Gene/Protein Disease Symptom Drug Enzyme Compound
Pivot Concepts:   Target Concepts:
Query: EC:2.7.7.6 (RNA polymerase)
 34,946 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

BACKGROUND The underlying mechanism of insulin resistance is complex; bioinformatics analysis is used to explore the mechanism based differential expression genes (DEGs) obtained from omics analysis. However, the expression and role of most DEGs involved in bioinformatics analysis are invalidated. This study aimed to disclose the mechanism of insulin resistance via bioinformatics analysis based on validated insulin resistance-related genes (IRRGs) collected from public disease-gene databases. MATERIAL AND METHODS IRRGs were collected from 4 disease databases including NCBI-Gene, CTD, RGD, and Phenopedia. GO and KEGG analysis of IRRGs were performed by DAVID. Then, the STRING database was employed to construct a protein-protein interaction (PPI) network of IRRGs. The module analysis and hub genes identification were carried out by MCODE and cytoHubba plugin of Cytoscape based on the primary PPI network, respectively. RESULTS A total of 1195 IRRGs were identified. Response to drug, hypoxia, insulin, positive regulation of transcription from RNA polymerase II promoter, cell proliferation, inflammatory response, negative regulation of apoptotic process, glucose homeostasis, cellular response to insulin stimulus, and aging were proposed as the crucial functions related to insulin resistance. Ten insulin resistance-related pathways included the pathways of insulin resistance, pathways in cancer, adipocytokine, prostate cancer, PI3K-Akt, insulin, AMPK, HIF-1, prolactin, and pancreatic cancer signaling pathway were revealed. INS, AKT1, IL-6, TP53, TNF, VEGFA, MAPK3, EGFR, EGF, and SRC were identified as the top 10 hub genes. CONCLUSIONS The current study presented a landscape view of possible underlying mechanism of insulin resistance by bioinformatics analysis based on validated IRRGs.
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PMID:Underlying Mechanism of Insulin Resistance: A Bioinformatics Analysis Based on Validated Related-Genes from Public Disease Databases. 3265 53

We have previously reported that miR-9 promotes the homing, proliferation, and angiogenesis of endothelial progenitor cells (EPCs) by targeting transient receptor potential melastatin 7 via the AKT autophagy pathway. In this way, miR-9 promotes thrombolysis and recanalization following deep vein thrombosis (DVT). However, the influence of miR-9 on messenger RNA (mRNA) expression profiles of EPCs remains unclear. The current study comprises a comprehensive exploration of the mechanisms underlying the miR-9-regulated angiogenesis of EPCs and highlights potential treatment strategies for DVT. We performed RNA sequence analysis, which revealed that 4068 mRNAs were differentially expressed between EPCs overexpressing miR-9 and the negative control group, of which 1894 were upregulated and 2174 were downregulated. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses indicated that these mRNAs were mainly involved in regulating cell proliferation/migration processes/pathways and the autophagy pathway, both of which represent potential EPC-based treatment strategies for DVT. Reverse transcriptase quantitative polymerase chain reaction confirmed the changes in mRNA expression related to EPC angiogenesis, migration, and autophagy. We also demonstrate that miR-9 promotes EPC migration and angiogenesis by regulating FGF5 directly or indirectly. In summary, miR-9 enhances the expression of VEGFA, FGF5, FGF12, MMP2, MMP7, MMP10, MMP11, MMP24, and ATG7, which influences EPC migration, angiogenesis, and autophagy. We provide a comprehensive evaluation of the miR-9-regulated mRNA expression in EPCs and highlight potential targets for the development of new therapeutic interventions for DVT.
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PMID:Clarification of the Role of miR-9 in the Angiogenesis, Migration, and Autophagy of Endothelial Progenitor Cells Through RNA Sequence Analysis. 3302 8

Published transcriptomic data from surgically removed metastatic clear cell renal cell carcinoma samples were analyzed from the genomic fabric paradigm (GFP) perspective to identify the best targets for gene therapy. GFP considers the transcriptome as a multi-dimensional mathematical object constrained by a dynamic set of expression controls and correlations among genes. Every gene in the chest wall metastasis, two distinct cancer nodules, and the surrounding normal tissue of the right kidney was characterized by three independent measures: average expression level, relative expression variation, and expression correlation with each other gene. The analyses determined the cancer-induced regulation, control, and remodeling of the chemokine and vascular endothelial growth factor (VEGF) signaling, apoptosis, basal transcription factors, cell cycle, oxidative phosphorylation, renal cell carcinoma, and RNA polymerase pathways. Interestingly, the three cancer regions exhibited different transcriptomic organization, suggesting that the gene therapy should not be personalized only for every patient but also for each major cancer nodule. The gene hierarchy was established on the basis of gene commanding height, and the gene master regulators DAPK3,TASOR, FAM27C and ALG13 were identified in each profiled region. We delineated the molecular mechanisms by which TASOR overexpression and ALG13 silencing would selectively affect the cancer cells with little consequences for the normal cells.
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PMID:Genomic Fabric Remodeling in Metastatic Clear Cell Renal Cell Carcinoma (ccRCC): A New Paradigm and Proposal for a Personalized Gene Therapy Approach. 3330 83


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