Distinct Gene Set Enrichment Profiles in Eosinophilic and Non-Eosinophilic Chronic Rhinosinusitis with Nasal Polyps by Bulk RNA Barcoding and Sequencing.

Chronic rhinosinusitis with nasal polyps (CRSwNP) is a chronic inflammatory disease with a high symptom burden, including nasal congestion and smell disorders. This study performed a detailed transcriptomic analysis in CRSwNP classified as eosinophilic CRS (ECRS), nonECRS according to the Japanese Epidemiological Survey of Refractory Eosinophilic Chronic Rhinosinusitis (JESREC) criteria, and a group of ECRS with comorbid aspirin intolerant asthma (Asp). Gene expression profiles of nasal polyps and the uncinate process in CRSwNP patients and normal subjects (controls) were generated by bulk RNA barcoding and sequencing (BRB-seq). A differentially expressed genes (DEGs) analysis was performed using DESeq2 software in iDEP to clarify any relationship between gene expression and disease backgrounds. A total of 3004 genes were identified by DEGs analysis to be associated with ECRS vs control, nonECRS vs control, and Asp vs control. A pathway analysis showed distinct profiles between the groups. A Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis using the Database for Annotation, Visualization, and Integrated Discovery (DAVID) showed distinct phenotype-specific pathways of expressed genes. In the specific pathway of "cytokine-cytokine receptor interaction", the differentially expressed genes were widely distributed. This study indicates that transcriptome analysis using BRB-seq may be a valuable tool to explore the pathogenesis of type 2 inflammation in CRSwNP.

Genes associated with inflammation may serve as biomarkers for the diagnosis of coronary artery disease and ischaemic stroke.

BACKGROUND: The current research aimed to expound the genes and pathways that are involved in coronary artery disease (CAD) and ischaemic stroke (IS) and the related mechanisms. METHODS: Two array CAD datasets of (GSE66360 and GSE97320) and an array IS dataset (GSE22255) were downloaded. Differentially expressed genes (DEGs) were identified using the limma package. The online tool Database for Annotation, Visualization and Integrated Discovery (DAVID) (version 6.8; david.abcc.ncifcrf.gov) was used to annotate the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Gene Ontology (GO) enrichment analyses of the DEGs. A protein-protein interaction (PPI) network was constructed by Cytoscape software, and then Molecular Complex Detection (MCODE) analysis was used to screen for hub genes. The hub genes were also confirmed by RT-qPCR and unconditional logistic regression analysis in our CAD and IS patients. RESULTS: A total of 20 common DEGs (all upregulated) were identified between the CAD/IS and control groups. Eleven molecular functions, 3 cellular components, and 49 biological processes were confirmed by GO enrichment analysis, and the 20 common upregulated DEGs were enriched in 21 KEGG pathways. A PPI network including 24 nodes and 68 edges was constructed with the STRING online tool. After MCODE analysis, the top 5 high degree genes, including Jun proto-oncogene (JUN, degree = 9), C-X-C motif chemokine ligand 8 (CXCL8, degree = 9), tumour necrosis factor (TNF, degree = 9), suppressor of cytokine signalling 3 (SOCS3, degree = 8) and TNF alpha induced protein 3 (TNFAIP3, degree = 8) were noted. RT-qPCR results demonstrated that the expression levels of CXCL8 were increased in IS patients than in normal participants and the expression levels of SOCS3, TNF and TNFAIP were higher in CAD/IS patients than in normal participants. Meanwhile, unconditional logistic regression analysis revealed that the incidence of CAD or IS was positively correlated with the CXCL8, SOCS3, TNF and TNFAIP3. CONCLUSIONS: The CXCL8, TNF, SOCS3 and TNFAIP3 associated with inflammation may serve as biomarkers for the diagnosis of CAD or IS. The possible mechanisms may involve the Toll-like receptor, TNF, NF-kappa B, cytokine-cytokine receptor interactions and the NOD-like receptor signalling pathways.

Identification of metastasis-associated genes in colorectal cancer through an integrated genomic and transcriptomic analysis.

OBJECTIVE: Identification of colorectal cancer (CRC) metastasis genes is one of the most important issues in CRC research. For the purpose of mining CRC metastasis-associated genes, an integrated analysis of microarray data was presented, by combined with evidence acquired from comparative genomic hybridization (CGH) data. METHODS: Gene expression profile data of CRC samples were obtained at Gene Expression Omnibus (GEO) website. The 15 important chromosomal aberration sites detected by using CGH technology were used for integrated genomic and transcriptomic analysis. Significant Analysis of Microarray (SAM) was used to detect significantly differentially expressed genes across the whole genome. The overlapping genes were selected in their corresponding chromosomal aberration regions, and analyzed by using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Finally, SVM-T-RFE gene selection algorithm was applied to identify metastasis-associated genes in CRC. RESULTS: A minimum gene set was obtained with the minimum number [14] of genes, and the highest classification accuracy (100%) in both PRI and META datasets. A fraction of selected genes are associated with CRC or its metastasis. CONCLUSIONS: Our results demonstrated that integration analysis is an effective strategy for mining cancer-associated genes.

Systemic bioinformatics analysis of recurrent aphthous stomatitis gene expression profiles.

Recurrent aphthous stomatitis (RAS) represents the most common chronic oral diseases with the prevalence ranges from 5% to 25% for different populations. Its pathogenesis remains poorly understood, which limits the development of effective drugs and treatment methods. In this study, we conducted systemic bioinformatics analysis of gene expression profiles from the Gene Expression Omnibus (GEO) to identify potential drug targets for RAS. We firstly downloaded the gene microarray datasets with the accession number of GSE37265 from GEO and performed robust multi-array (RMA) normalization with affy R programming package. Secondly, differential expression genes (DEGs) in RAS samples compared with control samples were identified based on limma package. Enriched gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of DEGs were obtained through the Database for Annotation, Visualization and Integrated Discovery (DAVID). Finally, protein-protein interaction (PPI) network was constructed based on the combination of HPRD and BioGrid databases. What's more, we identified modules of PPI network through MCODE plugin of Cytoscape for the purpose of screening of valuable targets. As a result, 915 genes were found to be significantly differential expression in RAS samples and biological processes related to immune and inflammatory response were significantly enriched in those genes. Network and module analysis identified FBXO6, ITGA4, VCAM1 and etc as valuable therapeutic targets for RAS. Finally, FBXO6, ITGA4, and VCAM1 were further confirmed by real time RT-PCR and western blot. This study should be helpful for the research and treatment of RAS.