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

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.
 ...
PMID:Analysis of gene expression profiles associated with glioma progression. 2584 10

MicroRNAs (miRNAs) play an important role in the development and progression of human diseases. The identification of disease-associated miRNAs will be helpful for understanding the molecular mechanisms of diseases at the post-transcriptional level. Based on different types of genomic data sources, computational methods for miRNA-disease association prediction have been proposed. However, individual source of genomic data tends to be incomplete and noisy; therefore, the integration of various types of genomic data for inferring reliable miRNA-disease associations is urgently needed. In this study, we present a computational framework, CHNmiRD, for identifying miRNA-disease associations by integrating multiple genomic and phenotype data, including protein-protein interaction data, gene ontology data, experimentally verified miRNA-target relationships, disease phenotype information and known miRNA-disease connections. The performance of CHNmiRD was evaluated by experimentally verified miRNA-disease associations, which achieved an area under the ROC curve (AUC) of 0.834 for 5-fold cross-validation. In particular, CHNmiRD displayed excellent performance for diseases without any known related miRNAs. The results of case studies for three human diseases (glioblastoma, myocardial infarction and type 1 diabetes) showed that all of the top 10 ranked miRNAs having no known associations with these three diseases in existing miRNA-disease databases were directly or indirectly confirmed by our latest literature mining. All these results demonstrated the reliability and efficiency of CHNmiRD, and it is anticipated that CHNmiRD will serve as a powerful bioinformatics method for mining novel disease-related miRNAs and providing a new perspective into molecular mechanisms underlying human diseases at the post-transcriptional level. CHNmiRD is freely available at http://www.bio-bigdata.com/CHNmiRD.
...
PMID:Integration of Multiple Genomic and Phenotype Data to Infer Novel miRNA-Disease Associations. 2684 7

Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) selectively induces carcinoma cell death through the extrinsic pathway of apoptosis. Preclinical trials of gene therapy have been conducted using viral transfer of the TRAIL transgene into prostate, bladder, breast, kidney, liver, non-small cell lung cancer and also glioblastoma cells. Experiments in vitro demonstrated the extensive apoptosis of target cells as well as frequent disease regression or remission. TRAIL transfer did not show any side effects, opposite to chemotherapy. Encouraging results of TRAIL-related gene therapy were observed in rheumatoid arthritis and type 1 diabetes. Adenoviral vectors (AdV) encoding TRAIL are the most promising tool in anti-tumor therapy. They have undergone numerous modifications by increasing transfection efficiency and transgene expression in target cells. However, only one clinical phase I trial has been performed. AdV encoding the TRAIL transgene caused local inflammation and apoptosis in patients with prostate cancer.
...
PMID:[Viral transfer of tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) in gene therapy]. 2725 13