Identification and Functional Enrichment Analysis of Differentially Expressed Genes in Osteoarthritis

2020 ◽  
Author(s):  
Chen Xu ◽  
Ling-bing Meng ◽  
Yu Xiao ◽  
Yong Qiu ◽  
Ying-jue Du ◽  
...  
Keyword(s):  
Hydrogen Peroxide ◽  
Differentially Expressed Genes ◽  
Cellular Response ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Gene Set Enrichment ◽  
Gene Set

Abstract Background Osteoarthritis (OA) is a chronic, progressive, inflammatory, degenerative disease, which has become an osteoarthropathy that seriously affects physical health and quality of life of elderly people. However, the etiology and pathogenesis of OA remains unclear. Therefore, the study purposed to utilize bioinformatics technology to perform identification and functional enrichment analysis of differentially expressed genes in osteoarthritis. Method The main methods of this study consist of access to microarray data (GSE82107 and GSE55235), identification of differently expressed genes (DEGs) by GEO2R between OA and normal synovium samples, enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) by Gene Set Enrichment Analysis (GSEA), construction and analysis of protein-protein interaction (PPI) network, significant module and hub genes. Result A total of 300 DEGs were identified, consisting of 64 up-regulated genes and 11 down-regulated genes in OA samples compared to normal synovium tissues. Gene set enrichment analysis of DEGs provided a comprehensive overview of some major pathophysiological mechanisms in OA: cellular response to hydrogen peroxide, P53 signaling pathway and so on. The study also built the PPI network, and a total of 10 key genes were identified: CYR61, PENK, GOLM1, DUSP1, ATF3, STC2, FOSB, PRSS23, TF, and TNC. Conclusion DEGs exists between OA patients and normal cartilage tissue, which may be involved in the related mechanism of OA development, especially cellular response to hydrogen peroxide and CYR61.

scholarly journals Gene Set Enrichment Analysis and Ingenuity Pathway Analysis to verify the impact on Wnt Signaling Pathway in Taodan Granules Treated Psoriasis

2021 ◽  
Author(s):  
Ying Luo ◽  
Ying Zhang ◽  
Shuang-yi Yin ◽  
Yue Luo ◽  
Xiao-jie Ding ◽  
...  
Keyword(s):  
Wnt Signaling ◽  
Signaling Pathway ◽  
Ingenuity Pathway Analysis ◽  
Pathway Analysis ◽  
Wnt Signaling Pathway ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Gene Set Enrichment ◽  
Gene Set

Abstract Background: Taodan granules (TDGs), traditional Chinese herbals, are effective in treating psoriasis. However, mechanisms of TDGs remain indistinct. The current study aims to indicate the molecular mechanisms of TDGs in treating psoriasis.Methods: Primarily, transcriptional profiling was applied to identify differentially expression genes (DEGs). The following was that we used Gene Set Enrichment Analysis (GSEA) and Ingenuity Pathway Analysis (IPA) analysis for functional enrichment analysis. Eventually, RT-PCR was performed to validation. Results: The results revealed that TDGs could regulated the Wnt signaling pathway to ameliorate skin lesions of imiquimod (IMQ)-induced psoriatic mouse models. IPA core network associated with Wnt signaling pathways in TDGs for psoriasis was established. Thereinto zeste homolog (EZH) 2, CTNNB1, TP63, and WD repeat domain (WDR) 5 could be considered as upstream genes in the Wnt signaling pathway.Conclusions: The Wnt signaling pathway with these above upstream genes might be potential therapeutic targets of TDGs for psoriasis.

scholarly journals A Gene Set Enrichment Analysis of multiomic celiac disease data

2017 ◽  
Author(s):  
Eugenio Del Prete ◽  
Angelo Facchiano ◽  
Pietro Liò
Keyword(s):  
Celiac Disease ◽  
Cellular Response ◽  
System Development ◽  
Enrichment Analysis ◽  
Point Of View ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Exact Test ◽  
Gene Set ◽  
Immune System Development

Celiac disease is a chronic condition, which can be described as inflammatory and autoimmune. The well-known treatment is a lifelong gluten-free diet, but it can be not totally effective for a high percentage of the patients. The aim of this work is to approach the celiac disease complexity from a bioinformatics point of view. The idea is to analyse the state of the art from GEO online repository and revisit the works, by integrating gene expression data and Gene Ontology (GO) terms. Gene Set Enrichment Analysis (GSEA) is a set of statistical methods to classify genes in groups, which are related to common biological function, chromosomal location or regulation. The work is developed in R environment. The packages are downloaded by the online repository Bioconductor. The studies are not standardized. In these circumstances, the candidate genes subset is chosen with a trade-off among all the scores, thus the creation of a GO graph eludes the Fishers exact test, keeping its biological importance to define process clusters. A little framework on the biological processes involved in each study on celiac disease is suggested: GSE11501, peptidyl-tyrosine phosphorylation, phosphatidylinositol 3-kinase signaling, and response to endoplasmic reticulum stress; GSE87629, mitosis regulation, microtubule cytoskeleton organisation, and protein destabilization; GSE72625, signaling pathway and cellular response about interferon-gamma; GSE61849a, immune response and immune system development; GSE61849b, protein phosphorylation, apoptotic process, and regulation of cell adhesion; GSE76168, cytokine mediate signaling pathways.

scholarly journals A Gene Set Enrichment Analysis of multiomic celiac disease data

2017 ◽  
Author(s):  
Eugenio Del Prete ◽  
Angelo Facchiano ◽  
Pietro Liò
Keyword(s):  
Celiac Disease ◽  
Cellular Response ◽  
System Development ◽  
Enrichment Analysis ◽  
Point Of View ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Exact Test ◽  
Gene Set ◽  
Immune System Development

Celiac disease is a chronic condition, which can be described as inflammatory and autoimmune. The well-known treatment is a lifelong gluten-free diet, but it can be not totally effective for a high percentage of the patients. The aim of this work is to approach the celiac disease complexity from a bioinformatics point of view. The idea is to analyse the state of the art from GEO online repository and revisit the works, by integrating gene expression data and Gene Ontology (GO) terms. Gene Set Enrichment Analysis (GSEA) is a set of statistical methods to classify genes in groups, which are related to common biological function, chromosomal location or regulation. The work is developed in R environment. The packages are downloaded by the online repository Bioconductor. The studies are not standardized. In these circumstances, the candidate genes subset is chosen with a trade-off among all the scores, thus the creation of a GO graph eludes the Fishers exact test, keeping its biological importance to define process clusters. A little framework on the biological processes involved in each study on celiac disease is suggested: GSE11501, peptidyl-tyrosine phosphorylation, phosphatidylinositol 3-kinase signaling, and response to endoplasmic reticulum stress; GSE87629, mitosis regulation, microtubule cytoskeleton organisation, and protein destabilization; GSE72625, signaling pathway and cellular response about interferon-gamma; GSE61849a, immune response and immune system development; GSE61849b, protein phosphorylation, apoptotic process, and regulation of cell adhesion; GSE76168, cytokine mediate signaling pathways.

scholarly journals Identification of key gene in colorectal cancer using bioinformatics analysis.

2020 ◽  
Author(s):  
Rongrong Xiao ◽  
Ping Wang ◽  
Tian Xia ◽  
Chun-Yi Li ◽  
Ting Jiang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Tumor Microenvironment ◽  
Differentially Expressed Genes ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes

Abstract Background Tumor microenvironment plays important roles in the development of cancer. The aim of our study was to examine the expression of genes in colorectal cancer and also to evaluate the association value between expression level of these genes and clinical features. Methods We combined The Cancer Genome Atlas (TCGA) datasets to identify differentially expressed genes in colon cancer. Using these differentially expressed genes, we constructed protein-protein interaction network and conducted functional enrichment analysis. Genes with degree beyond 10 in the PPI network were regarded as hub genes. Then, we verified of the expression of molecules in Oncomine datasets and conducted Kaplan-Meier curve and log-rank test and functional enrichment analysis on these hub genes. Finally, we analyzed the relationship clinicopathological features analysis with the key gene. Results There were 719 differentially expressed genes identified to be associated with colon cancer microenvironment. We screened out 10 hub genes by construction of PPI network. The functions of these hub genes were enriched in cytokine-cytokine receptor interaction, alcoholism and systemic lupus erythematosus, which provided further insight into the roles of these genes in the tumor microenvironment. GNG4, with the highest degrees in the PPI network, were highly exprepressed in metastasis(P = 9.5-05) ,N1(P = 0.0025) and N2(,0.037).It was a relationship with stage. It was significantly different between with stage I and IV, II and III, II and IV,III and IV (P = 0.0015,0.029,3.9-05,0.00074,0.01,respectively) Conclusions We identified GNG4 can be regarded as a prognostic biomarker in colon cancer.

scholarly journals Gene Set Enrichment Analysis of Interaction Networks Weighted by Node Centrality

2021 ◽  
Vol 12 ◽  
Author(s):  
Alessandra Zito ◽  
Marta Lualdi ◽  
Paola Granata ◽  
Dario Cocciadiferro ◽  
Antonio Novelli ◽  
...  
Keyword(s):  
Betweenness Centrality ◽  
Cellular Response ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Comparative Genomic ◽  
Expression Data ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Ppi Networks

Gene set enrichment analysis (GSEA) is a powerful tool to associate a disease phenotype to a group of genes/proteins. GSEA attributes a specific weight to each gene/protein in the input list that depends on a metric of choice, which is usually represented by quantitative expression data. However, expression data are not always available. Here, GSEA based on betweenness centrality of a protein–protein interaction (PPI) network is described and applied to two cases, where an expression metric is missing. First, personalized PPI networks were generated from genes displaying alterations (assessed by array comparative genomic hybridization and whole exome sequencing) in four probands bearing a 16p13.11 microdeletion in common and several other point variants. Patients showed disease phenotypes linked to neurodevelopment. All networks were assembled around a cluster of first interactors of altered genes with high betweenness centrality. All four clusters included genes known to be involved in neurodevelopmental disorders with different centrality. Moreover, the GSEA results pointed out to the evidence of “cell cycle” among enriched pathways. Second, a large interaction network obtained by merging proteomics studies on three neurodegenerative disorders was analyzed from the topological point of view. We observed that most central proteins are often linked to Parkinson’s disease. The selection of these proteins improved the specificity of GSEA, with “Metabolism of amino acids and derivatives” and “Cellular response to stress or external stimuli” as top-ranked enriched pathways. In conclusion, betweenness centrality revealed to be a suitable metric for GSEA. Thus, centrality-based GSEA represents an opportunity for precision medicine and network medicine.

scholarly journals Higher Acid-Base Imbalance Associated with Respiratory Failure Could Decrease the Survival of Patients with Scrub Typhus during Intensive Care Unit Stay: A Gene Set Enrichment Analysis

2019 ◽  
Vol 8 (10) ◽  
pp. 1580 ◽  
Author(s):  
Kyoung Min Moon ◽  
Kyueng-Whan Min ◽  
Mi-Hye Kim ◽  
Dong-Hoon Kim ◽  
Byoung Kwan Son ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Respiratory Failure ◽  
Scrub Typhus ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Acid Base ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets

Ninety percent of patients with scrub typhus (SC) with vasculitis-like syndrome recover after mild symptoms; however, 10% can suffer serious complications, such as acute respiratory failure (ARF) and admission to the intensive care unit (ICU). Predictors for the progression of SC have not yet been established, and conventional scoring systems for ICU patients are insufficient to predict severity. We aimed to identify simple and robust indicators to predict aggressive behaviors of SC. We evaluated 91 patients with SC and 81 non-SC patients who were admitted to the ICU, and 32 cases from the public functional genomics data repository for gene expression analysis. We analyzed the relationships between several predictors and clinicopathological characteristics in patients with SC. We performed gene set enrichment analysis (GSEA) to identify SC-specific gene sets. The acid-base imbalance (ABI), measured 24 h before serious complications, was higher in patients with SC than in non-SC patients. A high ABI was associated with an increased incidence of ARF, leading to mechanical ventilation and worse survival. GSEA revealed that SC correlated to gene sets reflecting inflammation/apoptotic response and airway inflammation. ABI can be used to indicate ARF in patients with SC and assist with early detection.

scholarly journals Transcriptional survey of alveolar macrophages in a murine model of chronic granulomatous inflammation reveals common themes with human sarcoidosis

2018 ◽  
Vol 314 (4) ◽  
pp. L617-L625 ◽  
Author(s):  
Arjun Mohan ◽  
Anagha Malur ◽  
Matthew McPeek ◽  
Barbara P. Barna ◽  
Lynn M. Schnapp ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Murine Model ◽  
Alveolar Macrophages ◽  
Gene Set Enrichment Analysis ◽  
Differentially Expressed ◽  
Multiwall Carbon Nanotube ◽  
Gene Set Enrichment ◽  
Integrated Network ◽  
Gene Set ◽  
Gene Sets

To advance our understanding of the pathobiology of sarcoidosis, we developed a multiwall carbon nanotube (MWCNT)-based murine model that shows marked histological and inflammatory signal similarities to this disease. In this study, we compared the alveolar macrophage transcriptional signatures of our animal model with human sarcoidosis to identify overlapping molecular programs. Whole genome microarrays were used to assess gene expression of alveolar macrophages in six MWCNT-exposed and six control animals. The results were compared with the transcriptional profiles of alveolar immune cells in 15 sarcoidosis patients and 12 healthy humans. Rigorous statistical methods were used to identify differentially expressed genes. To better elucidate activated pathways, integrated network and gene set enrichment analysis (GSEA) was performed. We identified over 1,000 differentially expressed between control and MWCNT mice. Gene ontology functional analysis showed overrepresentation of processes primarily involved in immunity and inflammation in MCWNT mice. Applying GSEA to both mouse and human samples revealed upregulation of 92 gene sets in MWCNT mice and 142 gene sets in sarcoidosis patients. Commonly activated pathways in both MWCNT mice and sarcoidosis included adaptive immunity, T-cell signaling, IL-12/IL-17 signaling, and oxidative phosphorylation. Differences in gene set enrichment between MWCNT mice and sarcoidosis patients were also observed. We applied network analysis to differentially expressed genes common between the MWCNT model and sarcoidosis to identify key drivers of disease. In conclusion, an integrated network and transcriptomics approach revealed substantial functional similarities between a murine model and human sarcoidosis particularly with respect to activation of immune-specific pathways.

scholarly journals Drug perturbation gene set enrichment analysis (dpGSEA): a new transcriptomic drug screening approach

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Mike Fang ◽  
Brian Richardson ◽  
Cheryl M. Cameron ◽  
Jean-Eudes Dazard ◽  
Mark J. Cameron
Keyword(s):  
Drug Targets ◽  
T Regulatory Cells ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Regulatory Cells ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets ◽  
Gastroenteropancreatic Neuroendocrine Tumor ◽  
Public Datasets

Abstract Background In this study, we demonstrate that our modified Gene Set Enrichment Analysis (GSEA) method, drug perturbation GSEA (dpGSEA), can detect phenotypically relevant drug targets through a unique transcriptomic enrichment that emphasizes biological directionality of drug-derived gene sets. Results We detail our dpGSEA method and show its effectiveness in detecting specific perturbation of drugs in independent public datasets by confirming fluvastatin, paclitaxel, and rosiglitazone perturbation in gastroenteropancreatic neuroendocrine tumor cells. In drug discovery experiments, we found that dpGSEA was able to detect phenotypically relevant drug targets in previously published differentially expressed genes of CD4+T regulatory cells from immune responders and non-responders to antiviral therapy in HIV-infected individuals, such as those involved with virion replication, cell cycle dysfunction, and mitochondrial dysfunction. dpGSEA is publicly available at https://github.com/sxf296/drug_targeting. Conclusions dpGSEA is an approach that uniquely enriches on drug-defined gene sets while considering directionality of gene modulation. We recommend dpGSEA as an exploratory tool to screen for possible drug targeting molecules.

scholarly journals Key pathways involved in prostate cancer based on gene set enrichment analysis and meta analysis

2011 ◽  
Vol 10 (4) ◽  
pp. 3856-3887 ◽  
Author(s):  
Q.Y. Ning ◽  
J.Z. Wu ◽  
N. Zang ◽  
J. Liang ◽  
Y.L. Hu ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Meta Analysis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Key Pathways

scholarly journals STAT1, IGF1, RAC1, and MDM2 Are Associated with Recurrence of Giant Cell Tumor of Bone

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Shuxin Chen ◽  
Zepeng Du ◽  
Bingli Wu ◽  
Huiyang Shen ◽  
Chunpeng Liu ◽  
...  
Keyword(s):  
Giant Cell Tumor ◽  
Giant Cell ◽  
Clinical Characteristics ◽  
Multivariate Logistic Regression Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Small Gtpase ◽  
Cell Tumor ◽  
Functional Enrichment ◽  
Ppi Network

Background. In our previous study, mouse double minute 2 homolog (MDM2), insulin-like growth factor 1 (IGF1), signal transducer and activator of transcription 1 (STAT1), and Rac family small GTPase 1 (RAC1) were correlated with the recurrence of giant cell tumor of bone (GCT). The aim of this study is to use a large cohort study to confirm the involvement of these four genes in GCT recurrence. Methods. The expression of these four genes was detected and compared between GCT patients with or without recurrence. The correlation between the expression of these four genes and clinical characteristics was evaluated. Protein-protein interaction (PPI) network was constructed for functional enrichment analysis. Results. It showed that the expression levels of MDM2, IGF1, STAT1, and RAC1 in GCT patients with recurrence were significantly higher than those in GCT patients without recurrence (P<0.05). Multivariate logistic regression analysis suggested that several clinical characteristics may influence prognosis. A PPI network was constructed using the four genes as hub genes. Functional enrichment analysis showed that this network involves many important biological progress mediated by these four genes, including immune response. Conclusion. MDM2, IGF1, STAT1, and RAC1 are associated with GCT recurrence, which might serve as biomarkers for GCT recurrence.

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