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1.
Int J Biochem Cell Biol ; 114: 105577, 2019 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-31391149

RESUMO

Cancer pathogenesis has been attributed to the minor and major disruptions in the cell cycle, with a key role being played by several of the recently discovered epigenetic factors. Lately, UHRF1 (Ubiquitin-like with containing PHD and RING Finger domains 1), an epigenetic regulator has been shown to be evidently over expressed in numerous malignancies through an in-depth review of literature. Molecular docking studies have found that existing drugs such as propranolol, naphthazarin and thymoquinone have favourable interactions with specific domains of UHRF1. However, these findings would need large scale clinical trials to confirm their potency and safety during chemotherapy. UHRF1 (Ubiquitin-like with containing PHD and RING Finger domains 1), an epigenetic factor, plays a crucial role as an important checkpoint in the cell machinery. Basic science continues to unravel multiple facets of this five domain protein which includes a detailed elucidation of its roles and mechanisms of interaction with various enzymes during DNA replication. The gene has recently begun to be also termed as the "Universal Oncogene" in response to the results of research conducted in heterogenous populations and in over 17 cancers displaying heightened mRNA and protein expression in breast, liver, lung, head and neck cancers and many more. This gene could therefore, be a potential biomarker for diagnosis and for the prediction of the prognosis and survival of the diseased. A scientifically established solution in the form of targeted treatment must follow such a discovery and therefore, several natural and synthetic compounds such as thymoquinone and the well-known antihypertensive, propranolol have been docked and reported to have favourable interactions with the SRA (Set and Ring Associated) domain of UHRF1 in this review. This comprehensive review is thus, a brief synopsis of details regarding the structure and heightened levels of UHRF1 in several malignancies. Furthermore, pharmacogenomic research revolving around this oncogene is a potential sphere for clinical studies to be conducted in much larger and heterogenous populations to not only validate these therapeutic docking results but to also to bring personalised medicine to the bedside for the benefit of the patients.

3.
Med Oncol ; 34(11): 182, 2017 Sep 26.
Artigo em Inglês | MEDLINE | ID: mdl-28952134

RESUMO

The aim of this study was to identify key genes associated with gliomas and glioblastoma and to explore the related signaling pathways. Gene expression profiles of three glioma stem cell line samples, three normal astrocyte samples, three astrocyte overexpressing 4 iPSC-inducing and oncogenic factors (myc(T58A), OCT-4, p53DD, and H-Ras(G12V)) samples, three astrocyte overexpressing 7 iPSC-inducing and oncogenic factors (OCT4, H-Ras(G12V), myc(T58A), p53DD, cyclin D1, CDK4(RC24) and hTERT) samples and three glioblastoma cell line samples were downloaded from the ArrayExpress database (accession: E-MTAB-4771). The differentially expressed genes (DEGs) in gliomas and glioblastoma were identified using FDR and t tests, and protein-protein interaction (PPI) networks for these DEGs were constructed using the protein interaction network analysis. The GeneTrail2 1.5 tool was used to identify potentially enriched biological processes among the DEGs using gene ontology (GO) terms and to identify the related pathways using the Kyoto Encyclopedia of Genes and Genomes, Reactome and WikiPathways pathway database. In addition, crucial modules of the constructed PPI networks were identified using the PEWCC1 plug-in, and their topological properties were analyzed using NetworkAnalyzer, both available from Cytoscape. We also constructed microRNA-target gene regulatory network and transcription factor-target gene regulatory network for these DEGs were constructed using the miRNet and binding and expression target analysis. We identified 200 genes that could potentially be involved in the gliomas and glioblastoma. Among them, bioinformatics analysis identified 137 up-regulated and 63 down-regulated DEGs in gliomas and glioblastoma. The significant enriched pathway (PI3K-Akt) for up-regulated genes such as COL4A1, COL4A2, EGFR, FGFR1, LAPR6, MYC, PDGFA, SPP1 were selected as well as significant GO term (ear development) for up-regulated genes such as CELSR1, CHRNA9, DDR1, FGFR1, GLI2, LGR5, SOX2, TSHR were selected, while the significant enriched pathway (amebiasis) for down-regulated gene such as COL3A1, COL5A2, LAMA2 were selected as well as significant GO term (RNA polymerase II core promoter proximal region sequence-specific binding (5) such as MEIS2, MEOX2, NR2E1, PITX2, TFAP2B, ZFPM2 were selected. Importantly, MYC and SOX2 were hub proteins in the up-regulated PPI network, while MET and CDKN2A were hub proteins in the down-regulated PPI network. After network module analysis, MYC, FGFR1 and HOXA10 were selected as the up-regulated coexpressed genes in the gliomas and glioblastoma, while SH3GL3 and SNRPN were selected as the down-regulated coexpressed genes in the gliomas and glioblastoma. MicroRNA hsa-mir-22-3p had a regulatory effect on the most up DEGs, including VSNL1, while hsa-mir-103a-3p had a regulatory effect on the most down DEGs, including DAPK1. Transcription factor EZH2 had a regulatory effect on the both up and down DEGs, including CD9, CHI3L1, MEIS2 and NR2E1. The DEGs, such as MYC, FGFR1, CDKN2A, HOXA10 and MET, may be used for targeted diagnosis and treatment of gliomas and glioblastoma.


Assuntos
Regulação Neoplásica da Expressão Gênica , Glioma/genética , Glioma/patologia , Mapas de Interação de Proteínas/genética , Astrócitos/fisiologia , Linhagem Celular Tumoral , Biologia Computacional/métodos , Perfilação da Expressão Gênica , Redes Reguladoras de Genes , Glioblastoma/genética , Glioblastoma/patologia , Humanos , MicroRNAs , Análise de Sequência com Séries de Oligonucleotídeos , Fatores de Transcrição/genética , Fatores de Transcrição/metabolismo
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