scholarly journals Hub Targets Analysis of miRNA-mRNA-TF Network in Diabetic Cardiomyopathy

2021 ◽  
Author(s):  
Bincheng Ren ◽  
Kaini He ◽  
Miao Yuan ◽  
Yu Wang ◽  
Yuanyuan Tie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Diabetic Cardiomyopathy ◽  
Bioinformatics Analysis ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Vital Role ◽  
Data Sets ◽  
Go Enrichment ◽  
Geo Database

Abstract Background: The pathogenic mechanism and development of the diabetic cardiomyopathy(DCM) has been generally explained, and it is clear that the microRNAs(miRNAs), mRNAs and transcription factors(TFs) participate in the process of the DCM disease. Yet, the hub targets of the disease progression are not clear.Methods: To figure out the problem, we downloaded data sets from the Gene Expression Omnibus(GEO) database (GSE44179 and GSE4745). The targeted mRNAs of miRNAs were downloaded from TargetScan, miRBD and microT-CDS database. Gene Ontology (GO) enrichment of miRNAs and mRNAs were analysed in DAVID.R studio software was used to visualize the results of screened targets and GO enrichment. Cytoscape software was used to visualize the miRNA-mRNA-TF interaction network and calculate the hub targets. Results: We filtered eight miRNAs, nine mRNAs and ten transcription factors(TFs) by bioinformatics analysis, and constructed a miRNA-mRNA-TF network. The top ten degrees of nodes in the network are rno-miR-7a, Hnf4a, rno-miR-17, rno-miR-21, rno-miR-122, rno-miR-200c, Med1, Mlxipl, SP1 and rno-miR-34a, which were closely related to the process of DCM. Conclusion: This study revealed that rno-miR-7a, Hnf4a, rno-miR-17and rno-miR-21 may play vital role in the progress of diabetic cardiomyopathy.

scholarly journals Identification of Hub Genes and Key Pathways of Paraquat Induced Human Embryonic Pulmonary Fibrosis by Bioinformatics Analysis and Vitro Experiments

2020 ◽  
Author(s):  
Yue Fu ◽  
Xiang Xia Zeng ◽  
Jin Lun Hu ◽  
Mei Yan ◽  
CHun Ming Xie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pulmonary Fibrosis ◽  
Lung Fibrosis ◽  
Bioinformatics Analysis ◽  
Core Promoter ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Hub Genes

Abstract Background: Paraquat is highly toxic pesticide, which usually led to acute lung injury and subsequently develop pulmonary fibrosis, the exact mechanisms of PQ-induced lung fibrosis remain largely unclear and no specific drugs for this disease have been approved. Methods: Our study aimed to identify its potential mechanism though modeling study in vitro and bioinformatics analysis. Gene expression datasets associated with PQ-induced lung fibrosis were obtained from the Gene Expression Omnibus and differentially expressed genes (DEGs) were identified using GEO2R. Functional enrichment analyses were performed using the Database for Annotation. Results: The DEGs in the two datasets, of which 92 overlapping genes were found in two microarray datasets. Functional analysis demonstrated that the 92 DEGs were enriched in the ‘TNF signaling pathway’, ‘CXCR chemokine receptor binding’, and ‘core promoter binding’. Moreover, nine hub genes were identified from a protein‑protein interaction network. Conclusions: This integrative analysis firstly identified candidate genes and pathways in PQ-induced lung fibrosis, as well as benefit to research novel approaches for treating for control of PQ-induced pulmonary fibrosis.

scholarly journals Prognostic values and prospective pathway signaling of miR-126 in non-small cell lung cancer: a study based on gene expression omnibus and bioinformatics analysis

2020 ◽  
Author(s):  
Zichen Jiao ◽  
Ao Yu ◽  
Xiaofeng He ◽  
Yulong Xuan ◽  
He Zhang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Gene Expression Omnibus ◽  
Data Sets ◽  
Hub Genes ◽  
Biological Regulation ◽  
Protein Protein Interaction ◽  
Kegg Pathways ◽  
Go Enrichment ◽  
Nsclc Patients

Abstract Objective MiRNAs are considered to be crucial for NSCLC’s initiation and development. MiRNAs have been widely identified in NSCLC. However, the role of miR-126 in NSCLC has not been fully explained.Methods miR-126 Expression in NSCLC was evaluated by analyzing the common data sets in Gene Expression Omnibus(GEO) database and reviewing former thesis papers. Three mRNA datasets, GSE18842, GSE19804 and GSE101929, from GEO to indentify the differentially expressed genes (DEG). We prognosed the target genes of hsa-miR-126-5p using TargetScan and analyzed the gene overlap between the target genes of miR-126 and DEG in NSCLC. Subsequently, we analyzed Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. We used STRING and Cytoscape to construct a protein-protein interaction (PPI) network, and analyzed the influence of HUB gene on the prognosis of NSCLC.Results A common pattern of mir-126 downregulation in NSCLC was identified in the literature review. A total of 187 DEGs were identified, both NSCLC-related and miR-126-related. Many DEGs are extendedly enriched in cell membranes, signal receptor binding, and biological regulation. Among the 10 main Hub genes analyzed by PPI, 4 HUB genes (NCAP-G,MELK,KIAA0101,TPX2) were obviously related to the poor recuperation of NSCLC patients. When these genes highly expressed, survival rate of NSCLC patients was low. Furthermore, we identified the recessive miR-126-related genes that may be involved in NSCLC, such as TPX2, HMMR, and ANLN through network analysis.Conclusion this study suggests that mir-126 is radical for the biological processing of NSCLC.

scholarly journals COnTORT: COmprehensive Transcriptomic ORganizational Tool for Simultaneously Retrieving and Organizing Numerous Gene Expression Data Sets from the NCBI Gene Expression Omnibus Database

2020 ◽  
Vol 9 (25) ◽  
Author(s):  
Kevin S. Myers ◽  
Michael Place ◽  
Daniel R. Noguera ◽  
Timothy J. Donohue
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Gene Expression Omnibus ◽  
Data Sets ◽  
Expression Data ◽  
Gene Expression Omnibus Database ◽  
Ncbi Gene Expression Omnibus ◽  
Geo Database

ABSTRACT We introduce COnTORT (COmprehensive Transcriptomic ORganizational Tool), a publicly available program that retrieves all available gene expression data and associated metadata for an organism from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. The data are compiled into text files that can be used for downstream bioinformatic applications.

scholarly journals FRI0584 SCREENING AND IDENTIFICATION OF BIOMARKERS IN MYOSITIS

2020 ◽  
Vol 79 (Suppl 1) ◽  
pp. 896-897
Author(s):  
W. Liu ◽  
X. Zhang
Keyword(s):  
Gene Expression ◽  
Interstitial Lung Disease ◽  
Lung Disease ◽  
Molecular Mechanisms ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network ◽  
Geo Database

Background:Myositis, including dermatomyositis and polymyositis, is autoimmune disorders that is characterized by muscle degeneration in the proximal extremities, with the complications of weakness of muscles, interstitial lung disease and vascular lesions, even leading to death in an acute progressive process[1,2]. However, the molecular mechanisms of myositis are rarely understood.Objectives:Identify the candidate genes in myositis.Methods:Microarray datasets GSE128470, GSE48280 and GSE39454 were extracted from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) and function enrichment analyses were conducted. The protein-protein interaction network and the analyses of hub genes were performed with STRING and Cytoscape.Results:There were 98 DEGs, of which the function and pathways enrichment analyses showed defense response, immune response, response to virus, inflammatory response, response to wounding, cell adhesion, cell proliferation, cell death and macromolecule metabolic process. 20 hub genes were identified, of which 7 including IRF9 TRIM22 MX2 IFITM1 IFI6 IFI44 IFI44L had not been reported in the literature, related to the response to virus, immune response, transcription from RNA polymerase II promoter, cell apoptosis, cell death. The verification analysis about the 7 genes in GSE128314 showed significant differences in myositis.Conclusion:In conclusion, DEGs and hub genes identified in our study showed the potential molecular mechanisms in myositis, providing the helpful targets for diagnosis and clinical strategy of myositis.References:[1] Wu H, Geng D, Xu J. An approach to the development of interstitial lung disease in dermatomyositis: a study of 230 cases in China[J]. Journal of International Medical Research. 2013;41(2):493–501.[2] Fathi M, Dastmalchi M, Rasmussen E, Lundberg IE, Tornling G. Interstitial lung disease, a common manifestation of newly diagnosed polymyositis and dermatomyositis[J]. Annals of the Rheumatic Diseases. 2004;63(3):297–301.Figure 1.The protein-protein interaction network of 20 hub genesFigure 2.7 genes in GSE128314 showed significant differences in myositisAcknowledgments:The authors acknowledge the efforts of the Gene Expression Omnibus (GEO) database. The interpretation and reporting of these data are the sole responsibility of the authors.Disclosure of Interests:None declared

scholarly journals Integrate analysis and identification for different expression genes in chondrogenesis

2020 ◽  
Author(s):  
Keda Liu ◽  
Nanjue Cao ◽  
Yuhe Zhu ◽  
Wei Wang
Keyword(s):  
Interaction Network ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Data Sets ◽  
Protein Protein Interaction ◽  
Related Data ◽  
Integrate Analysis ◽  
And Function ◽  
Microarray Datasets ◽  
Geo Database

Abstract Background: The intricate mechanisms of articular chondrogenesis are largely unknown. Gradually, with the help of high-throughput platforms, microarrays have become an important and useful method to testify hub genes in desease. Today, advanced bioinformatic analysis of available microarray data can provide more reliable and accurate screening results by duplicating related data sets. Results: Microarray datasets GSE9451 and GSE104113 were downloaded from Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were performed, and function enrichment analyses were demonstrated. The protein-protein interaction network (PPI) was constructed and the module analysis was performed by using STRING and Cytoscape. Quantitative PCR was used to confirm the results of bioinformatics analysis. Conclusion: Compared to individual studies, this study can provide extra reliable and accurate screening results by duplicating relevant records. Additional molecular experiments are required to confirm the discovery of candidate genes identified by chondrogenesis. S100A4 is predicted to integrate with miR-325-3p to promote osteogenesis.

scholarly journals Study of Osteoarthritis-Related Hub Genes Based on Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Zhengqing Zhu ◽  
Lei Zhong ◽  
Ronghang Li ◽  
Yuzhe Liu ◽  
Xiangrun Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Bioinformatics Analysis ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Protein Protein Interaction ◽  
Polymerase Chain ◽  
Upregulated Genes ◽  
Protein Protein Interaction Network

Osteoarthritis (OA) is a common cause of morbidity and disability worldwide. However, the pathogenesis of OA is unclear. Therefore, this study was conducted to characterize the pathogenesis and implicated genes of OA. The gene expression profiles of GSE82107 and GSE55235 were downloaded from the Gene Expression Omnibus database. Altogether, 173 differentially expressed genes including 68 upregulated genes and 105 downregulated genes in patients with OA were selected based on the criteria of ∣log fold‐change∣>1 and an adjusted p value < 0.05. Protein-protein interaction network analysis showed that FN1, COL1A1, IGF1, SPP1, TIMP1, BGN, COL5A1, MMP13, CLU, and SDC1 are the top ten genes most closely related to OA. Quantitative reverse transcription-polymerase chain reaction showed that the expression levels of COL1A1, COL5A1, TIMP1, MMP13, and SDC1 were significantly increased in OA. This study provides clues for the molecular mechanism and specific biomarkers of OA.

Decoding Psoriasis: Integrated Bioinformatics Approach to Understand Hub Genes and Involved Pathways

2020 ◽  
Vol 26 (29) ◽  
pp. 3619-3630
Author(s):  
Saumya Choudhary ◽  
Dibyabhaba Pradhan ◽  
Noor S. Khan ◽  
Harpreet Singh ◽  
George Thomas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Gene ◽  
Therapeutic Targets ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Keratinocyte Differentiation ◽  
Hub Genes ◽  
Lesional Skin ◽  
Ncbi Gene Expression Omnibus

Background: Psoriasis is a chronic immune mediated skin disorder with global prevalence of 0.2- 11.4%. Despite rare mortality, the severity of the disease could be understood by the accompanying comorbidities, that has even led to psychological problems among several patients. The cause and the disease mechanism still remain elusive. Objective: To identify potential therapeutic targets and affecting pathways for better insight of the disease pathogenesis. Method: The gene expression profile GSE13355 and GSE14905 were retrieved from NCBI, Gene Expression Omnibus database. The GEO profiles were integrated and the DEGs of lesional and non-lesional psoriasis skin were identified using the affy package in R software. The Kyoto Encyclopaedia of Genes and Genomes pathways of the DEGs were analyzed using clusterProfiler. Cytoscape, V3.7.1 was utilized to construct protein interaction network and analyze the interactome map of candidate proteins encoded in DEGs. Functionally relevant clusters were detected through Cytohubba and MCODE. Results: A total of 1013 genes were differentially expressed in lesional skin of which 557 were upregulated and 456 were downregulated. Seven dysregulated genes were extracted in non-lesional skin. The disease gene network of these DEGs revealed 75 newly identified differentially expressed gene that might have a role in development and progression of the disease. GO analysis revealed keratinocyte differentiation and positive regulation of cytokine production to be the most enriched biological process and molecular function. Cytokines -cytokine receptor was the most enriched pathways. Among 1013 identified DEGs in lesional group, 36 DEGs were found to have altered genetic signature including IL1B and STAT3 which are also reported as hub genes. CCNB1, CCNA2, CDK1, IL1B, CXCL8, MKI 67, ESR1, UBE2C, STAT1 and STAT3 were top 10 hub gene. Conclusion: The hub genes, genomic altered DEGs and other newly identified differentially dysregulated genes would improve our understanding of psoriasis pathogenesis, moreover, the hub genes could be explored as potential therapeutic targets for psoriasis.

scholarly journals Transcriptome Analysis of Hypertrophic Heart Tissues from Murine Transverse Aortic Constriction and Human Aortic Stenosis Reveals Key Genes and Transcription Factors Involved in Cardiac Remodeling Induced by Mechanical Stress

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Peng Yu ◽  
Baoli Zhang ◽  
Ming Liu ◽  
Ying Yu ◽  
Ji Zhao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Aortic Stenosis ◽  
Mechanical Stress ◽  
Cardiac Remodeling ◽  
Interaction Network ◽  
Cytokine Signaling ◽  
Transverse Aortic Constriction ◽  
Aortic Constriction ◽  
Genomic Changes

Background. Mechanical stress-induced cardiac remodeling that results in heart failure is characterized by transcriptional reprogramming of gene expression. However, a systematic study of genomic changes involved in this process has not been performed to date. To investigate the genomic changes and underlying mechanism of cardiac remodeling, we collected and analyzed DNA microarray data for murine transverse aortic constriction (TAC) and human aortic stenosis (AS) from the Gene Expression Omnibus database and the European Bioinformatics Institute. Methods and Results. The differential expression genes (DEGs) across the datasets were merged. The Venn diagrams showed that the number of intersections for early and late cardiac remodeling was 74 and 16, respectively. Gene ontology and protein–protein interaction network analysis showed that metabolic changes, cell differentiation and growth, cell cycling, and collagen fibril organization accounted for a great portion of the DEGs in the TAC model, while in AS patients’ immune system signaling and cytokine signaling displayed the most significant changes. The intersections between the TAC model and AS patients were few. Nevertheless, the DEGs of the two species shared some common regulatory transcription factors (TFs), including SP1, CEBPB, PPARG, and NFKB1, when the heart was challenged by applied mechanical stress. Conclusions. This study unravels the complex transcriptome profiles of the heart tissues and highlighting the candidate genes involved in cardiac remodeling induced by mechanical stress may usher in a new era of precision diagnostics and treatment in patients with cardiac remodeling.

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