scholarly journals Inflammation as a risk factor for stroke in atrial fibrillation: data from a microarray data analysis

2020 ◽  
Vol 48 (5) ◽  
pp. 030006052092167
Author(s):  
Yingyuan Li ◽  
Wulin Tan ◽  
Fang Ye ◽  
Shihong Wen ◽  
Rong Hu ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Microarray Data ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Microarray Data Analysis ◽  
Protein Protein Interaction ◽  
Inflammatory Processes ◽  
Key Genes

Objective Stroke is a severe complication of atrial fibrillation (AF). We aimed to discover key genes and microRNAs related to stroke risk in patients with AF using bioinformatics analysis. Methods GSE66724 microarray data, including peripheral blood samples from eight patients with AF and stroke and eight patients with AF without stroke, were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between AF patients with and without stroke were identified using the GEO2R online tool. Functional enrichment analysis was performed using the DAVID database. A protein–protein interaction (PPI) network was obtained using the STRING database. MicroRNAs (miRs) targeting these DEGs were obtained from the miRNet database. A miR–DEG network was constructed using Cytoscape software. Results We identified 165 DEGs (141 upregulated and 24 downregulated). Enrichment analysis showed enrichment of certain inflammatory processes. The miR–DEG network revealed key genes, including MEF2A, CAND1, PELI1, and PDCD4, and microRNAs, including miR-1, miR-1-3p, miR-21, miR-21-5p, miR-192, miR-192-5p, miR-155, and miR-155-5p. Conclusion Dysregulation of certain genes and microRNAs involved in inflammation may be associated with a higher risk of stroke in patients with AF. Evaluating these biomarkers could improve prediction, prevention, and treatment of stroke in patients with AF.

scholarly journals Application of Bioinformatics Methods to Identify Key Genes and Functions in Chronic Pelvic Pain

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wenchao Sun ◽  
Qiji Ju
Keyword(s):  
Pelvic Pain ◽  
Chronic Pelvic Pain ◽  
Single Gene ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
Protein Protein Interaction ◽  
Key Genes

Neuropathologic pain (NPP) occurs in most patients with chronic pelvic pain (CPP), and the unique physiological characteristics of visceral sensory neurons make the current analgesic effect of CPP patients not optimistic. Therefore, this study explored the possible biological characteristics of key genes in CPP through the bioinformatics method. CPP-related dataset GSE131619 was downloaded from Gene Expression Omnibus to investigate the differentially expressed genes (DEGs) between lumbar dorsal root ganglia (DRG) and sacral DRG, and the functional enrichment analysis was performed. A protein-protein interaction (PPI) network was constructed to search subnet modules of specific biological processes, and then, the genes in the subnet were enriched by single gene set analysis. A CPP mouse model was established, and the expression of key genes were identified by qPCR. The results showed that 127 upregulated DEGs and 103 downregulated DEGs are identified. Functional enrichment analysis showed that most of the genes involved in signal transduction were involved in the pathway of receptor interaction. A subnet module related to neural signal regulation was identified in PPI, including CHRNB4, CHRNA3, and CHRNB2. All three genes were associated with neurological or inflammatory activity and are downregulated in the sacral spinal cord of CPP mice. This study provided three key candidate genes for CPP: CHRNB4, CHRNA3, and CHRNB2, which may be involved in the occurrence and development of CPP, and provided a powerful molecular target for the clinical diagnosis and treatment of CPP.

scholarly journals Role of COL3A1 and POSTN on Pathologic Stages of Esophageal Cancer

2020 ◽  
Vol 19 ◽  
pp. 153303382097748
Author(s):  
Shao-wei Zhang ◽  
Nan Zhang ◽  
Na Wang
Keyword(s):  
Gene Expression ◽  
Esophageal Cancer ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Protein Protein Interaction ◽  
And Control ◽  
The Impact

Background: Esophageal cancer (EC) is a primary malignant tumor originating from the esophageal of the epithelium. Surgical resection is a potential treatment for EC, but this is only appropriate for patients who have locally resectable lesions suitable for surgery. However, most patients with EC are at a late stage when diagnosed. Therefore, there is an urgent need to further explore the pathogenesis of EC to enable early diagnosis and treatment. Methods: Our study downloaded 2 expression spectrum datasets (GSE92396 and GSE100942) in the Gene Expression Omnibus (GEO) database. GEO2 R was used to identify the Differentially expressed genes (DEGs) between the samples of EC and control. Using the DAVID tool to make the Functional enrichment analysis. Constructing A protein–protein interaction (PPI) network. Identifying the Hub genes. The impact of hub gene expression on overall survival and their expression based on immunohistochemistry were analyzed. Associated microRNAs were also predicted. Results: There were 36 common DEGs identified. The analysis of GO and KEGG results shown that the variations were predominantly concentrated in the extracellular matrix (ECM), ECM organization, DNA binding, platelet activation, and ECM-receptor interactions. COL3A1 and POSTN had high expression in EC tissues which was compared with their expression in healthy tissues. Analysis of pathologic stages showed that when COL3A1 and POSTN were highly expressed, the stage of the pathologic of EC patients was relatively high (P < 0.005). Conclusions: COL3A1 and POSTN may play an important role in the advancement and occurrence of EC. These genes could provide some novel ideas and basis for the diagnosis and targeted treatment of EC.

scholarly journals Bioinformatics analysis of key biomarkers for retinoblastoma

2021 ◽  
Vol 49 (6) ◽  
pp. 030006052110222
Author(s):  
Xin-mei Zhao ◽  
Yuan-Bin Li ◽  
Peng Sun ◽  
Ya-di Pu ◽  
Meng-jie shan ◽  
...  
Keyword(s):  
Enrichment Analysis ◽  
Microarray Dataset ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Protein Protein Interaction ◽  
Bilateral Retinoblastoma ◽  
Endopeptidase Activity ◽  
Novel Biomarkers ◽  
Geo Database

Objective To identify key genes involved in occurrence and development of retinoblastoma. Methods The microarray dataset, GSE5222, was downloaded from the gene expression omnibus (GEO) database. Differentially expressed genes (DEGs) between unilateral and bilateral retinoblastoma were identified and functional enrichment analysis performed. The protein–protein interaction (PPI) network was constructed and analysed by STRING and Cytoscape. Results DEGs were mainly associated with activation of cysteine-type endopeptidase activity involved in apoptotic process and small molecule catabolic process. Seven genes (WAS, GNB3, PTGER1, TACR1, GPR143, NPFF and CDKN2A) were identified as HUB genes. Conclusion Our research provides more understanding of the mechanisms of the disease at a molecular level and may help in the identification of novel biomarkers for retinoblastoma.

scholarly journals Identification of Potential Crucial Genes in Atrial Fibrillation: A Bioinformatic Analysis

2020 ◽  
Author(s):  
Junguo Zhang ◽  
Xin Huang ◽  
Xiaojie Wang ◽  
Yanhui Gao ◽  
Li Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Atrial Fibrillation ◽  
Growth Factor ◽  
Microarray Data ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Insulin Like Growth Factor

Abstract Background Atrial fibrillation (AF) is at least partially heritable, affecting 2-3% of the population in Europe and the USA. However, a substantial proportion of heritability is still lacking. In the present study, we aim to identify potential crucial genes associated with AF through bioinformatic analyses of public datasets. Methods Microarray data sets of GSE115574, GSE31821, GSE79768, GSE41177 and GSE14975 from the Gene Expression Omnibus (GEO) database were retrieved. After merging all microarray data and adjusting batch effect, differentially expressed genes (DEGs) were identified. Functional enrichment analyses based on Gene Ontology (GO) resource, Kyoto Encyclopedia of Genes and Genomes (KEGG) resource, Gene Set Enrichment Analysis (GSEA), Reactome Pathway Database and Disease Ontology (DO) were carried out. Protein-protein interaction (PPI) network was constructed using the STRING database. Combined with aforementioned significant bioinformatics information, potential crucial genes were subsequently selected. The comparative toxicogenomics database (CTD) was carried out to explore the interaction between potential crucial genes and AF. Result We identified 27 of DEGs with gene expression fold change (FC) ≥ 1.5 or ≤ 2/3 (|log2 FC| ≥ 0.58) and 5 with FC ≥ 2 or ≤ 0.5 (|log2 FC| ≥ 1) of AF patients compared with sinus rhythm controls. The most significantly enriched pathway was regulation of insulin-like growth factor transport and uptake by insulin-like growth factor binding proteins. IGFBP2, C1orf105, FHL2, CHGB, ATP1B4, IGFBP3, SLC26A9, CXCR4 and HTR2B were considered the potential crucial genes. CTD showed CXCR4, IGFBP2, IGFBP3 and FHL2 had higher scores with AF. Conclusions The 9 potential crucial genes, especially CXCR4, IGFBP2, IGFBP3 and FHL2 , may be associated with risk of AF. Our study provided new insights of AF into genetics, molecular pathogenesis and new therapeutic targets.

scholarly journals Identification of Key Genes and Pathways Associated with Sex Differences in Osteoarthritis Based on Bioinformatics Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Shiying Wang ◽  
Huanmei Wang ◽  
Wei Liu ◽  
Biaofang Wei
Keyword(s):  
Gene Expression ◽  
Sex Differences ◽  
Focal Adhesion ◽  
Osteoclast Differentiation ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Key Genes

Sex differences have been suggested to play critical roles in the pathophysiology of osteoarthritis (OA), resulting in sex-specific prevalence and incidence. However, their roles in the development of OA remain largely unknown. The aim of this study was to screen out key genes and pathways mediating biological differences between OA females after menopause and OA males. First, the gene expression data of GSE36700 and GSE55457 were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between sexes were identified using R software, respectively. The overlapping DEGs were obtained. Then, protein-protein interactive (PPI) network was constructed to further analyze interactions between the overlapping DEGs. Finally, enrichment analyses were separately performed using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes tools. In our results, a total of 278 overlapping DEGs were identified between OA females after menopause and OA males, including 219 upregulated and 59 downregulated genes. In the PPI network, seven hub genes were identified, including EGF, ERBB2, CDC42, PIK3R2, LCK, CBL, and STAT1. Functional enrichment analysis revealed that these genes were mainly enriched in PI3K-Akt signaling pathway, osteoclast differentiation, and focal adhesion. In conclusion, the results in the current study suggest that pathways of PI3K-Akt, osteoclast differentiation, and focal adhesion may play important roles in the development of OA females after menopause. EGFR, ERBB2, CDC42, and STAT1 may be key genes related to OA progression in postmenopausal women and may be promising therapeutic targets for OA.

scholarly journals Bioinformatic analysis identifies potential key genes of epilepsy

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0254326
Author(s):  
Yike Zhu ◽  
Dan Huang ◽  
Zhongyan Zhao ◽  
Chuansen Lu
Keyword(s):  
Network Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
P Value ◽  
Ppi Network ◽  
Functional Roles ◽  
Protein Protein Interaction

Background Epilepsy is one of the most common brain disorders worldwide. It is usually hard to be identified properly, and a third of patients are drug-resistant. Genes related to the progression and prognosis of epilepsy are particularly needed to be identified. Methods In our study, we downloaded the Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE143272. Differentially expressed genes (DEGs) with a fold change (FC) >1.2 and a P-value <0.05 were identified by GEO2R and grouped in male, female and overlapping DEGs. Functional enrichment analysis and Protein-Protein Interaction (PPI) network analysis were performed. Results In total, 183 DEGs overlapped (77 ups and 106 downs), 302 DEGs (185 ups and 117 downs) in the male dataset, and 750 DEGs (464 ups and 286 downs) in the female dataset were obtained from the GSE143272 dataset. These DEGs were markedly enriched under various Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. 16 following hub genes were identified based on PPI network analysis: ADCY7, C3AR1, DEGS1, CXCL1 in male-specific DEGs, TOLLIP, ORM1, ELANE, QPCT in female-specific DEGs and FCAR, CD3G, CLEC12A, MOSPD2, CD3D, ALDH3B1, GPR97, PLAUR in overlapping DEGs. Conclusion This discovery-driven study may be useful to provide a novel insight into the diagnosis and treatment of epilepsy. However, more experiments are needed in the future to study the functional roles of these genes in epilepsy.

scholarly journals Identification of ALDH3A2 as novel prognostic biomarker in gastric adenocarcinoma by integrated bioinformatics analysis

2020 ◽  
Author(s):  
Zhenhua Yin ◽  
Dejun Wu ◽  
Xiyi Wei ◽  
Jianping Shi ◽  
Nuyun Jin ◽  
...  
Keyword(s):  
Reference Value ◽  
Enrichment Analysis ◽  
Predictive Marker ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Functional Enrichment ◽  
Protein Protein Interaction ◽  
Cancer Genome Atlas

Abstract Extensive experiments and researches have elucidated that genes plays a pivotal role in tumorigenesis and development. Nonetheless, its latent involvement in gastric carcinoma (GC) remains to be further investigated. In this study, we identified overlapping differentially expressed genes (DEGs) by comparing the tumor tissue and adjacent normal tissue from Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) database, which included 79 up-regulated and 10 down-regulated genes. Based on these genes, functional enrichment analysis, protein-protein interaction (PPI) and prognosis analysis were conducted, and thus the gene ALDH3A2 was chosen for further analysis. Then, we performed Gene Set Enrichment Analysis (GSEA) and immunocorrelation analysis (infiltration, copy number alterations and checkpoints) to comprehend the in-deep mechanism of ALDH3A2. In a word, ALDH3A2 might have potential reference value for the relief and immunotherapy, and become an independent predictive marker for the prognosis of GC.

scholarly journals Identification of Potential Crucial Genes in Atrial Fibrillation: A Bioinformatic Analysis

2020 ◽  
Author(s):  
Junguo Zhang ◽  
Xin Huang ◽  
Xiaojie Wang ◽  
Yanhui Gao ◽  
Li Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Atrial Fibrillation ◽  
Growth Factor ◽  
Microarray Data ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Insulin Like Growth Factor

Abstract Background Atrial fibrillation (AF) is at least partially heritable, affecting 2-3% of the population in Europe and the USA. However, a substantial proportion of heritability is still lacking. In the present study, we aim to identify potential crucial genes associated with AF through bioinformatic analyses of public datasets. Methods Microarray data sets of GSE115574, GSE31821, GSE79768, GSE41177 and GSE14975 from the Gene Expression Omnibus (GEO) database were retrieved. After merging all microarray data and adjusting batch effect, differentially expressed genes (DEGs) were identified. Functional enrichment analyses based on Gene Ontology (GO) resource, Kyoto Encyclopedia of Genes and Genomes (KEGG) resource, Gene Set Enrichment Analysis (GSEA), Reactome Pathway Database and Disease Ontology (DO) were carried out. Protein-protein interaction (PPI) network was constructed using the STRING database. Combined with aforementioned significant bioinformatics information, potential crucial genes were subsequently selected. The comparative toxicogenomics database (CTD) was carried out to explore the interaction between potential crucial genes and AF. Result We identified 27 of DEGs with gene expression fold change (FC) ≥ 1.5 and 5 with FC ≥ 2 of AF patients compared with sinus rhythm controls. The most significantly enriched pathway was regulation of insulin-like growth factor transport and uptake by insulin-like growth factor binding proteins. IGFBP2, C1orf105, FHL2, CHGB, ATP1B4, IGFBP3, SLC26A9, CXCR4 and HTR2B were considered the potential crucial genes. CTD showed CXCR4, IGFBP2, IGFBP3 and FHL2 had higher scores with AF. Conclusions The 9 potential crucial genes, especially CXCR4, IGFBP2, IGFBP3 and FHL2 , may be associated with risk of AF. Our study provided new insights of AF into genetics, molecular pathogenesis and new therapeutic targets.

scholarly journals Screening of Key Genes Associated with Ischemic Stroke via Microarray Data

2013 ◽  
Vol 40 (6) ◽  
pp. 864-869 ◽  
Author(s):  
Jianmin Wang ◽  
Dongliang Zhou ◽  
Hongwei Qin ◽  
Ying Xu ◽  
Ying Guan ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Microarray Data ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Clustering Coefficient ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Reference Database ◽  
Key Genes

Objective:To promote understandings about the pathogenesis of ischemic stroke (IS) through mining key genes, functions and pathways with microarray technology.Methods:Differentially expressed genes (DEGs) in blood between patients with IS and healthy people were screened out through comparing microarray data obtained from Gene Expression Omnibus. Overrepresented functions in DEGs were revealed by Gene Ontology (GO) enrichment analysis. Interaction network was constructed for the top 24 DEGs with information from Human Protein Reference Database (HPRD). Relevant microRNAs (miRNAs) were retrieved from three databases: TargetScan, miRBase and miRanda.Results:A total of 503 DEGs were obtained. Functional enrichment analysis showed that immune response, signaling pathways and apoptosis were significantly over-represented. Six key genes with big degree, betweenness and clustering coefficient were then revealed, which might play important roles in the development of IS. In addition, 57 differentially expressed miRNAs targeting the 6 genes were retrieved.Conclusions:Our study provides insights into the pathogenesis of IS and potential targets to treat the disease.

scholarly journals Identification of Potential Crucial Genes in Atrial Fibrillation: A Bioinformatic Analysis

2020 ◽  
Author(s):  
Junguo Zhang ◽  
Xin Huang ◽  
Xiaojie Wang ◽  
Yanhui Gao ◽  
Li Liu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Atrial Fibrillation ◽  
Growth Factor ◽  
Microarray Data ◽  
Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Insulin Like Growth Factor

Abstract Background Atrial fibrillation (AF) is at least partially heritable, affecting 2-3% of the population in Europe and the USA. However, a substantial proportion of heritability is still lacking. In the present study, we aim to identify potential crucial genes associated with AF through bioinformatic analyses of public datasets.Methods Microarray data sets of GSE115574, GSE31821, GSE79768, GSE41177 and GSE14975 from the Gene Expression Omnibus (GEO) database were retrieved. After merging all microarray data and adjusting batch effect, differentially expressed genes (DEGs) were identified. Functional enrichment analyses based on Gene Ontology (GO) resource, Kyoto Encyclopedia of Genes and Genomes (KEGG) resource, Gene Set Enrichment Analysis (GSEA), Reactome Pathway Database and Disease Ontology (DO) were carried out. Protein-protein interaction (PPI) network was constructed using the STRING database. Combined with aforementioned significant bioinformatics information, potential crucial genes were subsequently selected. The comparative toxicogenomics database (CTD) was used to explore the interaction between potential crucial genes and AF.Result We identified 27 of DEGs with gene expression fold change (FC) ≥ 1.5 or ≤ 2/3 (|log2 FC| ≥ 0.58) and 5 with FC ≥ 2 or ≤ 0.5 (|log2 FC| ≥ 1) of AF patients compared with sinus rhythm controls. The most significantly enriched pathway was regulation of insulin-like growth factor transport and uptake by insulin-like growth factor binding proteins. IGFBP2, C1orf105, FHL2, CHGB, ATP1B4, IGFBP3, SLC26A9, CXCR4 and HTR2B were considered the potential crucial genes. CTD showed CXCR4, IGFBP2, IGFBP3 and FHL2 had higher scores with AF.Conclusions The 9 potential crucial genes, especially CXCR4, IGFBP2, IGFBP3 and FHL2, may be associated with risk of AF. Our study provided new insights of AF into genetics, molecular pathogenesis and new therapeutic targets.

Sign in / Sign up

Export Citation Format

Share Document