Dataset Information

Identification of Susceptibility Genes to Allergic Rhinitis by Gene Expression Data Sets.

ABSTRACT: As an extremely prevalent disease worldwide, allergic rhinitis (AR) is a condition characterized by chronic inflammation of the nasal mucosa. To identify the finer molecular mechanisms associated with the AR susceptibility genes, differentially expressed genes (DEGs) in AR were investigated. The DEG expression and clinical data of the GSE19187 data set were used for weighted gene co-expression network analysis (WGCNA). After the modules related to AR had been screened, the genes in the module were extracted for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, whereby the genes enriched in the KEGG pathway were regarded as the pathway-genes. The DEGs in patients with AR were subsequently screened out from GSE19187, and the sensitive genes were identified in GSE18574 in connection with the allergen challenge. Two kinds of genes were compared with the pathway-genes in order to screen the AR susceptibility genes. Receiver operating characteristic (ROC) curve was plotted to evaluate the capability of the susceptibility genes to distinguish the AR state. Based on the WGCNA in the GSE19187 data set, 10 co-expression network modules were identified. The correlation analyses revealed that the yellow module was positively correlated with the disease state of AR. A total of 89 genes were found to be involved in the enrichment of the yellow module pathway. Four genes (CST1, SH2D1B, DPP4, and SLC5A5) were upregulated in AR and sensitive to allergen challenge, whose potentials were further confirmed by ROC curve. Taken together, CST1, SH2D1B, DPP4, and SLC5A5 are susceptibility genes to AR.

SUBMITTER: Xue K

PROVIDER: S-EPMC6951973 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Identification of Susceptibility Genes to Allergic Rhinitis by Gene Expression Data Sets.

Xue Kai K Yang Jingpu J Zhao Yin Y Cheng Jinzhang J Wang Zonggui Z

Clinical and translational science 20191203 1

As an extremely prevalent disease worldwide, allergic rhinitis (AR) is a condition characterized by chronic inflammation of the nasal mucosa. To identify the finer molecular mechanisms associated with the AR susceptibility genes, differentially expressed genes (DEGs) in AR were investigated. The DEG expression and clinical data of the GSE19187 data set were used for weighted gene co-expression network analysis (WGCNA). After the modules related to AR had been screened, the genes in the module we ...[more]

PMID: 31794148

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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.

Project description:BackgroundAllergen-specific immunotherapy (AIT) is a causative treatment in allergic rhinitis (AR), comprising long-term allergen administration and over three years of treatment. This study is carried out for revealing the mechanisms and key genes of AIT in AR.MethodsThe present study utilized online Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE37157 and GSE29521 to analyze the hub genes changes related to AIT in AR. Based on limma package, differential expression analysis for the two groups (samples of allergic patients prior to AIT and samples of allergic patients undergoing AIT) was performed to obtain differentially expressed genes (DEGs). Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of DEGs were conducted using DAVID database. A Protein-Protein Interaction network (PPI) was built and a significant network module was acquired by using Cytoscape software (Cytoscape, 3.7.2). Utilizing the miRWalk database, we identified potential gene biomarkers, constructed interaction networks of target genes and microRNAs (miRNAs) using Cytoscape software, and explore the cell type-specific expression patterns of these genes in peripheral blood using publicly available single-cell RNA sequencing data (GSE200107). Finally, we are using PCR to detect changes in the hub genes that are screened using the above method in peripheral blood before and after AIT treatment.ResultsGSE37157 and GSE29521 included 28 and 13 samples, respectively. A total of 119 significantly co-upregulated DEGs and 33 co-downregulated DEGs were obtained from two datasets. The GO and KEGG analyses demonstrated that protein transport, positive regulation of apoptotic process, Natural killer cell mediated cytotoxicity, T cell receptor signaling pathway, TNF signaling pathway, B cell receptor signaling pathway and Apoptosis may be potential candidate therapeutic targets for AIT of AR. From the PPI network, 20 hub genes were obtained. Among them, the PPI sub-networks of CASP3, FOXO3, PIK3R1, PIK3R3, ATF4, and POLD3 screened out from our study have been identified as reliable predictors of AIT in AR, especially the PIK3R1.ConclusionOur analysis has identified novel gene signatures, thereby contributing to a more comprehensive understanding of the molecular mechanisms underlying AIT in the treatment of AR.

Project description:BACKGROUND: Asthma genetics has been extensively studied and many genes have been associated with the development or severity of this disease. In contrast, the genetic basis of allergic rhinitis (AR) has not been evaluated as extensively. It is well known that asthma is closely related with AR since a large proportion of individuals with asthma also present symptoms of AR, and patients with AR have a 5-6 fold increased risk of developing asthma. Thus, the relevance of asthma candidate genes as predisposing factors for AR is worth investigating. The present study was designed to investigate if SNPs in highly replicated asthma genes are associated with the occurrence of AR. METHODS: A total of 192 SNPs from 21 asthma candidate genes reported to be associated with asthma in 6 or more unrelated studies were genotyped in a Swedish population with 246 AR patients and 431 controls. Genotypes for 429 SNPs from the same set of genes were also extracted from a Singapore Chinese genome-wide dataset which consisted of 456 AR cases and 486 controls. All SNPs were subsequently analyzed for association with AR and their influence on allergic sensitization to common allergens. RESULTS: A limited number of potential associations were observed and the overall pattern of P-values corresponds well to the expectations in the absence of an effect. However, in the tests of allele effects in the Chinese population the number of significant P-values exceeds the expectations. The strongest signals were found for SNPs in NPSR1 and CTLA4. In these genes, a total of nine SNPs showed P-values <0.001 with corresponding Q-values <0.05. In the NPSR1 gene some P-values were lower than the Bonferroni correction level. Reanalysis after elimination of all patients with asthmatic symptoms excluded asthma as a confounding factor in our results. Weaker indications were found for IL13 and GSTP1 with respect to sensitization to birch pollen in the Swedish population. CONCLUSIONS: Genetic variation in the majority of the highly replicated asthma genes were not associated to AR in our populations which suggest that asthma and AR could have less in common than previously anticipated. However, NPSR1 and CTLA4 can be genetic links between AR and asthma and associations of polymorphisms in NPSR1 with AR have not been reported previously.

Dataset Information

Identification of Susceptibility Genes to Allergic Rhinitis by Gene Expression Data Sets.

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Identification of Susceptibility Genes to Allergic Rhinitis by Gene Expression Data Sets.

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