Glioblastoma multiforme (GBM) is the most common type of malignant brain tumor. The present standard treatment for GBM has not been effective; therefore, the prognosis remains dramatically poor and prolonged survival after treatment is still limited. The new therapeutic strategies are urgently needed to improve the treatment efficiency. Doxorubicin (Dox) has been widely used in the treatment of many cancers for decades. In recent years, with the advancement of delivery technology, more and more research indicates that Dox has the opportunity to be used in the treatment of GBM. Amphiregulin (AREG), a ligand of the epidermal growth factor receptor (EGFR), has been reported to have oncogenic effects in many cancer cell types and is implicated in drug resistance. However, the biological function and molecular mechanism of AREG in Dox treatment of GBM are still unclear. Here, we demonstrate that knockdown of AREG can boost Dox-induced endoplasmic reticulum (ER) stress to trigger activation in both autophagy and apoptosis in GBM cells, ultimately leading to cell death. To explore the importance of AREG in the clinic, we used available bioinformatics tools and found AREG is highly expressed in GBM tumor tissues that are associated with poor survival. In addition, we also used antibody array analysis to dissect pathways that are likely to be activated by AREG. Taken together, our results revealed AREG can serve as a potential therapeutic target and a promising biomarker in GBM.
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http://dx.doi.org/10.1007/s12031-020-01598-5 | DOI Listing |
The rapid growth, invasiveness, and resistance to treatment of glioblastoma multiforme (GBM) underscore the urgent need for improved diagnostics and therapies. Current surgical practice is limited by challenges with intraoperative imaging, while recurrence monitoring requires expensive magnetic resonance or nuclear imaging scans. Here we introduce 'acoustic tumor paint', an approach to labeling brain tumors for ultrasound imaging, a widely accessible imaging modality.
View Article and Find Full Text PDFGlioblastoma tumors remain a formidable challenge for immune-based treatments because of their molecular heterogeneity, poor immunogenicity, and growth in the largely isolated and immunosuppressive neural environment. As the tumor grows, GBM cells change the composition and architecture of the neural extracellular matrix (ECM), affecting the mobility, survival, and function of immune cells such as tumor-associated microglia and infiltrated macrophages (TAMs). We have previously described the unique expression of the ECM protein EFEMP1/fibulin-3 in GBM compared to normal brain and demonstrated that this secreted protein promotes the growth of the GBM stem cell (GSC) population.
View Article and Find Full Text PDFBackground: Bispecific T cell-engagers (BTEs) are engineered antibodies that redirect T cells to target antigen-expressing tumors. BTEs targeting various tumor-specific antigens, like interleukin 13 receptor alpha 2 (IL13RA2) and EGFRvIII, have been developed for glioblastoma (GBM). However, limited knowledge of BTE actions derived from studies conducted in immunocompromised animal models impedes progress in the field.
View Article and Find Full Text PDFFront Immunol
December 2024
Aix-Marseille Univ, CNRS, INP, Inst Neurophysiopathol, GlioME Team, Marseille, France.
In recent decades, immunometabolism in cancers has emerged as an interesting target for treatment development. Indeed, the tumor microenvironment (TME) unique characteristics such as hypoxia and limitation of nutrients availability lead to a switch in metabolic pathways in both tumor and TME cells in order to support their adaptation and grow. Glioblastoma (GBM), the most frequent and aggressive primary brain tumor in adults, has been extensively studied in multiple aspects regarding its immune population, but research focused on immunometabolism remains limited.
View Article and Find Full Text PDFBioinform Biol Insights
January 2025
Department of Pathology & Clinical Bioinformatics, Erasmus MC Cancer Institute, University Medical Center Rotterdam, Rotterdam, The Netherlands.
While deep learning (DL) is used in patients' outcome predictions, the insufficiency of patient samples limits the accuracy. In this study, we investigated how transfer learning (TL) alleviates the small sample size problem. A 2-step TL framework was constructed for a difficult task: predicting the response of the drug temozolomide (TMZ) in glioblastoma (GBM) cell cultures.
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