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Identification of key genes in atrial fibrillation using bioinformatics analysis.

ABSTRACT: BACKGROUND:Atrial fibrillation (AF) is one of the most common arrhythmia, which brings huge burden to the individual and the society. However, the mechanism of AF is not clear. This paper aims at screening the key differentially expressed genes (DEGs) of atrial fibrillation and to construct enrichment analysis and protein-protein interaction (PPI) network analysis for these DEGs. METHODS:The datasets were collected from the Gene Expression Omnibus database to extract data of left atrial appendage (LAA) RNA of patients with or without AF in GSE79768, GSE31821, GSE115574, GSE14975 and GSE41177. Batch normalization, screening of the differential genes and gene ontology analysis were finished by R software. Reactome analysis was used for pathway analysis. STRING platform was utilized for PPI network analysis. At last, we performed reverse transcription-quantitative polymerase chain reaction (RT-qPCR) to validate the expression of key genes in 20 sinus rhythm (SR) LAA tissues and 20 AF LAA tissues. RESULTS:A total of 106 DEGs were screened in the merged dataset. Among these DEGs, 74 genes were up-regulated and 32 genes down-regulated. DEGs were mostly enriched in extracellular matrix organization, protein activation cascade and extracellular structure organization. In PPI network, we identified SPP1, COL5A1 and VCAN as key genes which were associated with extracellular matrix. RT-qPCR showed the same expression trend of the three key genes as in our bioinformatics analysis. The expression levels of SPP1, COL5A1 and VCAN were increased in AF tissues compared to SR tissues (P?

SUBMITTER: Liu Y

PROVIDER: S-EPMC7419195 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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Identification of key genes in atrial fibrillation using bioinformatics analysis.

Liu Yueheng Y Tang Rui R Zhao Ye Y Jiang Xuan X Wang Yuchao Y Gu Tianxiang T

BMC cardiovascular disorders 20200810 1

<h4>Background</h4>Atrial fibrillation (AF) is one of the most common arrhythmia, which brings huge burden to the individual and the society. However, the mechanism of AF is not clear. This paper aims at screening the key differentially expressed genes (DEGs) of atrial fibrillation and to construct enrichment analysis and protein-protein interaction (PPI) network analysis for these DEGs.<h4>Methods</h4>The datasets were collected from the Gene Expression Omnibus database to extract data of left ...[more]

PMID: 32778054

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Project description:BackgroundAtrial fibrillation (AF) is the most common persistent arrhythmia. Valvular heart disease (VHD) and AF frequently coexist. In our study, from performing bioinformatics analysis, we sought to identify immune-related genes (IRGs) and explore the role of immune cell infiltration in AF-VHD in depth, aiming at investigating the potential molecular mechanism and developing new therapeutic targets for AF, including AF-VHD.MethodsThe gene expression of the GSE41177 and GSE79768 datasets were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were analyzed via the limma package in Bioconductor with R software. Differentially expressed immune-related genes (DEIRGs) were selected via combination ImmPort database with DEGs, and the enrichment function and pathway analysis were explored. A protein-protein interaction (PPI) network was built with a Search Tool for the Retrieval of Interacting Genes/Proteins plugin in Cytoscape. The CIBERSORT algorithm was used to evaluate immune infiltration in the left atrial (LA) tissues between AF-VHD and sinus rhythm (SR) patients. Finally, a correlation analysis between key DEIRGs and infiltrating immune cells was performed.ResultsA total of 130 DEIRGs were detected. Enrichment function of DEIRGs demonstrated that they are significant in immune and inflammatory responses. The key DEIRGs assessed by the PPI network and involved in both the immune and inflammatory responses were the C-X-C motif chemokine ligand (CXCL) 1, pro-platelet basic protein (PPBP), CXCL12, and C-C motif chemokine ligand 4 (CCL4). The immune infiltration findings indicated that, compared with the LA tissues from SR patients, the tissues from AF-VHD patients contained a higher proportion of gamma delta T cells, but a lower proportion of CD8 and regulatory T cells. The results of correlation analysis demonstrated that CXCL1 was positively correlated with activated mast cells and significantly negatively correlated with resting mast cells. PPBP, CXCL12, and CCL4 were positively correlated with the infiltration of various immune cells, such as neutrophils, plasma cells, and resting dendritic cells.ConclusionsThe key immune-related genes and the differences in immune infiltration in LA tissues play an essential role in the occurrence and progression of AF-VHD.

Project description:Osteosarcoma (OS) is an invasive malignant neoplasm of the bones. The present study identified and analyzed key genes associated with OS. Expression profiling of the dataset GSE49003, which included 6 metastatic and 6 non-metastatic OS cell lines and was obtained from the Gene Expression Omnibus, was performed. Following data preprocessing, the differentially expressed genes (DEGs) were selected using the limma package in R. Subsequently, bidirectional hierarchical clustering using the pheatmap package in R and an unpaired Students' t-test was performed for the DEGs. Based on the Search Tool for the Retrieval of Interacting Genes database and Cytoscape software, a protein-protein interaction (PPI) network for the DEGs was constructed. Using Database for Annotation, Visualization and Integrated Discovery software and the Kyoto Encyclopedia of Genes and Genomes Orthology Based Annotation System server, functional and pathway enrichment analyses were performed for the DEGs corresponding to the proteins of the network. In addition, the transcription factors (TFs) and CpG islands of the gene promoter were searched for using the TRANSFAC database and CpG Island Searcher software, respectively. A total of 323 DEGs were identified between the metastatic and non-metastatic samples. In the PPI network, upregulated epidermal growth factor receptor (EGFR) exhibits a high degree and was therefore highly interconnected with other proteins. Enrichment analysis revealed that EGFR was enriched in cytoskeleton organization, organic substance response and the signaling pathway of focal adhesion. The TFs early growth response 1, nuclear factor-?B complex subunits, peroxisome proliferator activated receptor ?, signal transducer and activator of transcription 3 and MYC proto-oncogene were identified in the EGFR promoter region. Furthermore, multiple CpG islands, starting from the 400 bp of the EGFR promoter sequence, were predicted. Methylated modification of the CpG islands in the EGFR promoter may help to regulate EGFR expression. The TFs identified in the EGFR promoter may function in the progression of OS.

Project description:IntroductionAtrial fibrillation (AF) is the most prevalent sustained cardiac arrhythmia, but the molecular mechanisms underlying AF are not known. We aimed to identify the pivotal genes and pathways involved in AF pathogenesis because they could become potential biomarkers and therapeutic targets of AF.MethodsThe microarray datasets of GSE31821 and GSE41177 were downloaded from the Gene Expression Omnibus database. After combining the two datasets, differentially expressed genes (DEGs) were screened by the Limma package. MicroRNAs (miRNAs) confirmed experimentally to have an interaction with AF were screened through the miRTarBase database. Target genes of miRNAs were predicted using the miRNet database, and the intersection between DEGs and target genes of miRNAs, which were defined as common genes (CGs), were analyzed. Functional and pathway-enrichment analyses of DEGs and CGs were performed using the databases DAVID and KOBAS. Protein-protein interaction (PPI) network, miRNA- messenger(m) RNA network, and drug-gene network was visualized. Finally, reverse transcription quantitative real-time polymerase chain reaction (RT-qPCR) was used to validate the expression of hub genes in the miRNA-mRNA network.ResultsThirty-three CGs were acquired from the intersection of 65 DEGs from the integrated dataset and 9777 target genes of miRNAs. Fifteen "hub" genes were selected from the PPI network, and the miRNA-mRNA network, including 82 miRNAs and 9 target mRNAs, was constructed. Furthermore, with the validation by RT-qPCR, macrophage migration inhibitory factor (MIF), MYC proto-oncogene, bHLH transcription factor (MYC), inhibitor of differentiation 1 (ID1), and C-X-C Motif Chemokine Receptor 4 (CXCR4) were upregulated and superoxide Dismutase 2 (SOD2) was downregulated in patients with AF compared with healthy controls. We also found MIF, MYC, and ID1 were enriched in the transforming growth factor (TGF)-β and Hippo signaling pathway.ConclusionWe identified several pivotal genes and pathways involved in AF pathogenesis. MIF, MYC, and ID1 might participate in AF progression through the TGF-β and Hippo signaling pathways. Our study provided new insights into the mechanisms of action of AF.

Project description:Gastric cancer (GC) is one of the most common types of cancer worldwide. Patients must be identified at an early stage of tumor progression for treatment to be effective. The aim of the present study was to identify potential biomarkers with diagnostic value in patients with GC. To examine potential therapeutic targets for GC, four Gene Expression Omnibus (GEO) datasets were downloaded and screened for differentially expressed genes (DEGs). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were subsequently performed to study the function and pathway enrichment of the identified DEGs. A protein-protein interaction (PPI) network was constructed. The CytoHubba plugin of Cytoscape was used to calculate the degree of connectivity of proteins in the PPI network, and the two genes with the highest degree of connectivity were selected for further analysis. Additionally, the two DEGs with the largest and smallest log Fold Change values were selected. These six key genes were further examined using Oncomine and the Kaplan-Meier plotter platform. A total of 99 upregulated and 172 downregulated genes common to all four GEO datasets were screened. The DEGs were primarily enriched in the Biological Process terms: 'extracellular matrix organization', 'collagen catabolic process' and 'cell adhesion'. These three KEGG pathways were significantly enriched in the categories: 'ECM-receptor interaction', 'protein digestion and absorption', and 'focal adhesion'. Based on Oncomine, expression of ATP4A and ATP4B were downregulated in GC, whereas expression of the other genes were all upregulated. The Kaplan-Meier plotter platform confirmed that upregulated expression of the identified key genes was significantly associated with worse overall survival of patients with GC. The results of the present study suggest that FN1, COL1A1, INHBA and CST1 may be potential biomarkers and therapeutic targets for GC. Additional studies are required to explore the potential value of ATP4A and ATP4B in the treatment of GC.

Project description:BackgroundThe immune system significantly participates in the pathologic process of atrial fibrillation (AF). However, the molecular mechanisms underlying this participation are not completely explained. The current research aimed to identify critical genes and immune cells that participate in the pathologic process of AF.MethodsCIBERSORT was utilized to reveal the immune cell infiltration pattern in AF patients. Meanwhile, weighted gene coexpression network analysis (WGCNA) was utilized to identify meaningful modules that were significantly correlated with AF. The characteristic genes correlated with AF were identified by the least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine recursive feature elimination (SVM-RFE) algorithm.ResultsIn comparison to sinus rhythm (SR) individuals, we observed that fewer activated mast cells and regulatory T cells (Tregs), as well as more gamma delta T cells, resting mast cells, and M2 macrophages, were infiltrated in AF patients. Three significant modules (pink, red, and magenta) were identified to be significantly associated with AF. Gene enrichment analysis showed that all 717 genes were associated with immunity- or inflammation-related pathways and biological processes. Four hub genes (GALNT16, HTR2B, BEX2, and RAB8A) were revealed to be significantly correlated with AF by the SVM-RFE algorithm and LASSO logistic regression. qRT-PCR results suggested that compared to the SR subjects, AF patients exhibited significantly reduced BEX2 and GALNT16 expression, as well as dramatically elevated HTR2B expression. The AUC measurement showed that the diagnostic efficiency of BEX2, HTR2B, and GALNT16 in the training set was 0.836, 0.883, and 0.893, respectively, and 0.858, 0.861, and 0.915, respectively, in the validation set.ConclusionsThree novel genes, BEX2, HTR2B, and GALNT16, were identified by WGCNA combined with machine learning, which provides potential new therapeutic targets for the early diagnosis and prevention of AF.

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Identification of key genes in atrial fibrillation using bioinformatics analysis.

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Identification of key genes in atrial fibrillation using bioinformatics analysis.

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