Identification of key genes in allergic rhinitis by bioinformatics analysis.
Ontology highlight
ABSTRACT: Objective
This study aimed to explore the potential molecular mechanism of allergic rhinitis (AR) and identify gene signatures by analyzing microarray data using bioinformatics methods.Methods
The dataset GSE19187 was used to screen differentially expressed genes (DEGs) between samples from patients with AR and healthy controls. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were applied for the DEGs. Subsequently, a protein-protein interaction (PPI) network was constructed to identify hub genes. GSE44037 and GSE43523 datasets were screened to validate critical genes.Results
A total of 156 DEGs were identified. GO analysis verified that the DEGs were enriched in antigen processing and presentation, the immune response, and antigen binding. KEGG analysis demonstrated that the DEGs were enriched in Staphylococcus aureus infection, rheumatoid arthritis, and allograft rejection. PPI network and module analysis predicted seven hub genes, of which six (CD44, HLA-DPA1, HLA-DRB1, HLA-DRB5, MUC5B, and CD274) were identified in the validation dataset.Conclusions
Our findings suggest that hub genes play important roles in the development of AR.
SUBMITTER: Zhang Y
PROVIDER: S-EPMC8326637 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:This study aimed to identify allergic rhinitis (AR)-related hub genes and functionally enriched pathways in a murine model. Dataset GSE52804 (including three normal controls and three AR mice) was downloaded from Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were identified. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) analyses of DEGs were performed to identify the hub genes in AR. The DEGs were classified into different modules by using the weighted gene co-expression network analysis (WGCNA). Moreover, to verify the potential hub genes, nasal mucosa tissues were obtained from murine AR models (n = 5) and controls (n = 5), and qRT-PCR and Western blot were performed. In this study, a total of 634 DEGs were identified. They were significantly enriched in 14 GO terms, such as integral component of membrane, plasma membrane, and G-protein-coupled receptor signaling pathway. Meanwhile, there were eight terms of KEGG pathways significantly enriched, such as Olfactory transduction, Cytokine-cytokine receptor interaction, and TNF signaling pathway. The top 10 hub genes (Rtp1, Rps27a, Penk, Cxcl2, Gng8, Gng3, Cxcl1, Cxcr2, Ccl9, and Anxa1) were identified by the PPI network. DEGs were classified into seven modules by WGCNA. According to qRT-PCR validation of the five genes of interest (Rtp1, Rps27a, Penk, Cxcl2, and Anxa1), the expression level of Rtp1 mRNA was significantly decreased in the AR group compared with the control group, while there are enhanced Rps27a, Penk, Cxcl2, and Anxa1 mRNA expressions in the AR mice group compared with the control group. Western blot was also performed to further explore the expression of Anxa1 in the protein level, and the results showed a similar expression trend.
| S-EPMC7477359 | biostudies-literature
Project description:BACKGROUND Rhinitis is the most common clinical manifestation of allergy, affecting more than 400 million people around the world. Rhinitis increases the risk of developing bronchial hyper-responsiveness and asthma. Previous studies have shown that rhinitis is closely related with the physiology, pathology, and pathogenesis of asthma. We analyzed co-expressed genes to explore the relationships between rhinitis and asthma and to find biomarkers of comorbid rhinitis and asthma. MATERIAL AND METHODS Asthma- and rhinitis-related differentially-expressed genes (DEGs) were identified by bioinformatic analysis of GSE104468 and GSE46171 datasets from the Gene Expression Omnibus (GEO) database. After assessment of Gene Ontology (GO) terms and pathway enrichment for DEGs, a protein-protein interaction (PPI) network was conducted via comprehensive target prediction and network analyses. We also evaluated co-expressed DEGs and corresponding predicted miRNAs involved in the developing process of rhinitis and asthma. RESULTS We identified 687 and 1001 DEGs in bronchial and nasal epithelia samples of asthma patients, respectively. For patients with rhinitis, we found 245 DEGs. The hub-genes of PAX6, NMU, NTS, NMUR1, PMCH, and KRT6A may be associated with rhinitis, while CPA3, CTSG, POSTN, CLCA1, HDC, and MUC5B may be involved in asthma. The co-expressed DEGs of BPIFA1, CCL26, CPA3, and CST1, together with corresponding predicted miRNAs (e.g., miR-195-5p and miR-125a-3p) were found to be significantly correlated with rhinitis and asthma. CONCLUSIONS Rhinitis and asthma are related, and there are significant correlations of BPIFA1, CCL26, CPA3, and CST1 genes with novel biomarkers involved in the comorbidity of rhinitis and asthma.
| S-EPMC7278529 | biostudies-literature
Project description:BackgroundStudies have shown that the lipid metabolism mediator leukotriene and prostaglandins are associated with the pathogenesis of allergic rhinitis (AR). The aim of this study was to identify key lipid metabolism-related genes (LMRGs) related to the diagnosis and treatment of AR.Materials and methodsAR-related expression datasets (GSE75011, GSE46171) were downloaded through the Gene Expression Omnibus (GEO) database. First, weighted gene co-expression network analysis (WGCNA) was used to get AR-related genes (ARRGs). Next, between control and AR groups in GSE75011, differentially expressed genes (DEGs) were screened, and DEGs were intersected with LMRGs to obtain lipid metabolism-related differentially expressed genes (LMR DEGs). Protein-protein interaction (PPI) networks were constructed for these LMR DEGs. Hub genes were then identified through stress, radiality, closeness and edge percolated component (EPC) analysis and intersected with the ARRGs to obtain candidate genes. Biomarkers with diagnostic value were screened via receiver operating characteristic (ROC) curves. Differential immune cells screened between control and AR groups were then assessed for correlation with the diagnostic genes, and clinical correlation analysis and enrichment analysis were performed. Finally, real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) was made on blood samples from control and AR patients to validate these identified diagnostic genes.Results73 LMR DEGs were obtained, which were involved in biological processes such as metabolism of lipids and lipid biosynthetic processes. 66 ARRGs and 22 hub genes were intersected to obtain four candidate genes. Three diagnostic genes (LPCAT1, SGPP1, SMARCD3) with diagnostic value were screened according to the AUC > 0.7, with markedly variant between control and AR groups. In addition, two immune cells, regulatory T cells (Treg) and T follicular helper cells (TFH), were marked variations between control and AR groups, and SMARCD3 was significantly associated with TFH. Moreover, SMARCD3 was relevant to immune-related pathways, and correlated significantly with clinical characteristics (age and sex). Finally, RT-qPCR results indicated that changes in the expression of LPCAT1 and SMARCD3 between control and AR groups were consistent with the GSE75011 and GSE46171.ConclusionLPCAT1, SGPP1 and SMARCD3 might be used as biomarkers for AR.
| S-EPMC10362667 | biostudies-literature
Project description: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?<?0.05). CONCLUSION:According to the analyses which were conducted by bioinformatics tools, genes related to extracellular matrix were involved in pathology of AF and may become the possible targets for the diagnosis and treatment of AF.
| S-EPMC7419195 | biostudies-literature
Project description:Gastric cancer (GC) is one of the most common malignancies of the digestive system with few genetic markers for its early detection and prevention. In this study, differentially expressed genes (DEGs) were analyzed using GEO2R from GSE54129 and GSE13911 of the Gene Expression Omnibus (GEO). Then, gene enrichment analysis, protein-protein interaction (PPI) network construction, and topological analysis were performed on the DEGs by the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, STRING, and Cytoscape. Finally, we performed survival analysis of key genes through the Kaplan-Meier plotter. A total of 1034 DEGs were identified in GC. GO and KEGG results showed that DEGs mainly enriched in plasma membrane, cell adhesion, and PI3K-Akt signaling pathway. Subsequently, the PPI network with 44 nodes and 333 edges was constructed, and 18 candidate genes in the network were focused on by centrality analysis and module analysis. Furthermore, data showed that high expressions of fibronectin 1(FN1), the tissue inhibitor of metalloproteinases 1 (TIMP1), secreted phosphoprotein 1 (SPP1), apolipoprotein E (APOE), and versican (VCAN) were related to poor overall survivals in GC patients. In summary, this study suggests that FN1, TIMP1, SPP1, APOE, and VCAN may act as the key genes in GC.
| S-EPMC7525308 | biostudies-literature
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.
| S-EPMC5778824 | biostudies-literature
Project description:Allergic rhinitis (AR) is increasingly becoming a patient self-managed disease. Just under 70% of patients purchasing pharmacotherapy self-select their treatment with no health-care professional intervention often resulting in poor choices, leading to suboptimal management and increased burden of AR on the individual and the community. However, no decision is made without external, influencing forces. This study aims to determine the key influences driving patients' decision-making around AR management. To accomplish this aim, we utilised a social network theory framework to map the patient's AR network and identify the strength of the influences within this network. Adults who reported having AR were interviewed and completed an AR network map and AR severity and quality of life questionnaires. Forty one people with AR completed the study. The AR networks of the participants had a range of 1-11 influences (alters), with an average number of 4 and a median of 5. The larger the impact of AR on their quality of life, the greater the number of alters within their network. The three most commonly identified alters were, general practitioners, pharmacists and the participants' 'own experience'. The strength of the influence of health-care professionals (HCPs) was varied. The proportion of HCPs within the AR network increased as the impact of AR on their quality of life increased. By mapping the AR network, this study demonstrated that there are multiple influences behind patient's decisions regarding AR management but the role of the HCP cannot be dismissed.
| S-EPMC6137238 | biostudies-literature
Project description:Meningioma is the most frequently occurring type of brain tumor. The present study aimed to conduct a comprehensive bioinformatics analysis of key genes and relevant pathways involved in meningioma, and acquire further insight into the underlying molecular mechanisms. Initially, differentially expressed genes (DEGs) in 47 meningioma samples as compared with 4 normal meninges were identified. Subsequently, these DEGs were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. In addition, a protein-protein interaction (PPI) network of the identified DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes and visualized using Cytoscape. In total, 1,683 DEGs were identified, including 66 upregulated and 1,617 downregulated genes. The GO analysis results revealed that the DEGs were significantly associated with the 'protein binding', 'cytoplasm', 'extracellular matrix (ECM) organization' and 'cell adhesion' terms. The KEGG analysis results demonstrated the significant pathways included 'AGE-RAGE signaling pathway in diabetic complications', 'PI3K-Akt signaling pathway', 'ECM-receptor interaction' and 'cell adhesion molecules'. The top five hub genes obtained from the PPI network were JUN, PIK3R1, FOS, AGT and MYC, and the most enriched KEGG pathways associated with the four obtained modules were 'chemokine signaling pathway', 'cytokine-cytokine receptor interaction', 'allograft rejection', and 'complement and coagulation cascades'. In conclusion, bioinformatics analysis identified a number of potential biomarkers and relevant pathways that may represent key mechanisms involved in the development and progression of meningioma. However, these findings require verification in future experimental studies.
| S-EPMC5950024 | biostudies-literature
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.
| S-EPMC7054703 | biostudies-literature
Project description:Preeclampsia is a leading cause of perinatal maternal-foetal mortality and morbidity. The aim of this study is to identify the key microRNAs and genes in preeclampsia and uncover their potential functions. We downloaded the miRNA expression profile of GSE84260 and the gene expression profile of GSE73374 from the Gene Expression Omnibus database. Differentially expressed miRNAs and genes were identified and compared to miRNA-target information from MiRWalk 2.0, and a total of 65 differentially expressed miRNAs (DEMIs), including 32 up-regulated miRNAs and 33 down-regulated miRNAs, and 91 differentially expressed genes (DEGs), including 83 up-regulated genes and 8 down-regulated genes, were identified. The pathway enrichment analyses of the DEMIs showed that the up-regulated DEMIs were enriched in the Hippo signalling pathway and MAPK signalling pathway, and the down-regulated DEMIs were enriched in HTLV-I infection and miRNAs in cancers. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses of the DEGs were performed using Multifaceted Analysis Tool for Human Transcriptome. The up-regulated DEGs were enriched in biological processes (BPs), including the response to cAMP, response to hydrogen peroxide and cell-cell adhesion mediated by integrin; no enrichment of down-regulated DEGs was identified. KEGG analysis showed that the up-regulated DEGs were enriched in the Hippo signalling pathway and pathways in cancer. A PPI network of the DEGs was constructed by using Cytoscape software, and FOS, STAT1, MMP14, ITGB1, VCAN, DUSP1, LDHA, MCL1, MET, and ZFP36 were identified as the hub genes. The current study illustrates a characteristic microRNA profile and gene profile in preeclampsia, which may contribute to the interpretation of the progression of preeclampsia and provide novel biomarkers and therapeutic targets for preeclampsia.
| S-EPMC5464566 | biostudies-literature