Transcriptional signaling pathways inversely regulated in Alzheimer's disease and glioblastoma multiform.
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ABSTRACT:
SUBMITTER: Liu T
PROVIDER: S-EPMC4894382 | biostudies-literature | 2013 Dec
REPOSITORIES: biostudies-literature
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Project description:The present study aimed to investigate the molecular mechanisms underlying glioblastoma multiform (GBM) and its biomarkers. The differentially expressed genes (DEGs) were diagnosed using the limma software package. The ToppGene (ToppFun) was used to perform pathway and Gene Ontology (GO) enrichment analysis of the DEGs. Protein-protein interaction (PPI) networks, extracted modules, miRNA-target genes regulatory network and TF-target genes regulatory network were used to obtain insight into the actions of DEGs. Survival analysis for DEGs was carried out. A total of 590 DEGs, including 243 up regulated and 347 down regulated genes, were diagnosed between scrambled shRNA expression and Lin7A knock down. The up-regulated genes were enriched in ribosome, mitochondrial translation termination, translation, and peptide biosynthetic process. The down-regulated genes were enriched in focal adhesion, VEGFR3 signaling in lymphatic endothelium, extracellular matrix organization, and extracellular matrix. The current study screened the genes in the PPI network, extracted modules, miRNA-target genes regulatory network, and TF-target genes regulatory network with higher degrees as hub genes, which included NPM1, CUL4A, YIPF1, SHC1, AKT1, VLDLR, RPL14, P3H2, DTNA, FAM126B, RPL34, and MYL5. Survival analysis indicated that the high expression of RPL36A and MRPL35 were predicting longer survival of GBM, while high expression of AP1S1 and AKAP12 were predicting shorter survival of GBM. High expression of RPL36A and AP1S1 were associated with pathogenesis of GBM, while low expression of ALPL was associated with pathogenesis of GBM. In conclusion, the current study diagnosed DEGs between scrambled shRNA expression and Lin7A knock down samples, which could improve our understanding of the molecular mechanisms in the progression of GBM, and these crucial as well as new diagnostic markers might be used as therapeutic targets for GBM.
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Project description:Small molecules that target microtubules (MTs) represent promising therapeutics to treat certain types of cancer, including glioblastoma multiform (GBM). We synthesized modified carbazoles and evaluated their antitumor activity in GBM cells in culture. Modified carbazoles with an ethyl moiety linked to the nitrogen of the carbazole and a carbonyl moiety linked to distinct biaromatic rings exhibited remarkably different killing activities in human GBM cell lines and patient-derived GBM cells, with IC50 values from 67 to >10,000 nM. Measures of the activity of modified carbazoles with tubulin and microtubules coupled to molecular docking studies show that these compounds bind to the colchicine site of tubulin in a unique low interaction space that inhibits tubulin assembly. The modified carbazoles reported here represent novel chemical tools to better understand how small molecules disrupt MT functions and kill devastating cancers such as GBM.
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Project description:Tryptophan (Trp)-catabolic enzymes (TCEs) produce metabolites that activate the aryl hydrocarbon receptor (AHR) and promote tumor progression and immunosuppression in glioblastoma. As therapies targeting TCEs or AHR become available, a better understanding of Trp metabolism is required. Methods: The combination of LC-MS/MS with chemical isobaric labeling enabled the simultaneous quantitative comparison of Trp and its amino group-bearing metabolites in multiple samples. We applied this method to the sera of a cohort of 43 recurrent glioblastoma patients and 43 age- and sex-matched healthy controls. Tumor volumes were measured in MRI data using an artificial neural network-based approach. MALDI MSI visualized Trp and its direct metabolite N-formylkynurenine (FK) in glioblastoma tissue. Analysis of scRNA-seq data was used to detect the presence of Trp metabolism and AHR activity in different cell types in glioblastoma. Results: Compared to healthy controls, glioblastoma patients showed decreased serum Trp levels. Surprisingly, the levels of Trp metabolites were also reduced. The decrease became smaller with more enzymatic steps between Trp and its metabolites, suggesting that Trp availability controls the levels of its systemic metabolites. High tumor volume associated with low systemic metabolite levels and low systemic kynurenine levels associated with worse overall survival. MALDI MSI demonstrated heterogeneity of Trp catabolism across glioblastoma tissues. Analysis of scRNA-seq data revealed that genes involved in Trp metabolism were expressed in almost all the cell types in glioblastoma and that most cell types, in particular macrophages and T cells, exhibited AHR activation. Moreover, high AHR activity associated with reduced overall survival in the glioblastoma TCGA dataset. Conclusion: The novel techniques we developed could support the identification of patients that may benefit from therapies targeting TCEs or AHR activation.
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Project description:BackgroundAlzheimer's disease (AD) is the most common cause of dementia among the elderly. To clarify pathogenesis of AD, thousands of reports have been accumulating. However, knowledge of signaling pathways in the field of AD has not been compiled as a database before.DescriptionHere, we have constructed a publicly available pathway map called "AlzPathway" that comprehensively catalogs signaling pathways in the field of AD. We have collected and manually curated over 100 review articles related to AD, and have built an AD pathway map using CellDesigner. AlzPathway is currently composed of 1347 molecules and 1070 reactions in neuron, brain blood barrier, presynaptic, postsynaptic, astrocyte, and microglial cells and their cellular localizations. AlzPathway is available as both the SBML (Systems Biology Markup Language) map for CellDesigner and the high resolution image map. AlzPathway is also available as a web service (online map) based on Payao system, a community-based, collaborative web service platform for pathway model curation, enabling continuous updates by AD researchers.ConclusionsAlzPathway is the first comprehensive map of intra, inter and extra cellular AD signaling pathways which can enable mechanistic deciphering of AD pathogenesis. The AlzPathway map is accessible at http://alzpathway.org/.
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Project description:Alzheimer's disease (AD) is an aging-related neurodegenerative disease and accounts for majority of human dementia. The hyper-phosphorylated tau-mediated intracellular neurofibrillary tangle and amyloid ?-mediated extracellular senile plaque are characterized as major pathological lesions of AD. Different from the dysregulated growth control and ample genetic mutations associated with human cancers, AD displays damage and death of brain neurons in the absence of genomic alterations. Although various biological processes predominately governing tumorigenesis such as inflammation, metabolic alteration, oxidative stress and insulin resistance have been associated with AD genesis, the mechanistic connection of these biological processes and signaling pathways including mTOR, MAPK, SIRT, HIF, and the FOXO pathway controlling aging and the pathological lesions of AD are not well recapitulated. Hence, we performed a thorough review by summarizing the physiological roles of these key cancer-related signaling pathways in AD pathogenesis, comprising of the crosstalk of these pathways with neurofibrillary tangle and senile plaque formation to impact AD phenotypes. Importantly, the pharmaceutical investigations of anti-aging and AD relevant medications have also been highlighted. In summary, in this review, we discuss the potential role that cancer-related signaling pathways may play in governing the pathogenesis of AD, as well as their potential as future targeted strategies to delay or prevent aging-related diseases and combating AD.
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Project description:BackgroundAlthough increasing evidence shows that immune infiltration plays an essential role in glioblastoma (GBM), current prognostic indicators do not accurately represent the risk of immune cells infiltration in patients. It is therefore critical to identify new prognostic markers for GBM. Here, we investigated the effectiveness of using immunoscore to improve risk stratification and prediction of prognosis in GBM patients receiving chemotherapy.MethodsUsing mRNA microarrays and CIBERSORT, we analyzed 22 types of immune cell fractions in 517 GBM samples and characterized an immunoscore using the least absolute shrinkage and selection operator (LASSO) Cox regression model based on the fraction of immune cell types and patients' overall survival. The prognostic and predictive accuracy of immunoscore was verified in the validation cohort and the entire cohort.ResultsUsing the LASSO model, an immunoscore was developed to classify patients into High and Low immunoscore groups in the training cohort (P < 0.0001) based on the fraction of eight immune cell types. The immunoscore performance was validated in the validation cohort (P < 0.0001) and the entire cohort (P < 0.0001). Furthermore, a nomogram comprising age, IDH1 status, and immunoscore was generated to predict one- and three-year survival rates in the training cohort. The predictive value of the immunoscore was also confirmed in the validation cohort and the entire cohort (C-index: 0.66, 0.67, and 0.68, respectively). In addition, we concluded that patients in the low-immunoscore group may benefit from adjuvant chemotherapy for GBM.ConclusionImmunoscore, an immune-infiltration-based signature, is a reliable prognostic and predictive tool for GBM.
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Project description:Gliomablastoma multiform (GBM) is the most fatal form of all brain cancers in humans. Currently there are limited diagnostic tools for GBM detection. Here, we applied surprisal analysis, a theory grounded in thermodynamics, to unveil how biomolecule energetics, specifically a redistribution of free energy amongst microRNAs (miRNAs), results in a system deviating from a non-cancer state to the GBM cancer -specific phenotypic state. Utilizing global miRNA microarray expression data of normal and GBM patients tumors, surprisal analysis characterizes a miRNA system response capable of distinguishing GBM samples from normal tissue biopsy samples. We indicate that the miRNAs contributing to this system behavior is a disease phenotypic state specific to GBM and is therefore a unique GBM-specific thermodynamic signature. MiRNAs implicated in the regulation of stochastic signaling processes crucial in the hallmarks of human cancer, dominate this GBM-cancer phenotypic state. With this theory, we were able to distinguish with high fidelity GBM patients solely by monitoring the dynamics of miRNAs present in patients' biopsy samples. We anticipate that the GBM-specific thermodynamic signature will provide a critical translational tool in better characterizing cancer types and in the development of future therapeutics for GBM.
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