Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0011854 (type 1 diabetes)
 20,749 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

The aims of this overview are to synthesize the current evidence of published systematic reviews (SRs) on nonallergic comorbidities of atopic eczema (AE). EMBASE and MEDLINE were searched for SRs published from inception to November 2012. SRs were selected independently based on predefined inclusion criteria. Methodological quality of SRs included was assessed by two independent reviewers using the Revised Assessment of Multiple Systematic Reviews (R-AMSTAR) checklist. Nine SRs met all inclusion criteria. Six reviews addressing the association between AE and cancer suggest a decreased risk of glioma, meningioma, and acute lymphoblastic leukemia in patients with current or previous AE. One SR reported a consistent positive association of AE with attention-deficit hyperactivity disorder (ADHD). Diabetes mellitus type 1 and multiple sclerosis (MS) were not significantly related to AE in reviews based on cross-sectional and case-control studies. Patients with AE appear to be at decreased risk of brain tumors. The relationship of AE with Th1- and Th17-mediated (auto-)inflammatory conditions such as diabetes mellitus type 1 and MS should be clarified in prospective observational studies. Children with AE are at increased risk of ADHD. SRs on the risk of depression and Th17-mediated disorders such as inflammatory bowel disease of patients with AE are missing.
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PMID:Nonallergic comorbidities of atopic eczema: an overview of systematic reviews. 2405 42

The present study aimed to investigate changes at the transcript level that are associated with spontaneous astrocytoma progression, and further, to discover novel targets for glioma diagnosis and therapy. GSE4290 microarray data downloaded from Gene Expression Omnibus were used to identify the differentially expressed genes (DGEs) by significant analysis of microarray (SAM). The Short Time Series Expression Miner (STEM) method was then applied to class these DEGs based on their degrees of differentiation in the process of tumor progression. Finally, EnrichNet was used to perform the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis based on a protein-protein interaction (PPI) network. A total of 4,506 DEGs were detected, and the number of DEGs was the highest in grade IV cells (2,580 DEGs). These DEGs were classified into nine clusters by the STEM method. In total, 11 KEGG pathways with XD-scores larger than the threshold (0.96) were obtained. The DEGs enriched in pathways 1 (extracellular matrix-receptor interaction), 3 (phagosome) and 6 (type I diabetes mellitus) mainly belonged to cluster 5. Pathway 2 (long-term potentiation), 4 (Vibrio cholerae infection) and 5 (epithelial cell signaling in Helicobacter pylori infection) was involved with DEGs that belonged to different clusters. Significant changes in gene expression occurred during glioma progression. Pathways 1, 3 and 6 may be important for the deterioration of glioma into glioblastoma, and pathways 2, 4 and 5 may have a role at each stage during glioma progression. The associated DEGs, including SV2, NMDAR and mGluRs, may be suitable as biomarkers or therapeutic targets for gliomas.
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PMID:Analysis of gene expression profiles associated with glioma progression. 2584 10

Differentially methylated genes (DMGs) serve a crucial role in the pathogenesis of glioma via the regulation of the cell cycle, proliferation, apoptosis, migration, infiltration, DNA repair and signaling pathways. This study aimed to identify aberrant DMGs and pathways by comprehensive bioinformatics analysis. The gene expression profile of GSE28094 was downloaded from the Gene Expression Omnibus (GEO) database, and the GEO2R online tool was used to find DMGs. Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of the DMGs were performed by using the Database for Annotation Visualization and Integrated Discovery. A protein-protein interaction (PPI) network was constructed with Search Tool for the Retrieval of Interacting Genes. Analysis of modules in the PPI networks was performed by Molecular Complex Detection in Cytoscape software, and four modules were performed. The hub genes with a high degree of connectivity were verified by The Cancer Genome Atlas database. A total of 349 DMGs, including 167 hypermethylation genes, were enriched in biological processes of negative and positive regulation of cell proliferation and positive regulation of transcription from RNA polymerase II promoter. Pathway analysis enrichment revealed that cancer regulated the pluripotency of stem cells and the PI3K-AKT signaling pathway, whereas 182 hypomethylated genes were enriched in biological processes of immune response, cellular response to lipopolysaccharide and peptidyl-tyrosine phosphorylation. Pathway enrichment analysis revealed cytokine-cytokine receptor interaction, type I diabetes mellitus and TNF signaling pathway. A total of 20 hub genes were identified, of which eight genes were associated with survival, including notch receptor 1 (NOTCH1), SRC proto-oncogene (also known as non-receptor tyrosine kinase, SRC), interleukin 6 (IL6), matrix metallopeptidase 9 (MMP9), interleukin 10 (IL10), caspase 3 (CASP3), erb-b2 receptor tyrosine kinase 2 (ERBB2) and epidermal growth factor (EGF). Therefore, bioinformatics analysis identified a series of core DMGs and pathways in glioma. The results of the present study may facilitate the assessment of the tumorigenicity and progression of glioma. Furthermore, the significant DMGs may provide potential methylation-based biomarkers for the precise diagnosis and targeted treatment of glioma.
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PMID:Identification of core differentially methylated genes in glioma. 3178 78