Bioinformatics analysis of key genes and signaling pathways associated with myocardial infarction following telomerase activation

Abstract Although the pathogenesis of type 1 diabetes mellitus (T1D) and acute myocardial infarction (AMI) remains unclear. We investigated the key genes and signaling pathways common to T1D and AMI. First, we screened differentially expressed genes (DEGs) co-expressed by T1D and AMI through gene expression synthesis (GEO) database and text mining. David database was used for enrichment and functional analysis of selected genes. The interaction between proteins (PPI) was created using STRING and Cytoscape software. MCODE is used for module analysis of PPI network. A total of 74 human genes that met the criteria were found in T1D and AMI. The first 10 central genes include STAT3, ITGAM, MMP9, ERBB2, MAPK3, FOS, MYD88, MAPK1, TFRC and TNFRSF1A.The establishment of the aforementioned key genes might serve as novel biomarkers for precision diagnosis and providing medical treatment for the occurrence of AMI in T1D patients in the future.

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Identification of Key Genes and Signaling Pathways Involved in Acute Myocardial Infarction and Potential Biomarkers of Its Consequent Heart Failure Based On Bioinformatics Analysis

10.21203/rs.3.rs-827195/v1 ◽

2021 ◽

Author(s):

Yuyan Xiong ◽

Yuejin Yang

Keyword(s):

Myocardial Infarction ◽

Heart Failure ◽

Acute Myocardial Infarction ◽

Ventricular Remodeling ◽

Bioinformatics Analysis ◽

Roc Curves ◽

Functional Enrichment ◽

Ppi Network ◽

Key Genes ◽

Potential Biomarkers

Abstract Backgrounds : Acute myocardial infarction (AMI) is the predominant cause of cardiac death and ischemic heart failure (IHF) worldwide in coronary artery disease (CAD). Although it results from coronary acute occlusion, we in the study explored some key genes involved in the development of AMI and consequent IHF using bioinformatics analysis. Methods Utilizing expression data of 52 patients with AMI and 53 controls from GSE66360 and GSE97320 datasets, we screened shared differentially expressed genes (DEGs) in the independent datasets. Functional enrichment analysis and protein-protein interaction (PPI) network were employed. GSE58967 of 111 AMI patients and 46 controls was used to validate the shared DEGs and further analyzed to identify the DEGs in AMI patients with and without heart failure (HF) with the dynamic changes also being evaluated. The receiver operating characteristic (ROC) curves and area under the curve (AUC) were used to validate the diagnostic efficiency. Results In the comparison of AMI patients with controls, we identified 105 shared DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed the shared DEGs mainly enriched in immune-related inflammation process and pathways. Filtered with PPI network, 5 genes of CXCL8, CXCL1, MMP9, FPR1 and TLR2 were considered as hub genes, which were further validated in GSE59867. Compared with the genes in AMI patients without HF, those of TNFAIP6, ADM, TRIB1, AQP9 and IL1R2 associated with ventricular remodeling were found to be significantly high expressed in patients with HF on admission with the AUC of ROC curves was 0.792–0.847 (all p < 0.05), which can be used as the potential biomarkers for early prediction of HF after AMI. Conclusions These findings based on integrated bioinformatic analysis provide new insights into the important roles of genes to play in the patients with AMI and consequent HF.

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Identification of Differentially Expressed Genes for Disc Degeneration Using Gene Expression Profiling and Bioinformatics Analysis

10.21203/rs.3.rs-508157/v1 ◽

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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Identification of Key Genes and Pathways in First Acute Myocardial Infarction Based on Gene Expression Profiling by Bioinformatics Analysis

10.31487/j.jicoa.2019.02.02 ◽

2019 ◽

Author(s):

Xiao-Li Xie

Keyword(s):

Gene Expression ◽

Myocardial Infarction ◽

Acute Myocardial Infarction ◽

Gene Expression Profiling ◽

Expression Profiling ◽

Bioinformatics Analysis ◽

Key Genes

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Identification of potential key genes for SARS-CoV-2 infected human bronchial organoids based on bioinformatics analysis

10.1101/2020.08.18.256735 ◽

2020 ◽

Author(s):

Hanming Gu ◽

Gongsheng Yuan

Keyword(s):

Signaling Pathways ◽

Bioinformatics Analysis ◽

Homo Sapiens ◽

Expression Profiles ◽

Gene Expression Profiles ◽

Receptor Interaction ◽

Functional Categories ◽

P53 Signaling ◽

Key Genes ◽

P53 Signaling Pathway

AbstractThere is an urgent need to understand the pathogenesis of the severe acute respiratory syndrome coronavirus clade 2 (SARS-CoV-2) that leads to COVID-19 and respiratory failure. Our study is to discover differentially expressed genes (DEGs) and biological signaling pathways by using a bioinformatics approach to elucidate their potential pathogenesis. The gene expression profiles of the GSE150819 datasets were originally produced using an Illumina NextSeq 500 (Homo sapiens). KEGG (Kyoto Encyclopedia of Genes and Genomes) and GO (Gene Ontology) were utilized to identify functional categories and significant pathways. KEGG and GO results suggested that the Cytokine-cytokine receptor interaction, P53 signaling pathway, and Apoptosis are the main signaling pathways in SARS-CoV-2 infected human bronchial organoids (hBOs). Furthermore, NFKBIA, C3, and CCL20 may be key genes in SARS-CoV-2 infected hBOs. Therefore, our study provides further insights into the therapy of COVID-19.

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Identification of key genes and pathways associated with Crohn’s disease by bioinformatics analysis

10.1101/543835 ◽

2019 ◽

Author(s):

Zheng Wang ◽

Jie Zhu ◽

Lixian Ma

Keyword(s):

Gene Expression ◽

Crohn’S Disease ◽

Crohn's Disease ◽

Signaling Pathways ◽

Bioinformatics Analysis ◽

Integrated Analysis ◽

Functional Modules ◽

Disease Pathogenesis ◽

Black Module ◽

Key Genes

AbstractCrohn’s disease is a type of inflammatory bowel disease posing a significant threat to human health all over the world. Genome-wide gene expression profiles of mucosal colonic biopsies have provided some insight into the molecular mechanisms of Crohn’s disease. However, the exact pathogenesis is unclear. This study aimed to identify key genes and significant signaling pathways associated with Crohn’s disease by bioinformatics analysis. To identify key genes, an integrated analysis of gene expression signature was conducted using a robust rank aggregation approach. A total of 179 Crohn’s disease patients and 94 healthy controls from nine public microarray datasets were included. MMP1 and CLDN8 were two key genes screened from the differentially expressed genes. Connectivity Map predicted several small molecules as possible adjuvant drugs to treat CD. Besides, we used weighted gene co-expression network analysis to explore the co-expression modules associated with Crohn’s disease pathogenesis. Seven main functional modules were identified, of which black module showed the highest correlation with Crohn’s disease. The genes in black module mainly enriched in Interferon Signaling and defense response to virus. Blue module was another important module and enriched in several signaling pathways, including extracellular matrix organization, inflammatory response and blood vessel development. There were also several other meaningful functional modules which enriched in many biological processes. The present study identified a number of key genes and pathways correlated with Crohn’s disease and potential drugs to combat it, which might offer insights into Crohn’s disease pathogenesis and provide a clue to potential treatments.

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