Prognosis Of Glioblastoma Multiforme Research Articles

Speeding up Glioblastoma Cancer Research: Highlighting the Zebrafish Xenograft Model

Glioblastoma multiforme (GBM) is a very aggressive and lethal primary brain cancer in adults. The multifaceted nature of GBM pathogenesis, rising from complex interactions between cells and the tumor microenvironment (TME), has posed great treatment challenges. Despite significant scientific efforts, the prognosis for GBM remains very poor, even after intensive treatment with surgery, radiation, and chemotherapy. Efficient GBM management still requires the invention of innovative treatment strategies. There is a strong necessity to complete cancer in vitro studies and in vivo studies to properly evaluate the mechanisms of tumor progression within the complex TME. In recent years, the animal models used to study GBM tumors have evolved, achieving highly invasive GBM models able to provide key information on the molecular mechanisms of GBM onset. At present, the most commonly used animal models in GBM research are represented by mammalian models, such as mouse and canine ones. However, the latter present several limitations, such as high cost and time-consuming management, making them inappropriate for large-scale anticancer drug evaluation. In recent years, the zebrafish (Danio rerio) model has emerged as a valuable tool for studying GBM. It has shown great promise in preclinical studies due to numerous advantages, such as its small size, its ability to generate a large cohort of genetically identical offspring, and its rapid development, permitting more time- and cost-effective management and high-throughput drug screening when compared to mammalian models. Moreover, due to its transparent nature in early developmental stages and genetic and anatomical similarities with humans, it allows for translatable brain cancer research and related genetic screening and drug discovery. For this reason, the aim of the present review is to highlight the potential of relevant transgenic and xenograft zebrafish models and to compare them to the traditionally used animal models in GBM research.

Abstract 2632: A new paradigm for brain tumors treatment: Guadecitabine as a fundamental player to improve tumor immune response

Abstract Background:Glioblastoma multiforme (GBM) is a highly aggressive primary brain tumor refractory to standard treatments and to immunotherapy with immune-checkpoint inhibitors (ICI) (PMID: 35626018). Interestingly, melanoma brain metastases (MM-BM), sharing the same niche as GBM, often respond to current ICI immunotherapies (PMID: 36521332). The role of epigenetic modifications in regulating GBM cellular proliferation, invasion, and prognosis, as well as their impact on the cross-talk between GBM and immune cells in the tumor milieu, has become evident (PMID: 34361090). Consequently, manipulating the epigenome has emerged as a therapeutic opportunity in GBM. Methods:Microarray transcriptional and methylation profiles, followed by gene set enrichment and IPA analyses, were conducted to study the differences in constitutive expression profiles between GBM and MM-BM cells compared to extracranial MM cells. Additionally, the modulatory effects of the DNA hypomethylating agent (DHA) guadecitabine on different tumor cells were investigated. The prognostic relevance of DHA-modulated genes was assessed through Cox analysis in a TCGA GBM patients' cohort. Results:The most notable differences between constitutive GBM and MM-BM cells lie in the enrichment of biological processes associated with tumor growth, invasion, and extravasation, as well as the inhibition of MHC class II antigen processing/presentation in GBM cells. Guadecitabine treatment appeared to shrink these differences, shifting the phenotype of GBM cells towards the more immunogenic MM-BM profile. This was achieved through promoter hypomethylation and subsequent up-regulation of genes mainly associated with the activation, proliferation, and migration of T and B cells, along with MHC class II antigen processing/presentation. Among DHA-modulated genes in GBM, 7.6% exhibited significant prognostic relevance. Moreover, a substantial set of immune-related upstream regulators (URs) were commonly modulated by DHA in GBM, MM-BM, and/or MM cells: DHA-activated URs enriched biological processes mainly involved in the regulation of cytokines and chemokines production, inflammatory response, and Type I/II/III IFN-mediated signaling. DHA-inhibited URs were associated with metabolic and proliferative pathways. Conclusions:Epigenetic remodeling of GBM by DHA represents a promising strategy to enhance the efficacy of cancer immunotherapy, supporting the rationale for developing new combinatorial approaches for the treatment of brain malignancies. Citation Format: Maria Fortunata Lofiego, Francesca Piazzini, Francesca Pia Caruso, Francesco Marzani, Laura Solmonese, Emma Bello, Fabrizio Celesti, Maria Claudia Costa, Teresa Maria Noviello, Roberta Mortarini, Andrea Anichini, Michele Ceccarelli, Sandra Coral, Anna Maria Di Giacomo, Michele Maio, Alessia Covre. A new paradigm for brain tumors treatment: Guadecitabine as a fundamental player to improve tumor immune response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2632.

Genetic mutation patterns among glioblastoma patients in the Taiwanese population - insights from a single institution retrospective study.

This study utilized Next-Generation Sequencing (NGS) to explore genetic determinants of survival duration in Glioblastoma Multiforme (GBM) patients. We categorized 30 primary GBM patients into two groups based on their survival periods: extended survival (over two years, N = 17) and abbreviated survival (under two years, N = 13). For identifying pathogenic or likely pathogenic variants, we leveraged the ClinVar database. The cohort, aged 23 to 66 (median: 53), included 17 patients in Group A (survival >2 years, 10 males, 7 females), and 13 patients in Group B (survival <2 years, 8 males, 5 females), with a 60% to 40% male-to-female ratio. Identified mutations included CHEK2 (c.1477 G > A, p.E493K), IDH1 (c.395 G > A, p.R132H), and TP53 mutations. Non-coding regions exhibited variants in the TERT promoter (c.-146C > T, c.-124C > T) and TP53 RNA splicing site (c.376-2 A > C, c.376-2 A > G). While Group A had more mutations, statistical significance wasn't reached, likely due to sample size. Notably, TP53, and ATR displayed a trend toward significance. Surprisingly, TP53 mutations were more prevalent in Group A, contradicting Western findings on poorer GBM prognosis. In Taiwanese GBM patients, bevacizumab usage is linked to improved survival rates, affirming its safety and effectiveness. EGFR mutations are infrequent, suggesting potential distinctions in carcinogenic pathways. Further research on EGFR mutations and amplifications is essential for refining therapeutic approaches. TP53 mutations are associated with enhanced survival, but their functional implications necessitate detailed exploration. This study pioneers genetic analysis in Taiwanese GBM patients using NGS, advancing our understanding of their genetic landscape.

Molecular signature of stem-like glioma cells (SLGCs) from human glioblastoma and gliosarcoma.

Glioblastoma multiforme (GBM) and the GBM variant gliosarcoma (GS) are among the tumors with the highest morbidity and mortality, providing only palliation. Stem-like glioma cells (SLGCs) are involved in tumor initiation, progression, therapy resistance, and relapse. The identification of general features of SLGCs could contribute to the development of more efficient therapies. Commercially available protein arrays were used to determine the cell surface signature of eight SLGC lines from GBMs, one SLGC line obtained from a xenotransplanted GBM-derived SLGC line, and three SLGC lines from GSs. By means of non-negative matrix factorization expression metaprofiles were calculated. Using the cophenetic correlation coefficient (CCC) five metaprofiles (MPs) were identified, which are characterized by specific combinations of 7-12 factors. Furthermore, the expression of several factors, that are associated with GBM prognosis, GBM subtypes, SLGC differentiation stages, or neural identity was evaluated. The investigation encompassed 24 distinct SLGC lines, four of which were derived from xenotransplanted SLGCs, and included the SLGC lines characterized by the metaprofiles. It turned out that all SLGC lines expressed the epidermal growth factor EGFR and EGFR ligands, often in the presence of additional receptor tyrosine kinases. Moreover, all SLGC lines displayed a neural signature and the IDH1 wildtype, but differed in their p53 and PTEN status. Pearson Correlation analysis identified a positive association between the pluripotency factor Sox2 and the expression of FABP7, Musashi, CD133, GFAP, but not with MGMT or Hif1α. Spherical growth, however, was positively correlated with high levels of Hif1α, CDK4, PTEN, and PDGFRβ, whereas correlations with stemness factors or MGMT (MGMT expression and promoter methylation) were low or missing. Factors highly expressed by all SLGC lines, irrespective of their degree of stemness and growth behavior, are Cathepsin-D, CD99, EMMPRIN/CD147, Intβ1, the Galectins 3 and 3b, and N-Cadherin.

Clinical Relevance and Interplay between miRNAs in Influencing Glioblastoma Multiforme Prognosis.

Glioblastoma multiforme (GBM) is usually treated with surgery followed by adjuvant partial radiotherapy combined with temozolomide (TMZ) chemotherapy. Recent studies demonstrated a better survival and good response to TMZ in methylguanine-DNA methyltransferase (MGMT)-methylated GBM cases. However, approximately 20% of patients with MGMT-unmethylated GBM display an unexpectedly favorable outcome. Therefore, additional mechanisms related to the TMZ response need to be investigated. As such, we decided to investigate the clinical relevance of six miRNAs involved in brain tumorigenesis (miR-181c, miR-181d, miR-21, miR-195, miR-196b, miR-648) as additional markers of response and survival in patients receiving TMZ for GBM. We evaluated miRNA expression and the interplay between miRNAs in 112 IDH wt GBMs by applying commercial assays. Then, we correlated the miRNA expression with patients' clinical outcomes. Upon bivariate analyses, we found a significant association between the expression levels of the miRNAs analyzed, but, more interestingly, the OS curves show that the combination of low miR-648 and miR-181c or miR-181d expressions is associated with a worse prognosis than cases with other low-expression miRNA pairs. To conclude, we found how specific miRNA pairs can influence survival in GBM cases treated with TMZ.

Prognostic marker CXCL5 in glioblastoma polyformis and its mechanism of immune invasion

Glioblastoma multiforme (GBM) is the most aggressive brain cancer with a poor prognosis. Therefore, the correlative molecular markers and molecular mechanisms should be explored to assess the occurrence and treatment of glioma.WB and qPCR assays were used to detect the expression of CXCL5 in human GBM tissues. The relationship between CXCL5 expression and clinicopathological features was evaluated using logistic regression analysis, Wilcoxon symbolic rank test, and Kruskal–Wallis test. Univariate, multivariate Cox regression and Kaplan–Meier methods were used to assess CXCL5 and other prognostic factors of GBM. Gene set enrichment analysis (GSEA) was used to identify pathways associated with CXCL5. The correlation between CXCL5 and tumor immunoinfiltration was investigated using single sample gene set enrichment analysis (ssGSEA) of TCGA data. Cell experiments and mouse subcutaneous transplanted tumor models were used to evaluate the role of CXCL5 in GBM. WB, qPCR, immunofluorescence, and immunohistochemical assays showed that CXCL5 expression was increased in human GBM tissues. Furthermore, high CXCL5 expression was closely related to poor disease-specific survival and overall survival of GBM patients. The ssGSEA suggested that CXCL5 is closely related to the cell cycle and immune response through PPAR signaling pathway. GSEA also showed that CXCL5 expression was positively correlated with macrophage cell infiltration level and negatively correlated with cytotoxic cell infiltration level. CXCL5 may be associated with the prognosis and immunoinfiltration of GBM.

A pan-cancer analysis of TTC9A expression level and its correlation with prognosis and immune microenvironment

To investigate the expression level of tetratricopeptide repeat protein 9A in tumors and its association with the patients' prognosis and immune infiltration. TTC9A expression in different tumor tissues and its association with prognosis, DNA methylation, tumor mutation burden (TMB), and microsatellite instability (MSI) were analyzed based on data from TCGA and GTEx. TIMER and xCell were used to analyze the relationship between TTC9A expression and immune infiltration. Western blotting and RT-qPCR were used to detect the expression of TTC9A in 4 types of cancer cell lines. TTC9A expressions were significantly increased in many tumors and down-regulated in a few cancer types (P < 0.05). Western blotting and RT-qPCR showed that TTC9A expressions were elevated in lung, colon and liver cancer cells but decreased in bladder cancer cells. In head and neck squamous cell carcinoma, renal clear cell carcinoma, renal papillary cell carcinoma, low-grade glioma, malignant mesothelioma, and endometrial carcinoma tumors, a high expression of TTC9A was strongly correlated with better overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI) (P < 0.05), but was correlated with worse OS, DSS, and PFI in lung adenocarcinoma, pancreatic adenocarcinoma, adrenal carcinoma, and rectal adenocarcinoma (P < 0.05). TTC9A hypermethylation was associated with a more favorable prognosis of glioblastoma multiforme, low- grade glioma, uveal melanoma, and ovarian plasmacytoid cystadenocarcinoma (P < 0.05) but with poor prognosis of squamous cell carcinoma of the uterine cervix and intracervical adenocarcinoma, squamous cell carcinoma of head and neck, squamous cell carcinoma of the lungs, adrenal carcinoma, and endometrial carcinoma (P < 0.05). In most of the cancer types, TTC9A was significantly correlated with the level of immune cell infiltration (P < 0.05). TTC9A can be used as a prognostic marker for a variety of cancers and is strongly associated with TBM, MSI and immune cell infiltration.

5-ALA localises to the autophagy compartment and increases its fluorescence upon autophagy enhancement through caloric restriction and spermidine treatment in human glioblastoma

Glioblastoma Multiforme (GBM) is the most invasive and prevalent Central Nervous System (CNS) malignancy. It is characterised by diffuse infiltrative growth and metabolic dysregulation that impairs the extent of surgical resection (EoR), contributing to its poor prognosis. 5-Aminolevulinic acid (5-ALA) fluorescence-guided surgical resection (FGR) takes advantage of the preferential generation of 5-ALA-derived fluorescence signal in glioma cells, thereby improving visualisation and enhancing the EoR. However, despite 5-ALA FGR is a widely used technique in the surgical management of malignant gliomas, the infiltrative tumour margins usually show only vague or no visible fluorescence and thus a significant amount of residual tumour tissue may hence remain in the resection cavity, subsequently driving tumour recurrence. To investigate the molecular mechanisms that govern the preferential accumulation of 5-ALA in glioma cells, we investigated the precise subcellular localisation of 5-ALA signal using Correlative Light and Electron Microscopy (CLEM) and colocalisation analyses in U118MG glioma cells. Our results revealed strong 5-ALA signal localisation in the autophagy compartment – specifically autolysosomes and lysosomes. Flow cytometry was employed to investigate whether autophagy enhancement through spermidine treatment (SPD) or nutrient deprivation/caloric restriction (CR) would enhance 5-ALA fluorescence signal generation. Indeed, SPD, CR and a combination of SPD/CR treatment significantly increased 5-ALA signal intensity, with a most robust increase in signal intensity observed in the combination treatment of SPD/CR. When using 3-D glioma spheroids to assess the effect of 5-ALA on cellular ultrastructure, we demonstrate that 5-ALA exposure leads to cytoplasmic disruption, vacuolarisation and large-scale mitophagy induction. These findings not only suggest a critical role for the autophagy compartment in 5-ALA engagement and signal generation but also point towards a novel and practically feasible approach to enhance 5-ALA fluorescence signal intensity. The findings may highlight that indeed autophagy control may serve as a promising avenue to promote an improved resection and GBM prognosis.

Systematic analysis based on the cuproptosis-related genes identifies ferredoxin 1 as an immune regulator and therapeutic target for glioblastoma

Glioblastoma multiforme (GBM) is recognized as the prevailing malignant and aggressive primary brain tumor, characterized by an exceedingly unfavorable prognosis. Cuproptosis, a recently identified form of programmed cell death, exhibits a strong association with cancer progression, therapeutic response, and prognostic outcomes. However, the specific impact of cuproptosis on GBM remains uncertain. To address this knowledge gap, we obtained transcriptional and clinical data pertaining to GBM tissues and their corresponding normal samples from various datasets, including TCGA, CGGA, GEO, and GTEx. R software was utilized for the analysis of various statistical techniques, including survival analysis, cluster analysis, Cox regression, Lasso regression, gene enrichment analysis, drug sensitivity analysis, and immune microenvironment analysis. Multiple assays were conducted to investigate the expression of genes related to cuproptosis and their impact on the proliferation, invasion, and migration of glioblastoma multiforme (GBM) cells. The datasets were obtained and prognostic risk score models were constructed and validated using differentially expressed genes (DEGs) associated with cuproptosis. To enhance the practicality of these models, a nomogram was developed.Patients with glioblastoma multiforme (GBM) who were classified as high risk exhibited a more unfavorable prognosis and shorter overall survival compared to those in the low risk group. Additionally, we specifically chose FDX1 from the differentially expressed genes (DEGs) within the high risk group to assess its expression, prognostic value, biological functionality, drug responsiveness, and immune cell infiltration. The findings demonstrated that FDX1 was significantly upregulated and associated with a poorer prognosis in GBM. Furthermore, its elevated expression appeared to be linked to various metabolic processes and the susceptibility to chemotherapy drugs. Moreover, FDX1 was found to be involved in immune cell infiltration and exhibited correlations with multiple immunosuppressive genes, including TGFBR1 and PDCD1LG2. The aforementioned studies offer substantial assistance in informing the chemotherapy and immunotherapy approaches for GBM. In summary, these findings contribute to a deeper comprehension of cuproptosis and offer novel perspectives on the involvement of cuproptosis-related genes in GBM, thereby presenting a promising therapeutic strategy for GBM patients.

Comparison of MRI radiomics-based machine learning survival models in predicting prognosis of glioblastoma multiforme

ObjectiveTo compare the performance of radiomics-based machine learning survival models in predicting the prognosis of glioblastoma multiforme (GBM) patients.Methods131 GBM patients were included in our study. The traditional Cox proportional-hazards (CoxPH) model and four machine learning models (SurvivalTree, Random survival forest (RSF), DeepSurv, DeepHit) were constructed, and the performance of the five models was evaluated using the C-index.ResultsAfter the screening, 1792 radiomics features were obtained. Seven radiomics features with the strongest relationship with prognosis were obtained following the application of the least absolute shrinkage and selection operator (LASSO) regression. The CoxPH model demonstrated that age (HR = 1.576, p = 0.037), Karnofsky performance status (KPS) score (HR = 1.890, p = 0.006), radiomics risk score (HR = 3.497, p = 0.001), and radiomics risk level (HR = 1.572, p = 0.043) were associated with poorer prognosis. The DeepSurv model performed the best among the five models, obtaining C-index of 0.882 and 0.732 for the training and test set, respectively. The performances of the other four models were lower: CoxPH (0.663 training set / 0.635 test set), SurvivalTree (0.702/0.655), RSF (0.735/0.667), DeepHit (0.608/0.560).ConclusionThis study confirmed the superior performance of deep learning algorithms based on radiomics relative to the traditional method in predicting the overall survival of GBM patients; specifically, the DeepSurv model showed the best predictive ability.

The genes significantly associated with an improved prognosis and long-term survival of glioblastoma.

Glioblastoma multiforme (GBM) is the most devastating brain tumor with less than 5% of patients surviving 5 years following diagnosis. Many studies have focused on the genetics of GBM with the aim of improving the prognosis of GBM patients. We investigated specific genes whose expressions are significantly related to both the length of the overall survival and the progression-free survival in patients with GBM. We obtained data for 12,042 gene mRNA expressions in 525 GBM tissues from the Cancer Genome Atlas (TCGA) database. Among those genes, we identified independent genes significantly associated with the prognosis of GBM. Receiver operating characteristic (ROC) curve analysis was performed to determine the genes significant for predicting the long-term survival of patients with GBM. Bioinformatics analysis was also performed for the significant genes. We identified 33 independent genes whose expressions were significantly associated with the prognosis of 525 patients with GBM. Among them, the expressions of five genes were independently associated with an improved prognosis of GBM, and the expressions of 28 genes were independently related to a poorer prognosis of GBM. The expressions of the ADAM22, ATP5C1, RAC3, SHANK1, AEBP1, C1RL, CHL1, CHST2, EFEMP2, and PGCP genes were either positively or negatively related to the long-term survival of GBM patients. Using a large-scale and open database, we found genes significantly associated with both the prognosis and long-term survival of patients with GBM. We believe that our findings may contribute to improving the understanding of the mechanisms underlying GBM.

Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model.

Glioblastoma multiforme (GBM) patients show a variety of signs and symptoms that affect their quality of life (QOL) and self-dependence. Since most existing studies have examined prognostic factors based only on clinical factors, there is a need to consider the value of integrating multi-omics data including gene expression and proteomics with clinical data in identifying significant biomarkers for GBM prognosis. Our research aimed to isolate significant features that differentiate between short-term (≤ 6 months) and long-term (≥ 2 years) GBM survival, and between high Karnofsky performance scores (KPS ≥ 80) and low (KPS ≤ 60), using the iterative random forest (iRF) algorithm. Using the Cancer Genomic Atlas (TCGA) database, we identified 35 molecular features composed of 19 genes and 16 proteins. Our findings propose molecular signatures for predicting GBM prognosis and will improve clinical decisions, GBM management, and drug development.

TMET-11. BERBERINE AS A POTENTIAL ENHANCER FOR 5-ALA-MEDIATED FLUORESCENCE IN GLIOBLASTOMA: INCREASING DETECTABILITY OF INFILTRATING GLIOMA STEM CELLS TO OPTIMIZE 5-ALA-GUIDED SURGERY

Abstract OBJECTIVE The prognosis of glioblastoma multiforme correlates with residual tumor volume. In fluorescence-guided surgery, 5-aminolevulinic acid (5-ALA) has been used to maximize safe resection. We searched for repositioning drugs that affect mitochondrial functions and energy metabolism, identifying berberine (BBR) as a potential enhancer of 5-ALA-mediated protoporphyrin IX (PpIX) fluorescence (5-ALA-fluorescence). Here, we investigated whether BBR can be applied to clinical practice as a 5-ALA-fluorescence enhancer. METHODS Effects of BBR on 5-ALA-fluorescence in glioma cells and GSCs were evaluated by flow cytometry (fluorescence-activated cell sorting; FACS) analysis. As 5-ALA is metabolized for heme synthesis, effects of BBR on mRNA expressions of seven enzymes in the heme-synthesis pathway were analyzed. Enzymes showing significantly higher expression than control in all cells were identified and protein analysis was performed. To examine clinical availability, the detectability and cytotoxicity of BBR in tumor-transplanted mice were analyzed. RESULTS Fluorescence microscopy revealed much more intense 5-ALA-fluorescence in both GSCs and non-stem cells with 5-ALA and BBR than with 5-ALA alone. FACS disclosed that BBR greatly enhanced 5-ALA-fluorescence compared to 5-ALA alone and enhancement was much higher for GSCs than for glioma cells. Among the seven enzymes examined, BBR upregulated mRNA expressions of ALA synthetase 1 (ALAS1) more highly in all cells, and activated ALAS1 through deregulating ALAS1 activity inhibited by the negative feedback of heme. In vivo study showed that 5-ALA-fluorescence with 5-ALA and BBR was significantly stronger than with 5-ALA alone, and the sensitivity and specificity of BBR-enhanced fluorescence were both 100%. In addition, BBR did not show any cytotoxicity for normal brain tissue. CONCLUSIONS BBR enhanced 5-ALA-mediated PpIX fluorescence by upregulating and activating ALAS1 through deregulation of negative feedback inhibition by heme. BBR is a clinically used drug with no side effects. BBR is expected to augment fluorescence-guided surgery and photodynamic therapy.

STEM-18. HYPOXIA-REGULATED EXPRESSION OF CD44 AND OSTEOPONTIN CAN CHANGE THE PHENOTYPE OF GLIOMA STEM-LIKE CELLS FROM HIGHLY INVASIVE TO LESS INVASIVE/PROLIFERATIVE TUMORS IN GLIOBLASTOMA

Abstract BACKGROUND The poor prognosis of glioblastoma multiforme (GBM) is primarily due to highly invasive and highly migratory glioma stem-like cells(GSCs) in tumors. Upon GBM recurrence or progression, the highly invasive phenotype of GSCs changes to a less-motile, proliferative phenotype, thus generating tumor mass. Elucidating the molecular mechanism underlying this phenotypic transition could lead to the identification of effective molecular targets for treating GBM. METHODS We examined the effects of hypoxia (1% O2: severe or 5% O2: moderate) on the expression and functions of hypoxia-inducible factor(HIF), CD44 and osteopontin (OPN) to clarify the role of these molecules in GBM tumor invasion and proliferation. We also investigated the anti-tumor effects of CD44 knockdown in vitro and in vivo using a GSC-transplantation mouse model. RESULTS Severe Hypoxia upregulates CD44 expression via activation of HIF-1α, inducing GSCs to assume a highly invasive phenotype. In contrast, moderate hypoxia upregulates OPN expression via activation of HIF-2α. OPN inhibits CD44-promoted GSC migration and invasion and stimulates GSC proliferation, resulting in GSCs assuming a less-invasive, highly proliferative phenotype. CD44 knockdown significantly inhibited GSC migration and invasion both in vitro and in vivo, and the mice survived significantly longer than control mice. CONCLUSION The highly invasive phenotype of GSCs can be reversed by switching from severe to moderate hypoxia, leading to less-invasive and proliferative tumors. CD44 and OPN play a central role in regulating GSC invasion and proliferation by inducing a phenotypic transition, suggesting that these molecules could be effective targets for treating GBM.

Advances in Glioblastoma Therapy: An Update on Current Approaches.

Glioblastoma multiforme (GBM) is a primary malignant brain tumor characterized by a high grade of malignancy and an extremely unfavorable prognosis. The current efficacy of established treatments for GBM is insufficient, necessitating the prompt development of novel therapeutic approaches. The progress made in the fundamental scientific understanding of GBM is swiftly translated into more advanced stages of therapeutic studies. Despite extensive efforts to identify new therapeutic approaches, GBM exhibits a high mortality rate. The current efficacy of treatments for GBM patients is insufficient due to factors such as tumor heterogeneity, the blood-brain barrier, glioma stem cells, drug efflux pumps, and DNA damage repair mechanisms. Considering this, pharmacological cocktail therapy has demonstrated a growing efficacy in addressing these challenges. Towards this, various forms of immunotherapy, including the immune checkpoint blockade, chimeric antigen receptor T (CAR T) cell therapy, oncolytic virotherapy, and vaccine therapy have emerged as potential strategies for enhancing the prognosis of GBM. Current investigations are focused on exploring combination therapies to mitigate undesirable side effects and enhance immune responses against tumors. Furthermore, clinical trials are underway to evaluate the efficacy of several strategies to circumvent the blood-brain barrier (BBB) to achieve targeted delivery in patients suffering from recurrent GBM. In this review, we have described the biological and molecular targets for GBM therapy, pharmacologic therapy status, prominent resistance mechanisms, and new treatment approaches. We also discuss these promising therapeutic approaches to assess prospective innovative therapeutic agents and evaluated the present state of preclinical and clinical studies in GBM treatment. Overall, this review attempts to provide comprehensive information on the current status of GBM therapy.

An Overview of CD133 as a Functional Unit of Prognosis and Treatment Resistance in Glioblastoma.

Biomarkers for resistance in Glioblastoma multiforme (GBM) are lacking, and progress in the clinic has been slow to arrive. CD133 (prominin-1) is a membrane-bound glycoprotein on the surface of cancer stem cells (CSCs) that has been associated with poor prognosis, therapy resistance, and tumor recurrence in GBM. Due to its connection to CSCs, to which tumor resistance and recurrence have been partially attributed in GBM, there is a growing field of research revolving around the potential role of CD133 in each of these processes. However, despite encouraging results in vitro and in vivo, the biological interplay of CD133 with these components is still unclear, causing a lack of clinical application. In parallel, omic data from biospecimens that include CD133 are beginning to emerge, increasing the importance of understanding CD133 for the effective use of these highly dimensional data sets. Given the significant mechanistic overlap, prioritization of the most robust findings is necessary to optimize the transition of CD133 to clinical applications using patient-derived biospecimens. As a result, this review aims to compile and analyze the current research regarding CD133 as a functional unit in GBM, exploring its connections to prognosis, the tumor microenvironment, tumor resistance, and tumor recurrence.

Targeting EZH2 regulates the biological characteristics of glioma stem cells via the Notch1 pathway.

Glioma is the most common malignant brain tumor, and its behavior is closely related to the presence of glioma stem cells (GSCs). We found that the enhancer of zeste homolog 2 (EZH2) is highly expressed in glioma and that its expression is correlated with the prognosis of glioblastoma multiforme (GBM) in two databases: The Cancer Genome Atlas and the Chinese Glioma Genome Atlas. Additionally, EZH2 is known to regulate the stemness-associated gene expression, proliferation, and invasion ability of GSCs, which may be achieved through the activation of the STAT3 and Notch1 pathways. Furthermore, we demonstrated the effect of the EZH2-specific inhibitor GSK126 on GSCs; these results not only corroborate our hypothesis, but also provide a potential novel treatment approach for glioma.

Identification of T-cell exhaustion-related gene signature for predicting prognosis in glioblastoma multiforme.

Glioblastoma multiforme (GBM) is a highly malignant primary brain tumour with a poor prognosis in adults. Identifying biomarkers that can aid in the molecular classification and risk stratification of GBM is critical. Here, we conducted a transcriptional profiling analysis of T-cell immunity in the tumour microenvironment of GBM patients and identified two novel T cell exhaustion (TEX)-related GBM subtypes (termed TEX-C1 and TEX-C2) using the consensus clustering. Our multi-omics analysis revealed distinct immunological, molecular and clinical characteristics for these two subtypes. Specifically, the TEX-C1 subtype had higher infiltration levels of immune cells and expressed higher levels of immune checkpoint molecules than the TEX-C2 subtype. Functional analysis revealed that upregulated genes in the TEX-C1 subtype were significantly enriched in immune response and signal transduction pathways, and upregulated genes in the TEX-C2 subtype were predominantly associated with cell fate and nervous system development pathways. Notably, patients with activated T-cell activity status in the TEX-C1 subgroup demonstrated a significantly worse prognosis than those with severe T cell exhaustion status in the TEX-C2 subgroup. Finally, we proposed a machine-learning-derived novel gene signature comprising 12 TEX-related genes (12TexSig) to indicate tumour subtyping. In the TCGA cohort, the 12TexSig demonstrated the ability to accurately predict the prognosis of GBM patients, and this prognostic value was further confirmed in two independent external cohorts. Taken together, our results suggest that the TEX-derived subtyping and gene signature has the potential to serve as a clinically helpful biomarker for guiding the management of GBM patients, pending further prospective validation.

Identifying the personalized driver gene sets maximally contributing to abnormality of transcriptome phenotype in glioblastoma multiforme individuals.

High heterogeneity in genome and phenotype of cancer populations made it difficult to apply population-based common driver genes to the diagnosis and treatment of cancer individuals. Characterizing and identifying the personalized driver mechanism for glioblastoma multiforme (GBM) individuals were pivotal for the realization of precision medicine. We proposed an integrative method to identify the personalized driver gene sets by integrating the profiles of gene expression and genetic alterations in cancer individuals. This method coupled genetic algorithm and random walk to identify the optimal gene sets that could explain abnormality of transcriptome phenotype to the maximum extent. The personalized driver gene sets were identified for 99 GBM individuals using our method. We found that genomic alterations in between one and seven driver genes could maximally and cumulatively explain the dysfunction of cancer hallmarks across GBM individuals. The driver gene sets were distinct even in GBM individuals with significantly similar transcriptomic phenotypes. Our method identified MCM4 with rare genetic alterations as previously unknown oncogenic genes, the high expression of which were significantly associated with poor GBM prognosis. The functional experiments confirmed that knockdown of could significantly inhibit proliferation, invasion, migration and clone formation of the GBM cell lines U251 and U118MG, and overexpression of MCM4 significantly promoted the proliferation, invasion, migration and clone formation of the GBM cell line U87MG. Our method could dissect the personalized driver genetic alteration sets that are pivotal for developing targeted therapy strategies and precision medicine. Our method could be extended to identify key drivers from other levels, and could be applied to more cancer types.

Prognostic value of preoperative hematological markers in patients with glioblastoma multiforme and construction of random survival forest model

ObjectiveIn recent years, an increasing number of studies have revealed that patients’ preoperative inflammatory response, coagulation function, and nutritional status are all linked to the occurrence, development, angiogenesis, and metastasis of various malignant tumors. The goal of this study is to determine the relationship between preoperative peripheral blood neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR), systemic immune-inflammatory index (SII), platelet to lymphocyte ratio (PLR), and platelet to fibrinogen ratio (FPR). Prognostic nutritional index (PNI) and the prognosis of glioblastoma multiforme (GBM) patients, as well as establish a forest prediction model that includes preoperative hematological markers to predict the individual GBM patient’s 3-year survival status after treatment.MethodsThe clinical and hematological data of 281 GBM patients were analyzed retrospectively; overall survival (OS) was the primary endpoint. X-Tile software was used to determine the best cut-off values for NLR, SII, and PLR, and the survival analysis was carried out by the Kaplan–Meier method as well as univariate and multivariate COX regression. Afterward, we created a random forest model that predicts the individual GBM patient’s 3-year survival status after treatment, and the area under the curve (AUC) is used to validate the model’s effectiveness.ResultsThe best cut-off values for NLR, SII, and PLR in GBM patients’ preoperative peripheral blood were 2.12, 537.50, and 93.5 respectively. The Kaplan–Meier method revealed that preoperative GBM patients with high SII, high NLR, and high PLR had shorter overall survival, and the difference was statistically significant. In addition to clinical and pathological factors. Univariate Cox showed NLR (HR = 1.456, 95% CI: 1.286 ~ 1.649, P < 0.001) MLR (HR = 1.272, 95% CI: 1.120 ~ 1.649, P < 0.001), FPR (HR = 1.183,95% CI: 1.049 ~ 1.333, P < 0.001), SII (HR = 0.218,95% CI: 1.645 ~ 2.127, P < 0.001) is related to the prognosis and overall survival of GBM. Multivariate Cox proportional hazard regression showed that SII (HR = 1.641, 95% CI: 1.430 ~ 1.884, P < 0.001) is also related to the overall survival of patients with GBM. In the random forest prognostic model with preoperative hematologic markers, the AUC in the test set and the validation set was 0.907 and 0.900, respectively.ConclusionHigh levels of NLR, MLR, PLR, FPR, and SII before surgery are prognostic risk factors for GBM patients. A high preoperative SII level is an independent risk factor for GBM prognosis. The random forest model that includes preoperative hematological markers has the potential to predict the individual GBM patient’s 3-year survival status after treatment,and assist the clinicians for making a good clinical decision.