Inhibition of guanosine monophosphate synthetase (<scp>GMPS</scp>) blocks glutamine metabolism and prostate cancer growth

Wang, Qian; Guan, Yi Fang; Hancock, Sarah; Wahi, Kanu; Geldermalsen, Michelle van; Zhang, Blake K.; Pang, Angel; Nagarajah, Rajini; Mak, Blossom; Freidman, Natasha; Horvath, Lisa G.; Turner, Nigel; Holst, Jeff

doi:10.1002/path.5665

Cited by 22 publications

(20 citation statements)

References 35 publications

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“…We found some features that can distinguish multiple cancer types with high impacts (passed counts >= 100). For example, the feature GO:0019002 (GMP binding) can classify BRCA, COADREAD, KIRC and OV, which is expected because GMP is the pharmacological target for treating multiple types of cancers ( 53 – 55 ). Another GO term group that can be used to classify multiple cancer type contains two GO terms, namely, GO:0031049 (programmed DNA elimination) and GO:0031052 (programmed DNA elimination by chromosome breakage), which are also involved in oncogenesis.…”

Section: Discussionmentioning

confidence: 99%

Functional and embedding feature analysis for pan-cancer classification

Ren

et al. 2022

Front. Oncol.

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With the increasing number of people suffering from cancer, this illness has become a major health problem worldwide. Exploring the biological functions and signaling pathways of carcinogenesis is essential for cancer detection and research. In this study, a mutation dataset for eleven cancer types was first obtained from a web-based resource called cBioPortal for Cancer Genomics, followed by extracting 21,049 features from three aspects: relationship to GO and KEGG (enrichment features), mutated genes learned by word2vec (text features), and protein-protein interaction network analyzed by node2vec (network features). Irrelevant features were then excluded using the Boruta feature filtering method, and the retained relevant features were ranked by four feature selection methods (least absolute shrinkage and selection operator, minimum redundancy maximum relevance, Monte Carlo feature selection and light gradient boosting machine) to generate four feature-ranked lists. Incremental feature selection was used to determine the optimal number of features based on these feature lists to build the optimal classifiers and derive interpretable classification rules. The results of four feature-ranking methods were integrated to identify key functional pathways, such as olfactory transduction (hsa04740) and colorectal cancer (hsa05210), and the roles of these functional pathways in cancers were discussed in reference to literature. Overall, this machine learning-based study revealed the altered biological functions of cancers and provided a reference for the mechanisms of different cancers.

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Section: Discussionmentioning

confidence: 99%

Functional and embedding feature analysis for pan-cancer classification

Ren

et al. 2022

Front. Oncol.

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“…Its cancer-promoting effect and mechanism in HCC have also been widely studied [ 45 , 46 ]. A recent report showed that the inhibition of GMPS could block glutamine metabolism and the growth of prostate cancer [ 47 ]. Kerstin Holzer et al revealed that GMPS was a target for p53 inhibition in hepatocellular carcinoma by proteomic analysis [ 48 ].…”

Section: Discussionmentioning

confidence: 99%

Comprehensive Analysis of Histone Modifications in Hepatocellular Carcinoma Reveals Different Subtypes and Key Prognostic Models

Xiao

et al. 2022

Journal of Oncology

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Histone modification, an important epigenetic mechanism, is related to the carcinogenesis of hepatocellular carcinoma (HCC). In three datasets, we screened 88 epigenetic-dysregulated PCGs (epi-PCGs) , which were significantly associated with HCC survival and could cluster HCC into three molecular subtypes. These subtypes were associated with prognosis, immunomodulatory alterations, and response to different treatment strategies. Based on 88 epi-PCGs in the TCGA training set, a risk prediction model composed of 4 epi-PCGs was established. The model was closely related to the clinicopathological features and showed a strong predictive ability in different clinical subgroups. In addition, the risk prediction model was an independent prognostic factor for patients with HCC. The significance of epi-PCGs in HCC is revealed by our data analysis.

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“…It is also being tested in other cancer types [ 30 ]. Another recent study revealed that the inhibition of guanosine monophosphate synthetase, which uses glutamine as a source of nitrogen to synthesise guanine, decreased the tumour growth in PC-3 xenograft models [ 31 ].…”

Section: Discussionmentioning

confidence: 99%

Glutamine and Cholesterol Plasma Levels and Clinical Outcomes of Patients with Metastatic Castration-Resistant Prostate Cancer Treated with Taxanes

Marín-Aguilera

Pereira

Jiménez

et al. 2021

Cancers

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Altered metabolism is a hallmark of cancer. Malignant cells metabolise glutamine to fulfil their metabolic needs. In prostate cancer, androgen receptor signalling promotes glutamine metabolism, which is also involved in cholesterol homeostasis. We aimed to determine whether the plasma glutamine levels correlate with the blood lipid profile, clinical characteristics and outcomes in patients with metastatic castration resistance prostate cancer (mCRPC) undergoing taxanes. We retrospectively assessed the glutamine and glutamate levels in plasma samples by a bioluminescent assay. Pre-treatment glutamine, glutamate, cholesterol and triglycerides levels were correlated with patients’ clinical characteristics, taxanes response and clinical outcomes. Seventy-five patients with mCRPC treated with taxanes were included. The plasma glutamine levels were significantly higher in patients that received abiraterone or enzalutamide prior to taxanes (p = 0.003). Besides, patients with low glutamine levels were more likely to present a PSA response to taxanes (p = 0.048). Higher glutamine levels were significantly correlated with shorter biochemical/clinical progression-free survival (PSA/RX-PFS) (median 2.5 vs. 4.2 months; p = 0.048) and overall survival (OS) (median 12.6 vs. 20.3; p = 0.008). High cholesterol levels independently predicted early PSA/RX-PFS (p = 0.034). High glutamine and cholesterol in the plasma from patients with mCRPC were associated with adverse clinical outcomes, supporting the relevance of further research on metabolism in prostate cancer progression.

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Inhibition of guanosine monophosphate synthetase (GMPS) blocks glutamine metabolism and prostate cancer growth

Cited by 22 publications

References 35 publications

Functional and embedding feature analysis for pan-cancer classification

Functional and embedding feature analysis for pan-cancer classification

Comprehensive Analysis of Histone Modifications in Hepatocellular Carcinoma Reveals Different Subtypes and Key Prognostic Models

Glutamine and Cholesterol Plasma Levels and Clinical Outcomes of Patients with Metastatic Castration-Resistant Prostate Cancer Treated with Taxanes

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