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Proteomic alterations in young GBM: confirmation with western analysis. Western blotting replicates the alterations in defined proteins in GBM in a subset (determined by tissue availability) from the same subjects as used in the proteomic 2D gel electrophoresis. a 2D gel electrophoresis identified a consistent significant (p = 1.8E−06) reduction in DPYSL2 in young GBM. b Western blot analysis identified a similar consistent reduction in DPYSL2 in young GBM. c 2D gel electrophoresis identified a significant increase (p = 0.0057) in Sorcin (though with inter-subject variability) in young GBM. d Western blot analysis identified a similar increase in Sorcin in young GBM, again with inter-subject variability. There was also good correspondence between 2D gel electrophoresis and western blot analysis in young GBM for all 10 proteins examined with both techniques (see Supplementary Fig. 3)
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Increasing age is an important prognostic variable in glioblastoma (GBM). We have defined the proteomic response in GBM samples from 7 young patients (mean age 36 years) compared to peritumoural-control samples from 10 young patients (mean age 32 years). 2-Dimensional-gel-electrophoresis, image analysis, and protein identification (LC/MS) were perf...
Citations
... GBM, like most malignant cancers, is driven by glucose and glutamine fermentation through the glycolysis and glutaminolysis pathways, respectively (22)(23)(24)(25)(26)(27). The dependency on glucose and glutamine fermentation arises from inefficient oxidative phosphorylation (OxPhos) that is linked to abnormalities in the number, structure, and function of mitochondria in GBM tissue (17,(26)(27)(28)(29)(30)(31)(32)(33). Surgical debulking followed by radiotherapy inadvertently increases the availability of glucose and glutamine in the tumor microenvironment (17,34,35). ...
Background: Successful treatment of glioblastoma (GBM) remains futile despite decades of intense research. GBM is similar to most other malignant cancers in requiring glucose and glutamine for growth, regardless of histological or genetic heterogeneity. Ketogenic metabolic therapy (KMT) is a non-toxic nutritional intervention for cancer management. We report the case of a 32-year-old man who presented in 2014 with seizures and a right frontal lobe tumor on MRI. The tumor cells were immunoreactive with antibodies to the IDH1 (R132H) mutation, P53 (patchy), MIB-1 index (4–6%), and absent ATRX protein expression. DNA analysis showed no evidence of methylation of the MGMT gene promoter. The presence of prominent microvascular proliferation and areas of necrosis were consistent with an IDH-mutant glioblastoma (WHO Grade 4).
Methods: The patient refused standard of care (SOC) and steroid medication after initial diagnosis, but was knowledgeable and self-motivated enough to consume a low-carbohydrate ketogenic diet consisting mostly of saturated fats, minimal vegetables, and a variety of meats. The patient used the glucose ketone index calculator to maintain his Glucose Ketone Index (GKI) near 2.0 without body weight loss.
Results: The tumor continued to grow slowly without expected vasogenic edema until 2017, when the patient opted for surgical debulking. The enhancing area, centered in the inferior frontal gyrus, was surgically excised. The pathology specimen confirmed IDH1-mutant GBM. Following surgery, the patient continued with a self-administered ketogenic diet to maintain low GKI values, indicative of therapeutic ketosis. At the time of this report (May 2021), the patient remains alive with a good quality of life, except for occasional seizures. MRI continues to show slow interval progression of the tumor.
Conclusion: This is the first report of confirmed IDH1-mutant GBM treated with KMT and surgical debulking without chemo- or radiotherapy. The long-term survival of this patient, now at 80 months, could be due in part to a therapeutic metabolic synergy between KMT and the IDH1 mutation that simultaneously target the glycolysis and glutaminolysis pathways that are essential for GBM growth. Further studies are needed to determine if this non-toxic therapeutic strategy could be effective in providing long-term management for other GBM patients with or without IDH mutations.
... Young GBM patients show a better prognosis than older GBM patients. To identify the molecular features responsible for better prognosis in young GBM patients, Deighton et al. performed tissue proteomic analysis of young and old GBM patients using classical 2DE method and identified upregulation of phosphatidylethanolamine binding protein 1 (PEBP1) in young GBM patients, which inhibits pro-oncogenic signaling pathways, while this protein was downregulated in old GBM patients, thus indicating its importance as potential prognostic marker in GBMs [43]. Cristolysis of mitochondria from GBM patients results in increased oxidative damage and decreased electron transport chain proteins in GBMs [44]. ...
Introduction: Despite being rare cancers, gliomas account for a significant number of cancer related deaths. Identification and treatment of these tumors at an early stage would greatly improve the therapeutic outcomes. There is an urgent need for diagnostic and prognostic markers, which can identify disease early and discriminate the subtypes of these tumors thereby improving the existing treatment modalities.
Areas covered: In this article we have reviewed published literature on proteomics biomarkers for gliomas and their importance in diagnosis or prognosis. Proteomic studies for the discovery of protein, autoantibody biomarkers and biological pathway alterations in serum, CSF and tumor biopsies have been discussed in this review.
Expert commentary: The rapid development in the field of mass spectrometry and increased sensitivity and reproducibility in assays has led to the identification and quantification of large number of proteins very precisely. Though, genomic markers are the prime focus in the classification of gliomas, incorporating protein markers would further improve the existing classification. In this regard, data mining and studies on large cohorts of glioma patients would help in the identification of diagnostic and prognostic markers ultimately translating to the clinics.
... Other important individual prognostic factors are the age of diagnosis, the Karnofsky performance status and the methylation status of the MGMT promoter gene [65]. A comparative proteomic analysis of young and old glioblastoma patients identified multiple differentially expressed proteins involved in the regulation of the tumorigenesis that could explain the prognostic differences between young and old glioblastoma patients [38]. Among the differentially expressed proteins of special interest is the Phosphatidylethanolamine-binding protein 1 (PEBP1), an inhibitor to both Raf/MEK/ERK and nuclear factor kappa B pathways [66]. ...
... Among the differentially expressed proteins of special interest is the Phosphatidylethanolamine-binding protein 1 (PEBP1), an inhibitor to both Raf/MEK/ERK and nuclear factor kappa B pathways [66]. PEPB1 was up-regulated in young glioblastoma and down-regulated in old glioblastoma patients [38]. The regulation differences of PEPB1 between young and old glioblastoma patients should be further investigated to understand its influence on prognosis. ...
Being the fourth leading cause of cancer-related death, glial tumors are highly diverse tumor entities characterized by important heterogeneity regarding tumor malignancy and prognosis. However, despite the identification of important alterations in the genome of the glial tumors, there remains a gap in understanding the mechanisms involved in glioma malignancy. Previous research focused on decoding the genomic alterations in these tumors, but due to intricate cellular mechanisms, the genomic findings do not correlate with the functional proteins expressed at the cellular level. The development of mass spectrometry (MS) based proteomics allowed researchers to study proteins expressed at the cellular level or in serum that may provide new insights on the proteins involved in the proliferation, invasiveness, metastasis and resistance to therapy in glial tumors. The integration of data provided by genomic and proteomic approaches into clinical practice could allow for the identification of new predictive, diagnostic and prognostic biomarkers that will improve the clinical management of patients with glial tumors. This paper aims to provide an updated review of the recent proteomic findings, possible clinical applications, and future research perspectives in diffuse astrocytic and oligodendroglial tumors, pilocytic astrocytomas, and ependymomas.
... Since proteins are the ultimate biological effectors of the cells, in this study we have analyzed the total proteome of residual resistant cells of glioma [10][11][12][13]. Till date majority of proteomics studies in glioblastoma have focused on identification of differential proteins amongst different GBM cell lines, patient samples or within the same tumour to investigate the heterogeneity of glioblastoma, mechanism of chemoresistance and identification of diagnostic biomarkers [14][15][16][17][18][19][20][21][22][23][24][25][26]. However, none of these studies could identify survival mechanism of innately resistant cells due to their unavailability. ...
Therapy resistance and recurrence in Glioblastoma is due to the presence of residual radiation resistant cells. However, because of their inaccessibility from patient biopsies, the molecular mechanisms driving their survival remain unexplored. Residual Radiation Resistant (RR) and Relapse (R) cells were captured using cellular radiation resistant model generated from patient derived primary cultures and cell lines. iTRAQ based quantitative proteomics was performed to identify pathways unique to RR cells followed by in vitro and in vivo experiments showing their role in radio-resistance. 2720 proteins were identified across Parent (P), RR and R population with 824 and 874 differential proteins in RR and R cells. Unsupervised clustering showed proteasome pathway as the most significantly deregulated pathway in RR cells. Concordantly, the RR cells displayed enhanced expression and activity of proteasome subunits, which triggered NFkB signalling. Pharmacological inhibition of proteasome activity led to impeded NFkB transcriptional activity, radio-sensitization of RR cells in vitro, and significantly reduced capacity to form orthotopic tumours in vivo. We demonstrate that combination of proteasome inhibitor with radio-therapy abolish the inaccessible residual resistant cells thereby preventing GBM recurrence. Furthermore, we identified first proteomic signature of RR cells that can be exploited for GBM therapeutics.
... It has also been shown that NF-kB induces cancer development and tumor progression by inducing a proinflammatory microenvironment and even leads to chemotherapy resistance (4,43). NF-kB regulates protein transcription for the down-regulation of apoptosis and increases cell invasion, angiogenesis, and vascular permeability in the cell nucleus (12). Chin et al. ...
Aim:
To investigate the apoptotic and molecular effects of glycogen synthase kinase-3 (GSK-3) in glioblastoma multiforme (GBM).
Material and methods:
Human primary glioblastoma cell line (U-87 MG) and the human fetal glial cell line (SVGp12) were used. The cells were exposed to the different doses of GSK inhibitor for 24, 48 and 72 hours. Induction of apoptosis was assessed by DNA fragmentation (TUNEL) assay. EGFR and NF-kB expression was evaluated by immunofluorescence analyses.
Results:
GSK-3 inhibitor IX induced cytotoxicity and apoptosis in dose-dependent manner in GBM cells. Our results indicated that GSK-3 inhibitor IX induces apoptosis, resulting in a significant decrease in the expression of NF-kB and EGF.
Conclusion:
Inhibition through GSK-3 has been found promising in creating therapeutic management of GBM cells. Proliferation, differentiation, cell cycle regulation, and apoptosis are mechanisms that must be interpreted as a whole. Components associated with EGFR, NF-kB, and apoptosis affect the mechanism solely and collectively. Our collective data suggest that GSK-3 inhibitor IX inhibited cellular proliferation and induced apoptotic events by modulating EGFR and NF-kB expression in GBM cells. GSK-3 inhibition holds promise for the development of new approaches for the therapeutic management of GBM cells.
... UCHL1 is a gene encoding a thiol protease that is specifically expressed in neurons, and it has been strongly associated with a poor prognosis when overexpressed in LC as it seems to promote metastasis by abrogating HIF-1α ubiquitination 27 . By contrast, this gene is down-regulated in GBM brains 28 , increasing NF-κB activity by activating IKK 29 , and in AD mouse brains where its up-regulation improves memory deficits 30 . ...
Epidemiological studies indicate that patients suffering from Alzheimer's disease have a lower risk of developing lung cancer, and suggest a higher risk of developing glioblastoma. Here we explore the molecular scenarios that might underlie direct and inverse co-morbidities between these diseases. Transcriptomic meta-analyses reveal significant numbers of genes with inverse patterns of expression in Alzheimer's disease and lung cancer, and with similar patterns of expression in Alzheimer's disease and glioblastoma. These observations support the existence of molecular substrates that could at least partially account for these direct and inverse co-morbidity relationships. A functional analysis of the sets of deregulated genes points to the immune system, up-regulated in both Alzheimer's disease and glioblastoma, as a potential link between these two diseases. Mitochondrial metabolism is regulated oppositely in Alzheimer's disease and lung cancer, indicating that it may be involved in the inverse co-morbidity between these diseases. Finally, oxidative phosphorylation is a good candidate to play a dual role by decreasing or increasing the risk of lung cancer and glioblastoma in Alzheimer's disease.
... Positive Negative FOXO1 MBTPS1 [76], RBP4 [77], HN1 [78], SSTR2 [79], LNX1 [80], ENO2 [81], CDK5 [82], ABCC1 [83], EFNB3 [84], SMYD3 [85], HABP4 [86], PFKM [87], ITPR1 [88], NNAT [89], CYFIP2 [90], ARG2 [91], ARF3 [92], HSPA12A [93], MYO10 [94], DCTN2 [95], ACTR3B [96], MTCH1 [97], SLC1A6 [98], NBEA [99], MAP2K4 [100], TNPO2 [101], MOAP1 [102], ARPP21 [103], CADM3 [104], KCNA4 [105], SVOP [106], REPS2 [107], SLIT2 [108], PANX1 [109], CCT7 [110], KCNMA1 [111], CACNA2D3 [112], KCNV1 [113], PIK3CB [114], NPTX1 [98], CDH18 [115], GLS2 [116], NRIP3 [98], TACC2 [117], CALM3 [118], NELL2 [119], CBX7 [120], MTA3 [121], AJAP1 [122,123], PARK2 [124], PI4KA [125] CARHSP1 MADD [126], STXBP1 [127], TMX4 [128], GLS [116], KCNK3 [129], MAP2 [130], YWHAB [131], PANK2 [132], UCK2 [133], ATXN10 [134], ATP1A1 [135], EPB41L4B [136], DRP2 [137], CALM1 [138], CHAF1B [139] ST6GALNAC5 [140], OLFM3 [141], PCDH8 [142], PRPF19 [143], CHGB [144], DUSP4 [145], SLC32A1 [146], PPP1R14C [147], MACROD2 [148], ATP6V1B2 [149], YWHAH [97], CERS6 [150], SCG2 [151], GRM7 [ that the general preponderance of GO terms with clear relevance to brain development and function indicates that the resulting network represents genuine and meaningful relationships between the genes present in the network. While the gene expression data used in this study came from the same source, this is not a requirement for the method to be viable. ...
Differential co-expression network analyses have recently become an important step in the investigation of cellular differentiation and dysfunctional gene-regulation in cell and tissue disease-states. The resulting networks have been analyzed to identify and understand pathways associated with disorders, or to infer molecular interactions. However, existing methods for differential co-expression network analysis are unable to distinguish between various forms of differential co-expression. To close this gap, here we define the three different kinds (conserved, specific, and differentiated) of differential co-expression and present a systematic framework, CSD, for differential co-expression network analysis that incorporates these interactions on an equal footing. In addition, our method includes a subsampling strategy to estimate the variance of co-expressions. Our framework is applicable to a wide variety of cases, such as the study of differential co-expression networks between healthy and disease states, before and after treatments, or between species. Applying the CSD approach to a published gene-expression data set of cerebral cortex and basal ganglia samples from healthy individuals, we find that the resulting CSD network is enriched in genes associated with cognitive function, signaling pathways involving compounds with well-known roles in the central nervous system, as well as certain neurological diseases. From the CSD analysis, we identify a set of prominent hubs of differential co-expression, whose neighborhood contains a substantial number of genes associated with glioblastoma. The resulting gene-sets identified by our CSD analysis also contain many genes that so far have not been recognized as having a role in glioblastoma, but are good candidates for further studies. CSD may thus aid in hypothesis-generation for functional disease-associations.
... Previous proteomics studies have been reported on Glioma. These include a large panel of different studies based of different methodologies and including profiles determined from glioma cell lines [49][50][51], animal models [49][50][51][52], patients fluids [50,51,53] and patients samples [50,51,[53][54][55][56][57][58][59]. To date, mainly two methodologies were used for such glioma proteomics studies namely gel-based protein profiling [56,57,59] and quantitative proteomics [55,[60][61][62][63]. ...
... These include a large panel of different studies based of different methodologies and including profiles determined from glioma cell lines [49][50][51], animal models [49][50][51][52], patients fluids [50,51,53] and patients samples [50,51,[53][54][55][56][57][58][59]. To date, mainly two methodologies were used for such glioma proteomics studies namely gel-based protein profiling [56,57,59] and quantitative proteomics [55,[60][61][62][63]. However, none of these previous studies used MALDI MSI methodology coupled to label-free tissue microproteomics [33,34,64]. ...
... For the verification of the objective function-'GBM_BM' in representing the properties of glioblastoma, a qualitative analysis was performed to compare the activity of certain reported reactions in the astrocytic and glioblastoma scenario. The fold change in activity from astrocytic to glioblastoma scenario as predicted from the model was compared to existing proteome data extracted from young glioblastoma patients (Deighton et al. 2014). The results of this comparison are listed in Table 1. ...
... Also, through the in silico approach, we tried to gain some insight into the alternative metabolic routes and metabolites which contributed to the metabolic heterogeneity of glioblastoma. The present model was capable of yielding results which were in correspondence to the experimentally proved phenomena of both astrocytes and glioblastoma (Deighton et al. 2014;Mangia et al. 2009;Marrif and Juurlink 1999;Pellerin and Magistretti 1994;Wise et al. 2008;Zhou et al. 2011). From our study, specific pathways were observed to demonstrate a co-operative effect in the astrocyte and glioblastoma scenarios. ...
Brain cancers demonstrate a complex metabolic behavior so as to adapt the external hypoxic environment and internal stress generated by reactive oxygen species. To survive in these stringent conditions, glioblastoma cells develop an antagonistic metabolic phenotype as compared to their predecessors, the astrocytes, thereby quenching the resources expected for nourishing the neurons. The complexity and cumulative effect of the large scale metabolic functioning of glioblastoma is mostly unexplored. In this study, we reconstruct a metabolic network comprising of pathways that are known to be deregulated in glioblastoma cells as compared to the astrocytes. The network, consisted of 147 genes encoding for enzymes performing 247 reactions distributed across five distinct model compartments, was then studied using constrained-based modeling approach by recreating the scenarios for astrocytes and glioblastoma, and validated with available experimental evidences. From our analysis, we predict that glycine requirement of the astrocytes are mostly fulfilled by the internal glycine–serine metabolism, whereas glioblastoma cells demand an external uptake of glycine to utilize it for glutathione production. Also, cystine and glucose were identified to be the major contributors to glioblastoma growth. We also proposed an extensive set of single and double lethal reaction knockouts, which were further perturbed to ascertain their role as probable chemotherapeutic targets. These simulation results suggested that, apart from targeting the reactions of central carbon metabolism, knockout of reactions belonging to the glycine–serine metabolism effectively reduce glioblastoma growth. The combinatorial targeting of glycine transporter with any other reaction belonging to glycine–serine metabolism proved lethal to glioblastoma growth.
... Based on numerous findings in human glioma cell lines and tissues, several research groups suggested that the majority of malignant brain tumors are incapable of producing adequate amounts of energy through oxidative phosphorylation [78,79,91,92,101,[103][104][105]. Besides these ultrastructure findings, Renner and co-workers showed that tumor cells isolated from human GBM could produce ATP in the presence of potassium cyanide [106]. ...
