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)

The mechanisms of idiopathic severe aplastic anemia (SAA) in children are not completely understood. Insufficiency of the bone marrow microenvironment, in which mesenchymal stem cells (MSCs) are an important element, can be a potential factor associated with hematopoietic impairment. In the current study, we studied whether aberrant gene expression could be found in MSCs from children with SAA. Using microarray analysis, two different patterns of global gene expression were detected in the SAA MSCs. Fourteen genes (POLE2, HGF, KIF20A, TK1, IL18R1, KITLG, FGF18, RRM2, TTK, CXCL12, DLG7, TOP2A, NUF2, and TYMS), which are related to DNA synthesis, cytokines, or growth factors, were significantly downregulated. Further, knockdown of gene expression was performed using the small hairpin RNA (shRNA)-containing lentivirus method. We found that knockdown of CXCL12, HGF, IL-18R1, FGF18, or RRM2 expression compelled MSCs from the controls to behave like those from the SAA children, with decreased survival and differentiation potential. Among them, inhibition of CXCL12 gene expression had the most profound effects on the behavior of MSCs. Further experiments regarding re-introduction of the CXCL12 gene could largely recover the survival and differentiation potential in MSCs with inhibition of CXCL12 expression. Our findings suggest that MSCs from children with SAA exhibit aberrant gene expression profiles and downregulation of CXCL12 gene may be associated with alterations in the bone marrow microenvironment.
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PMID:Downregulated CXCL12 expression in mesenchymal stem cells associated with severe aplastic anemia in children. 2511 93

Glioblastoma is a common malignant tumor in the central nervous system with an extremely poor outcome; understanding the mechanisms of glioblastoma at the molecular level is essential for clinical treatment. In the present study, we used bioinformatics analysis to identify potential biomarkers associated with prognosis in glioblastoma and elucidate the underlying mechanisms. The result revealed that 552 common genes were differentially expressed between glioblastoma and normal tissues based on TCGA, GSE4290, and GSE 50161 datasets. Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment and protein-protein interaction (PPI) network were carried out to gain insight into the actions of differentially expressed genes (DEGs). As a result, 20 genes (CALB1, CDC20, CDCA8, CDK1, CEP55, DLGAP5, KIF20A, KIF4A, NDC80, PBK, RRM2, SYN1, SYP, SYT1, TPX2, TTK, VEGFA, BDNF, GNG3, and TOP2A) were found as hub genes via CytoHubba in Cytoscape and functioned mainly by participating in cell cycle and p53 signaling pathway; among them, RRM2 and CEP55 were considered to have relationship with the prognosis of glioblastoma, especially RRM2. High expression of RRM2 was consistent with shorter overall survival time. In conclusion, our study displayed the bioinformatic analysis methods in screening potential oncogenes in glioblastoma and underlying mechanisms. What is more is that we successfully identified RRM2 as a novel biomarker linked with prognosis, which might be expected to be a promising target for the therapy of glioblastoma.
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PMID:Identification of Potential Biomarkers in Glioblastoma through Bioinformatic Analysis and Evaluating Their Prognostic Value. 3111 82

Atypical teratoid/rhabdoid tumor (ATRT) is a devastating intracranial tumor in children. Currently, its molecular mechanisms cannot be studied effectively because patient samples are limited, and many factors are involved in its pathogenesis. In this study, we analyzed three gene expression profile data sets obtained from the Gene Expression Omnibus (GEO) database to identify genes that participate in ATRT. The datasets were integrated and analyzed using the RobustRankAggreg method to screen for differentially expressed genes (DEGs). We identified 197 DEGs, including 94 downregulated and 103 upregulated genes which were then used for gene set enrichment analysis. The results showed that the downregulated genes were mainly enriched in synaptic vesicle cycle, nicotine addiction, and GABAergic synapse, whereas the upregulated genes were enriched in the cell cycle, p53 signaling pathway, and cellular senescence. Consistent with these results, gene set enrichment analysis showed that E2F targets, G2M checkpoints, and MYC targets were significantly enriched in datasets. Protein-protein interaction (PPI) network revealed that CDK1, CCNA2, BUB1B, CDC20, KIF11, KIF20A, KIF2C, NCAPG, NDC80, NUSAP1, PBK, RRM2, TPX2, TOP2A, and TTK were hub genes. NetworkAnalyst algorithm was used to predict the transcription factor (TF), and the results showed that MYC, SOX2, and KDM5B could regulate these hub genes. In conclusion, the present study brings a new perspective of ATRT pathogenesis and the strategy targeted to cell cycle related gene may be promising treatments for the disease.
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PMID:Identification of Hub Genes in Atypical Teratoid/Rhabdoid Tumor by Bioinformatics Analyses. 3244 Aug 21

Retinoblastoma (RB) is the commonest malignant tumor of the infant retina. Besides genetic changes, epigenetic events are also considered to implicate the occurrence of RB. This study aimed to identify significantly altered protein-coding genes, DNA methylation, microRNAs (miRNAs), long noncoding RNAs (lncRNAs), and their molecular functions and pathways associated with RB, and investigate the epigenetically regulatory mechanism of DNA methylation modification and non-coding RNAs on key genes of RB via bioinformatics method.We obtained multi-omics data on protein-coding genes, DNA methylation, miRNAs, and lncRNAs from the Gene Expression Omnibus database. We identified differentially expressed genes (DEGs) using the Limma package in R, discerned their biological functions and pathways using enrichment analysis, and conducted the modular analysis based on protein-protein interaction network to identify hub genes of RB. Survival analyses based on The Cancer Genome Atlas clinical database were performed to analyze prognostic values of key genes of RB. Subsequently, we identified the differentially methylated genes, differentially expressed miRNAs (DEMs) and lncRNAs (DELs), and intersected them with key genes to analyze possible targets of the underlying epigenetic regulatory mechanisms. Finally, the ceRNA network of lncRNAs-miRNAs-mRNAs was constructed using Cytoscape.A total of 193 DEGs, 74 differentially methylated-DEGs (DM-DEGs), 45 DEMs, 5 DELs were identified. The molecular pathways of DEGs were enriched in cell cycle, p53 signaling pathway, and DNA replication. A total of 10 key genes were identified and found significantly associated with poor survival outcome based on survival analyses, including CDK1, BUB1, CCNB2, TOP2A, CCNB1, RRM2, KIF11, KIF20A, NDC80, and TTK. We further found that hub genes MCM6 and KIF14 were differentially methylated, key gene RRM2 was targeted by DEMs, and key genes TTK, RRM2, and CDK1 were indirectly regulated by DELs. Additionally, the ceRNA network with 222 regulatory associations was constructed to visualize the correlations between lncRNAs-miRNAs-mRNAs.This study presents an integrated bioinformatics analysis of genetic and epigenetic changes that may be associated with the development of RB. Findings may yield many new insights into the molecular biomarker candidates and epigenetically regulatory targets of RB.
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PMID:Bioinformatics analysis of multi-omics data identifying molecular biomarker candidates and epigenetically regulatory targets associated with retinoblastoma. 3321 67