Modulating autophagy as a therapeutic strategy for the treatment of paediatric high‐grade glioma

Howarth, Alison; Madureira, Patrícia A.; Lockwood, G.; Storer, Lisa; Grundy, Richard G.; Rahman, Ruman; Pilkington, Geoffrey J.; Hill, Richard

doi:10.1111/bpa.12729

Cited by 11 publications

(13 citation statements)

References 155 publications

(194 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Then, ULK1 and ULK2 are activated and phosphorylate Atg13 and FIP200. The beclin1 complex is recruited and activates the class III PI3K VPS34, stimulating the autophagosome nucleation [42,43]. In addition, other studies demonstrate that inhibition of mTORC1 promotes the transcription factors EB (TFEB) and E3 (TFE3) nuclear translocation, inducing expression of autophagy-lysosome-relevant genes [44,45].…”

Section: Regulationmentioning

confidence: 99%

Autophagy in the Immunosuppressive Perivascular Microenvironment of Glioblastoma

Molina

García‐Bernal

Valdor

2019

Cancers

View full text Add to dashboard Cite

Glioblastoma (GB) has been shown to up-regulate autophagy with anti- or pro-oncogenic effects. Recently, our group has shown how GB cells aberrantly up-regulate chaperone-mediated autophagy (CMA) in pericytes of peritumoral areas to modulate their immune function through cell-cell interaction and in the tumor’s own benefit. Thus, to understand GB progression, the effect that GB cells could have on autophagy of immune cells that surround the tumor needs to be deeply explored. In this review, we summarize all the latest evidence of several molecular and cellular immunosuppressive mechanisms in the perivascular tumor microenvironment. This immunosuppression has been reported to facilitate GB progression and may be differently modulated by several types of autophagy as a critical point to be considered for therapeutic interventions.

show abstract

Section: Regulationmentioning

confidence: 99%

Autophagy in the Immunosuppressive Perivascular Microenvironment of Glioblastoma

Molina

García‐Bernal

Valdor

2019

Cancers

View full text Add to dashboard Cite

show abstract

“…Previous studies have investigated the roles of certain autophagy-related genes (ATGs) in the development and progression of glioma. These ATGs may be regulated by-and, in turn, regulate-multiple signaling pathways, many of which are dysregulated in GBM and targetable with various inhibitors [12,13]. Therefore, ATGs are promising therapeutic targets and prognostic predictors in GBM.…”

Section: Introductionmentioning

confidence: 99%

Development and validation of a nomogram with an autophagy-related gene signature for predicting survival in patients with glioblastoma

Wang

Gao

Guo

et al. 2019

Aging

View full text Add to dashboard Cite

Glioblastoma (GBM) is the most common brain tumor with significant morbidity and mortality. Autophagy plays a vital role in GBM development and progression. We aimed to establish an autophagy-related multigene expression signature for individualized prognosis prediction in patients with GBM. Differentially expressed autophagy-related genes (DE-ATGs) in GBM and normal samples were screened using TCGA. Univariate and multivariate Cox regression analyses were performed on DE-ATGs to identify the optimal prognosis-related genes. Consequently, NRG1 (HR=1.142, P=0.008), ITGA3 (HR=1.149, P=0.043), and MAP1LC3A (HR=1.308, P=0.014) were selected to establish the prognostic risk score model and validated in the CGGA validation cohort. GSEA revealed that these genes were mainly enriched in cancer-and autophagy-related KEGG pathways. Kaplan-Meier survival analysis demonstrated that patients with high risk scores had significantly poorer overall survival (OS, log-rank P= 6.955×10-5). The autophagy signature was identified as an independent prognostic factor. Finally, a prognostic nomogram including the autophagy signature, age, pharmacotherapy, radiotherapy, and IDH mutation status was constructed, and TCGA/CGGA-based calibration plots indicated its excellent predictive performance. The autophagy-related three-gene risk score model could be a prognostic biomarker and suggest therapeutic targets for GBM. The prognostic nomogram could assist individualized survival prediction and improve treatment strategies.

show abstract

“…Reaserches have further elucidated the potential mechanism of conventional antidepressants therapy for GBM. By targeting the respiratory chain complex III and changing membrane potential, the tricyclics antidepressants mentioned above induce mitochondria-mediated apoptosis of malignant glioma cells, activate the intrinsic pathway of cytochrome-C release and caspase-3 dependent apoptosis process, and finally results in glioma cell death (106)(107)(108)(109)(110)(111)(112). It is worth mentioning that previous studies have also indicated that the anticoagulant ticlopidine synergized with imipramine to induce the death of human glioma cell lines and primary mouse glioma cells (98,99).…”

Section: Discussionmentioning

confidence: 99%

Identification of a Metabolism-Related Risk Signature Associated With Clinical Prognosis in Glioblastoma Using Integrated Bioinformatic Analysis

Wang

Xue

et al. 2020

Front. Oncol.

View full text Add to dashboard Cite

Altered metabolism of glucose, lipid and glutamine is a prominent hallmark of cancer cells. Currently, cell heterogeneity is believed to be the main cause of poor prognosis of glioblastoma (GBM) and is closely related to relapse caused by therapy resistance. However, the comprehensive model of genes related to glucose-, lipid-and glutamine-metabolism associated with the prognosis of GBM remains unclear, and the metabolic heterogeneity of GBM still needs to be further explored. Based on the expression profiles of 1,395 metabolism-related genes in three datasets of TCGA/CGGA/GSE, consistent cluster analysis revealed that GBM had three different metabolic status and prognostic clusters. Combining univariate Cox regression analysis and LASSO-penalized Cox regression machine learning methods, we identified a 17-metabolism-related genes risk signature associated with GBM prognosis. Kaplan-Meier analysis found that obtained signature could differentiate the prognosis of high-and low-risk patients in three datasets. Moreover, the multivariate Cox regression analysis and receiver operating characteristic curves indicated that the signature was an independent prognostic factor for GBM and had a strong predictive power. The above results were further validated in the CGGA and GSE13041 datasets, and consistent results were obtained. Gene set enrichment analysis (GSEA) suggested glycolysis gluconeogenesis and oxidative phosphorylation were significantly enriched in high-and low-risk GBM. Lastly Connectivity Map screened 54 potential compounds specific to different subgroups of GBM patients. Our study identified a novel metabolism-related gene signature, in addition the existence of three different metabolic status and two opposite biological processes in GBM were recognized, which revealed the metabolic heterogeneity of GBM. Robust metabolic subtypes and powerful risk prognostic models contributed a new perspective to the metabolic exploration of GBM.

show abstract

Modulating autophagy as a therapeutic strategy for the treatment of paediatric high‐grade glioma

Cited by 11 publications

References 155 publications

Autophagy in the Immunosuppressive Perivascular Microenvironment of Glioblastoma

Autophagy in the Immunosuppressive Perivascular Microenvironment of Glioblastoma

Development and validation of a nomogram with an autophagy-related gene signature for predicting survival in patients with glioblastoma

Identification of a Metabolism-Related Risk Signature Associated With Clinical Prognosis in Glioblastoma Using Integrated Bioinformatic Analysis

Contact Info

Product

Resources

About