽  
Her2 Breast Cancer ◽  
Key Genes

Human epidermal growth factor 2 (HER2)+ breast cancer is considered the most dangerous type of breast cancers. Herein, we used bioinformatics methods to identify potential key genes in HER2+ breast cancer to enable its diagnosis, treatment, and prognosis prediction. Datasets of HER2+ breast cancer and normal tissue samples retrieved from Gene Expression Omnibus and The Cancer Genome Atlas databases were subjected to analysis for differentially expressed genes using R software. The identified differentially expressed genes were subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses followed by construction of protein-protein interaction networks using the STRING database to identify key genes. The genes were further validated via survival and differential gene expression analyses. We identified 97 upregulated and 106 downregulated genes that were primarily associated with processes such as mitosis, protein kinase activity, cell cycle, and the p53 signaling pathway. Visualization of the protein-protein interaction network identified 10 key genes ( CCNA2, CDK1, CDC20, CCNB1, DLGAP5, AURKA, BUB1B, RRM2, TPX2, and MAD2L1), all of which were upregulated. Survival analysis using PROGgeneV2 showed that CDC20, CCNA2, DLGAP5, RRM2, and TPX2 are prognosis-related key genes in HER2+ breast cancer. A nomogram showed that high expression of RRM2, DLGAP5, and TPX2 was positively associated with the risk of death. TPX2, which has not previously been reported in HER2+ breast cancer, was associated with breast cancer development, progression, and prognosis and is therefore a potential key gene. It is hoped that this study can provide a new method for the diagnosis and treatment of HER2 + breast cancer.

scholarly journals Diagnostic model of combined ceRNA and DNA methylation related genes in esophageal carcinoma

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8831 ◽  
Author(s):  
Xiaojiao Guan ◽  
Yao Yao ◽  
Guangyao Bao ◽  
Yue Wang ◽  
Aimeng Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Esophageal Cancer ◽  
Esophageal Carcinoma ◽  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Diagnostic Model ◽  
Link Type ◽  
Key Genes

Esophageal cancer is a common malignant tumor in the world, and the aim of this study was to screen key genes related to the development of esophageal cancer using a variety of bioinformatics analysis tools and analyze their biological functions. The data of esophageal squamous cell carcinoma from the Gene Expression Omnibus (GEO) were selected as the research object, processed and analyzed to screen differentially expressed microRNAs (miRNAs) and differential methylation genes. The competing endogenous RNAs (ceRNAs) interaction network of differentially expressed genes was constructed by bioinformatics tools DAVID, String, and Cytoscape. Biofunctional enrichment analysis was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The expression of the screened genes and the survival of the patients were verified. By analyzing GSE59973 and GSE114110, we found three down-regulated and nine up-regulated miRNAs. The gene expression matrix of GSE120356 was calculated by Pearson correlation coefficient, and the 11696 pairs of ceRNA relation were determined. In the ceRNA network, 643 lncRNAs and 147 mRNAs showed methylation difference. Functional enrichment analysis showed that these differentially expressed genes were mainly concentrated in the FoxO signaling pathway and were involved in the corresponding cascade of calcineurin. By analyzing the clinical data in The Cancer Genome Atlas (TCGA) database, it was found that four lncRNAs had an important impact on the survival and prognosis of esophageal carcinoma patients. QRT-PCR was also conducted to identify the expression of the key lncRNAs (RNF217-AS1, HCP5, ZFPM2-AS1 and HCG22) in ESCC samples. The selected key genes can provide theoretical guidance for further research on the molecular mechanism of esophageal carcinoma and the screening of molecular markers.

scholarly journals Can We Assume the Gene Expression Profile as a Proxy for Signaling Network Activity?

Biomolecules ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 850 ◽  
Author(s):  
Mehran Piran ◽  
Reza Karbalaei ◽  
Mehrdad Piran ◽  
Jehad Aldahdooh ◽  
Mehdi Mirzaie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Expression Profile ◽  
Signaling Network ◽  
Differentially Expressed ◽  
Network Activity ◽  
Gene Products ◽  
Expression Data ◽  
Gene Pairs

Studying relationships among gene products by expression profile analysis is a common approach in systems biology. Many studies have generalized the outcomes to the different levels of central dogma information flow and assumed a correlation of transcript and protein expression levels. However, the relation between the various types of interaction (i.e., activation and inhibition) of gene products to their expression profiles has not been widely studied. In fact, looking for any perturbation according to differentially expressed genes is the common approach, while analyzing the effects of altered expression on the activity of signaling pathways is often ignored. In this study, we examine whether significant changes in gene expression necessarily lead to dysregulated signaling pathways. Using four commonly used and comprehensive databases, we extracted all relevant gene expression data and all relationships among directly linked gene pairs. We aimed to evaluate the ratio of coherency or sign consistency between the expression level as well as the causal relationships among the gene pairs. Through a comparison with random unconnected gene pairs, we illustrate that the signaling network is incoherent, and inconsistent with the recorded expression profile. Finally, we demonstrate that, to infer perturbed signaling pathways, we need to consider the type of relationships in addition to gene-product expression data, especially at the transcript level. We assert that identifying enriched biological processes via differentially expressed genes is limited when attempting to infer dysregulated pathways.

scholarly journals Digital Gene Expression Profiling Analysis of Aged Mice under Moxibustion Treatment

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Nan Liu ◽  
Yunyao Jiang ◽  
Min Xing ◽  
Baixiao Zhao ◽  
Jincai Hou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Ontology ◽  
Gene Expression Profiling ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Expression Profiling ◽  
Digital Gene Expression ◽  
Differentially Expressed ◽  
Aged Mice ◽  
Digital Gene Expression Profiling

Aging is closely connected with death, progressive physiological decline, and increased risk of diseases, such as cancer, arteriosclerosis, heart disease, hypertension, and neurodegenerative diseases. It is reported that moxibustion can treat more than 300 kinds of diseases including aging related problems and can improve immune function and physiological functions. The digital gene expression profiling of aged mice with or without moxibustion treatment was investigated and the mechanisms of moxibustion in aged mice were speculated by gene ontology and pathway analysis in the study. Almost 145 million raw reads were obtained by digital gene expression analysis and about 140 million (96.55%) were clean reads. Five differentially expressed genes with an adjusted P value < 0.05 and |log⁡2(fold  change)| > 1 were identified between the control and moxibustion groups. They were Gm6563, Gm8116, Rps26-ps1, Nat8f4, and Igkv3-12. Gene ontology analysis was carried out by the GOseq R package and functional annotations of the differentially expressed genes related to translation, mRNA export from nucleus, mRNA transport, nuclear body, acetyltransferase activity, and so on. Kyoto Encyclopedia of Genes and Genomes database was used for pathway analysis and ribosome was the most significantly enriched pathway term.

scholarly journals Transcriptomic analysis of PPARα-dependent alterations during cardiac hypertrophy

2008 ◽  
Vol 36 (1) ◽  
pp. 15-23 ◽  
Author(s):  
Pascal J. H. Smeets ◽  
Heleen M. de Vogel-van den Bosch ◽  
Peter H. M. Willemsen ◽  
Alphons P. Stassen ◽  
Torik Ayoubi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Immune Response ◽  
Lipid Metabolism ◽  
Cardiac Hypertrophy ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Left Ventricular ◽  
Transcriptomic Analysis ◽  
Differentially Expressed ◽  
Wild Type

Peroxisome proliferator-activated receptor (PPAR)α regulates lipid metabolism at the transcriptional level and modulates the expression of genes involved in inflammation, cell proliferation, and differentiation. Although PPARα has been shown to mitigate cardiac hypertrophy, knowledge about underlying mechanisms and the nature of signaling pathways involved is fragmentary and incomplete. The aim of this study was to identify the processes and signaling pathways regulated by PPARα in hearts challenged by a chronic pressure overload by means of whole genome transcriptomic analysis. PPARα−/− and wild-type mice were subjected to transverse aortic constriction (TAC) for 28 days, and left ventricular gene expression profile was determined with Affymetrix GeneChip Mouse Genome 430 2.0 arrays containing >45,000 probe sets. In unchallenged hearts, the mere lack of PPARα resulted in 821 differentially expressed genes, many of which are related to lipid metabolism and immune response. TAC resulted in a more pronounced cardiac hypertrophy and more extensive changes in gene expression (1,910 and 312 differentially expressed genes, respectively) in PPARα−/− mice than in wild-type mice. Many of the hypertrophy-related genes were related to development, signal transduction, actin filament organization, and collagen synthesis. Compared with wild-type hypertrophied hearts, PPARα−/− hypertrophied hearts revealed enrichment of gene clusters related to extracellular matrix remodeling, immune response, oxidative stress, and inflammatory signaling pathways. The present study therefore demonstrates that, in addition to lipid metabolism, PPARα is an important modulator of immune and inflammatory response in cardiac muscle.

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