Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:2.7.11.26 (GSK)
 6,788 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

In recording the changes acquired in gene expression profile during culture of fresh bone marrow samples from patients with multiple myeloma or acute myeloid leukemia, the most remarkable finding in both instances was widespread downregulation of mitotic and transcriptional genes (e.g. MKI67, CCNB1, ASPM, SGOL1, DLGAP5, CENPF, BUB1, KIF23, KIF18a, KIF11, KIF14, KIF4, NUF2, KIF1, AE2FB, TOP2A, NCAPG, TTK, CDC20, and AURKB), which could account for the ensuing proliferation arrest. Many of these genes were also underexpressed in leukemic cells from the blood or myeloma cells from an extramedullary site compared with their expression in the aspirates. Taken together, our results exhibited mitotic and transcriptional gene subsets where their expression appears to be coordinated and niche dependent. In addition, the genes induced during culture specified a variety of angiogenic factors (e.g. interleukin-8 and CXCL-5) and extracellular matrix proteins (e.g. osteopontin and fibronectin) probably released by the tumor cells while generating their favored microenvironment.
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PMID:The proliferation arrest of primary tumor cells out-of-niche is associated with widespread downregulation of mitotic and transcriptional genes. 2407 79

Cyclosporin A is an immunosuppressant drug that is used not only in solid transplant rejection, but also in moderate and severe forms of psoriasis, pyoderma, lupus or arthritis. Serious side effects of the drug such as skin cancer or gingival hyperplasia probably start with the latent proliferation process. Little is known about the influence of cyclosporin A on molecular signaling in epidermal tissue. Thus, the aim of this study was to estimate the influence of cyclosporin A on the process of proliferation in normal human dermal fibroblasts. Fibroblasts were cultured in a liquid growth medium in standard conditions. Cyclosporin A was added to the culture after the confluence state. Survival and proliferation tests on human dermal fibroblast cells were performed. Total RNA was extracted from fibroblasts, based on which cDNA and cRNA were synthesized. The obtained cRNA was hybridized with the expression microarray HGU-133A_2.0. Statistical analysis of 2734 mRNAs was performed by the use of GeneSpring 13.0 software and only results with p < 0.05 were accepted. Analysis of variance with Tukey post hoc test with Benjamini-Hochberg correction for all three (8, 24, 48 h) culture stages (with and without cyclosporin A) was performed to lower the number of statistically significant results from 679 to 66, and less. Between statistically and biologically significant mRNAs down-regulated were EGRJ, BUBIB, MKI67, CDK1, TTK, E2F8, TPX2, however, the INSIG1, FOSL1, HMOX1 were up-regulated. The experiment data revealed that cyclosporin A up-regulated FOSL1 in the first 24 h, afterwards down-regulating its expression. The HMOX1 gene was up-regulated in the first stage of the experiment (CsA 8 h), however, after the next 16 h of culture time its expression was down-regulated (CsA 24 h), to finally increased in the later time period. The results indicate that cyclosporin A had a significant effect on proliferation in normal human dermal fibroblasts through the changes in the expression of genes related to the cell cycle and transcription regulation process.
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PMID:CYCLOSPORIN A AFFECTS THE PROLIFERATION PROCESS IN NORMAL HUMAN DERMAL FIBROBLASTS. 2700 1

Synovial sarcoma (SS) is a highly aggressive soft tissue tumor with high risk of local recurrence and metastasis. However, the mechanisms underlying SS metastasis are still largely unclear. The purpose of this study is to screen metastasis-associated biomarkers in SS by integrated bioinformatics analysis. Two mRNA datasets (GSE40018 and GSE40021) were selected to analyze the differentially expressed genes (DEGs). Using the Database for Annotation, Visualization and Integrated Discovery (DAVID) and gene set enrichment analysis (GSEA), functional and pathway enrichment analyses were performed for DEGs. Then, the protein-protein interaction (PPI) network was constructed via the Search Tool for the Retrieval of Interacting Genes (STRING) database. The module analysis of the PPI network and hub genes validation were performed using Cytoscape software. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of the hub genes were performed using WEB-based GEne SeT AnaLysis Toolkit (WebGestalt). The expression levels and survival analysis of hub genes were further assessed through Gene Expression Profiling Interactive Analysis (GEPIA) and the Kaplan-Meier plotter database. In total, 213 overlapping DEGs were identified, of which 109 were upregulated and 104 were downregulated. GO analysis revealed that the DEGs were predominantly involved in mitosis and cell division. KEGG pathways analysis demonstrated that most DEGs were significantly enriched in cell cycle pathway. GSEA revealed that the DEGs were mainly enriched in oocyte meiosis, cell cycle and DNA replication pathways. A key module was identified and 10 hub genes (CENPF, KIF11, KIF23, TTK, MKI67, TOP2A, CDC45, MELK, AURKB, and BUB1) were screened out. The expression and survival analysis disclosed that the 10 hub genes were upregulated in SS patients and could result in significantly reduced survival. Our study identified a series of metastasis-associated biomarkers involved in the progression of SS, and may provide novel therapeutic targets for SS metastasis.
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PMID:Identification of Metastasis-Associated Biomarkers in Synovial Sarcoma Using Bioinformatics Analysis. 3306 42

Accumulating evidence indicates that the reliable gene signature may serve as an independent prognosis factor for lung adenocarcinoma (LUAD) diagnosis. Here, we sought to identify a risk score signature for survival prediction of LUAD patients. In the Gene Expression Omnibus (GEO) database, GSE18842, GSE75037, GSE101929, and GSE19188 mRNA expression profiles were downloaded to screen differentially expressed genes (DEGs), which were used to establish a protein-protein interaction network and perform clustering module analysis. Univariate and multivariate proportional hazards regression analyses were applied to develop and validate the gene signature based on the TCGA dataset. The signature genes were then verified on GEPIA, Oncomine, and HPA platforms. Expression levels of corresponding genes were also measured by qRT-PCR and Western blotting in HBE, A549, and PC-9 cell lines. The prognostic signature based on eight genes (TTK, HMMR, ASPM, CDCA8, KIF2C, CCNA2, CCNB2, and MKI67) was established, which was independent of other clinical factors. The risk model offered better discrimination between risk groups, and patients with high-risk scores tended to have poor survival rate at 1-, 3- and 5-year follow-up. The model also presented better survival prediction in cancer-specific cohorts of age, gender, clinical stage III/IV, primary tumor 1/2, and lymph node metastasis 1/2. The signature genes, moreover, were highly expressed in A549 and PC-9 cells. In conclusion, the risk score signature could be used for prognostic estimation and as an independent risk factor for survival prediction in patients with LUAD.
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PMID:Establishment of a Gene Signature to Predict Prognosis for Patients with Lung Adenocarcinoma. 3318 19