Identification of metabolites in plasma for predicting survival in glioblastoma

Shen, Jie; Song, Renduo; Hodges, Tiffany R.; Heimberger, Amy B.; Zhao, Hua

doi:10.1002/mc.22815

Cited by 25 publications

(19 citation statements)

References 34 publications

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“…Circulating-metabolite-based prognostic models have previously been shown to have its promising clinical applications in several cancers, such as glioblastoma (34), non-small cell lung cancer (35), and esophageal adenocarcinoma (24). However, the previous metabolomic studies of ESCC solely focused on the diagnostic value of the metabolites and rarely assessed their prognostic significance.…”

Section: Discussionmentioning

confidence: 99%

Combined Metabolomic Analysis of Plasma and Tissue Reveals a Prognostic Risk Score System and Metabolic Dysregulation in Esophageal Squamous Cell Carcinoma

et al. 2020

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Background: Esophageal squamous cell carcinoma (ESCC) is a gastrointestinal malignancy with a poor prognosis. Although studies have shown metabolic reprogramming to be linked to ESCC development, no prognostic metabolic biomarkers or potential therapeutic metabolic targets have been identified. Method: The present study investigated some circulating metabolites associated with overall survival in 276 curatively resected ESCC patients using liquid chromatography/mass spectrometry metabolomics and Kaplan-Meier analysis. Tissue metabolomic analysis of 23-paired ESCC tissue samples was performed to discover metabolic dysregulation in ESCC cancerous tissue. A method consisting of support vector machine recursive feature elimination and LIMMA differential expression analysis was utilized to select promising feature genes within transcriptomic data from 179-paired ESCC tissue samples. Joint pathway analysis with genes and metabolites identified relevant metabolic pathways and targets for ESCC. Results: Four metabolites, kynurenine, 1-myristoyl-glycero-3-phosphocholine (LPC(14:0)sn-1), 2-piperidinone, and hippuric acid, were identified as prognostic factors in the preoperative plasma from ESCC patients. A risk score consisting of kynurenine and LPC(14:0)sn-1 significantly improved the prognostic performance of the tumor-node-metastasis staging system and was able to stratify risk for ESCC. Combined tissue metabolomic analysis and support vector machine recursive feature elimination gene selection revealed dysregulated kynurenine pathway as an important metabolic feature of ESCC, including accumulation of tryptophan, formylkynurenine, and kynurenine, as well as up-regulated indoleamine 2,3-dioxygenase 1 in ESCC cancerous tissue. Chen et al. Metabolic Features of ESCC Conclusions: This work identified for the first time four potential prognostic circulating metabolites. In addition, kynurenine pathway metabolism was shown to be up-regulated tryptophan-kynurenine metabolism in ESCC. Results not only provide a metabolite-based risk score system for prognosis, but also improve the understanding of the molecular basis of ESCC onset and progression, and as well as novel potential therapeutic targets for ESCC.

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Section: Discussionmentioning

confidence: 99%

Combined Metabolomic Analysis of Plasma and Tissue Reveals a Prognostic Risk Score System and Metabolic Dysregulation in Esophageal Squamous Cell Carcinoma

et al. 2020

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“…Presence of the IDH mutation led to higher levels of N -acetylputrescine, trimethylamine- N -oxide, niacin, arginine, glucosamine, and methionine in the plasma [ 210 ]. Plasma metabolomics was also used to predict the GBM patient survival, and higher levels of arginine and methionine are correlated with increased probability of survival, while higher levels of kynurenate with decreased probability [ 211 ]. An exploratory plasma NMR metabolomics study indicated that the levels of creatine, citrate, glucose, pyruvate, and glutamine can be used to discriminate between primary brain tumor patients (among them GBM patients) and healthy controls [ 212 ].…”

Section: The Metabolome—a Way Of Diagnosis and Prognosis Of Gliomamentioning

confidence: 99%

Take Advantage of Glutamine Anaplerosis, the Kernel of the Metabolic Rewiring in Malignant Gliomas

et al. 2020

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Glutamine is a non-essential amino acid that plays a key role in the metabolism of proliferating cells including neoplastic cells. In the central nervous system (CNS), glutamine metabolism is particularly relevant, because the glutamine-glutamate cycle is a way of controlling the production of glutamate-derived neurotransmitters by tightly regulating the bioavailability of the amino acids in a neuron-astrocyte metabolic symbiosis-dependent manner. Glutamine-related metabolic adjustments have been reported in several CNS malignancies including malignant gliomas that are considered ‘glutamine addicted’. In these tumors, glutamine becomes an essential amino acid preferentially used in energy and biomass production including glutathione (GSH) generation, which is crucial in oxidative stress control. Therefore, in this review, we will highlight the metabolic remodeling that gliomas undergo, focusing on glutamine metabolism. We will address some therapeutic regimens including novel research attempts to target glutamine metabolism and a brief update of diagnosis strategies that take advantage of this altered profile. A better understanding of malignant glioma cell metabolism will help in the identification of new molecular targets and the design of new therapies.

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“…Compared with m / z 477.3938 and m / z 441.3727 from RPDQ, the diagnostic ion m / z 493.3866 and m / z 457.3649 was 16 Da higher than them. Therefore, M2 was identified as an oxidrolysis metabolite of RPDQ [ 25 ].…”

Section: Resultsmentioning

confidence: 99%

Pharmacokinetic and Metabolism Studies of 12-Riboside-Pseudoginsengenin DQ by UPLC-MS/MS and UPLC-QTOF-MSE

et al. 2018

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Pharmacokinetic and metabolism studies of 12-riboside-pseudoginsengenin DQ (RPDQ), a novel ginsenoside with an anti-cancer effect, were carried out, aiming at discussing the characteristics of the ginsenoside with glycosylation site at C-12. In the pharmacokinetic analysis, we developed and validated a method by UPLC-MS to quantify RPDQ in rat plasma. In the range of 5–1000 ng/mL, the assay was linear (R2 > 0.9966), with the LLOQ (lower limit of quantification) being 5 ng/mL. The LOD (limit of detection) was 1.5 ng/mL. The deviations of intra-day and inter-day, expressed as relative standard deviation (RSD), were ≤ 3.51% and ≤ 5.41% respectively. The accuracy, expressed as relative error (RE), was in the range –8.82~3.47% and –5.61~2.87%, respectively. The recoveries were in the range 85.66~92.90%. The method was then applied to a pharmacokinetic study in rats intragastrically administrated with 6, 12, and 24 mg/kg RPDQ. The results showed that RPDQ exhibited slow oral absorption (Tmax = 7.0 h, 7.5 h, and 7.0 h, respectively), low elimination (t1/2 = 12.59 h, 12.83 h, and 13.74 h, respectively) and poor absolute bioavailability (5.55, 5.15, and 6.08%, respectively). Moreover, the investigation of metabolites were carried out by UPLC-QTOF-MS. Thirteen metabolites of RPDQ were characterized from plasma, bile, urine, and feces of rats. Some metabolic pathways, including oxidation, acetylation, hydration, reduction, hydroxylation, glycine conjugation, sulfation, phosphorylation, glucuronidation, glutathione conjugation, and deglycosylation, were profiled. In general, both the rapid quantitative method and a good understanding of the characteristics of RPDQ in vivo were provided in this study.

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Identification of metabolites in plasma for predicting survival in glioblastoma

Cited by 25 publications

References 34 publications

Combined Metabolomic Analysis of Plasma and Tissue Reveals a Prognostic Risk Score System and Metabolic Dysregulation in Esophageal Squamous Cell Carcinoma

Combined Metabolomic Analysis of Plasma and Tissue Reveals a Prognostic Risk Score System and Metabolic Dysregulation in Esophageal Squamous Cell Carcinoma

Take Advantage of Glutamine Anaplerosis, the Kernel of the Metabolic Rewiring in Malignant Gliomas

Pharmacokinetic and Metabolism Studies of 12-Riboside-Pseudoginsengenin DQ by UPLC-MS/MS and UPLC-QTOF-MSE

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