Glioblastoma Multiforme Patients Research Articles

Impact of Extended Adjuvant Temozolamide Beyond 6 Months in the Management of Glioblastoma Patients.

Our study aimed to assess the benefit of prolonging adjuvant temozolomide (TMZ) therapy beyond 6 cycles in glioblastoma multiform patients. The medical records of 329 patients in 2 cancer centers in Egypt were reviewed from January 2008 to December 2018 who were diagnosed with diffuse gliomas. Data were collected on patient demographics, presenting complaints, tumor size, treatment modalities (extent of surgery, radiotherapy dose and technique, concomitant TMZ, and the number of adjuvant TMZ cycles), and reported adverse events. In the studied cohort, 105 patients were treated with adjuvant TMZ, 33 patients received <6 cycles (TMZL), 41 patients received the standard 6 cycles (TMZS), and 31 patients received >6 cycles (TMZE). Our results showed the median overall survival in the TMZL arm was 8.47 months compared with 15.83 months in the TMZS arm and 27.33 months in the TMZE arm ( P < 0.001). Furthermore, a median progression-free survival of 6.35 months was reported in the TMZL group versus, 12.7 and 22.90 months in (TMZS) and (TMZE) groups, respectively( P < 0.001). In the multivariate analysis, the extended adjuvant TMZ with a hazard ratio of 3.106 (95% CI: 2.43-14.46; P < 0.001) was statistically significantly associated with a better outcome. Extended adjuvant TMZ therapy beyond 6 cycles may significantly improve the progression-free survival and overall survival in patients with glioblastoma multiform.

Proteomic analysis predicts anti-angiogenic resistance in recurred glioblastoma

BackgroundRecurrence is common in glioblastoma multiforme (GBM) because of the infiltrative, residual cells in the tumor margin. Standard therapy for GBM consists of surgical resection followed by chemotherapy and radiotherapy, but the median survival of GBM patients remains poor (~ 1.5 years). For recurrent GBM, anti-angiogenic treatment is one of the common treatment approaches. However, current anti-angiogenic treatment modalities are not satisfactory because of the resistance to anti-angiogenic agents in some patients. Therefore, we sought to identify novel prognostic biomarkers that can predict the therapeutic response to anti-angiogenic agents in patients with recurrent glioblastoma.MethodsWe selected patients with recurrent GBM who were treated with anti-angiogenic agents and classified them into responders and non-responders to anti-angiogenic therapy. Then, we performed proteomic analysis using liquid-chromatography mass spectrometry (LC–MS) with formalin-fixed paraffin-embedded (FFPE) tissues obtained from surgical specimens. We conducted a gene-ontology (GO) analysis based on protein abundance in the responder and non-responder groups. Based on the LC–MS and GO analysis results, we identified potential predictive biomarkers for anti-angiogenic therapy and validated them in recurrent glioblastoma patients.ResultsIn the mass spectrometry-based approach, 4957 unique proteins were quantified with high confidence across clinical parameters. Unsupervised clustering analysis highlighted distinct proteomic patterns (n = 269 proteins) between responders and non-responders. The GO term enrichment analysis revealed a cluster of genes related to immune cell-related pathways (e.g., TMEM173, FADD, CD99) in the responder group, whereas the non-responder group had a high expression of genes related to nuclear replisome (POLD) and damaged DNA binding (ERCC2). Immunohistochemistry of these biomarkers showed that the expression levels of TMEM173 and FADD were significantly associated with the overall survival and progression-free survival of patients with recurrent GBM.ConclusionsThe candidate biomarkers identified in our protein analysis may be useful for predicting the clinical response to anti-angiogenic agents in patients with recurred GBM.

Diagnosis of Glioblastoma Multiforme Progression via Interpretable Structure-Constrained Graph Neural Networks.

Glioblastoma multiforme (GBM) is the most common type of brain tumors with high recurrence and mortality rates. After chemotherapy treatment, GBM patients still show a high rate of differentiating pseudoprogression (PsP), which is often confused as true tumor progression (TTP) due to high phenotypical similarities. Thus, it is crucial to construct an automated diagnosis model for differentiating between these two types of glioma progression. However, attaining this goal is impeded by the limited data availability and the high demand for interpretability in clinical settings. In this work, we propose an interpretable structure-constrained graph neural network (ISGNN) with enhanced features to automatically discriminate between PsP and TTP. This network employs a metric-based meta-learning strategy to aggregate class-specific graph nodes, focus on meta-tasks associated with various small graphs, thus improving the classification performance on small-scale datasets. Specifically, a node feature enhancement module is proposed to account for the relative importance of node features and enhance their distinguishability through inductive learning. A graph generation constraint module enables learning reasonable graph structures to improve the efficiency of information diffusion while avoiding propagation errors. Furthermore, model interpretability can be naturally enhanced based on the learned node features and graph structures that are closely related to the classification results. Comprehensive experimental evaluation of our method demonstrated excellent interpretable results in the diagnosis of glioma progression. In general, our work provides a novel systematic GNN approach for dealing with data scarcity and enhancing decision interpretability. Our source codes will be released at https://github.com/SJTUBME-QianLab/GBM-GNN.

Evaluating the Gray Level Co-Occurrence Matrix-Based Texture Features of Magnetic Resonance Images for Glioblastoma Multiform Patients' Treatment Response Assessment.

Medical images of cancer patients are usually evaluated qualitatively by clinical specialists which makes the accuracy of the diagnosis subjective and related to the skills of clinicians. Quantitative methods based on the textural feature analysis may be useful to facilitate such evaluations. This study aimed to analyze the gray level co-occurrence matrix (GLCM)-based texture features extracted from T1-axial magnetic resonance (MR) images of glioblastoma multiform (GBM) patients to determine the distinctive features specific to treatment response or disease progression. 20 GLCM-based texture features, in addition to mean, standard deviation, entropy, RMS, kurtosis, and skewness were extracted from step I MR images (obtained 72 h after surgery) and step II MR images (obtained three months later). Responded and not responded patients to treatment were classified manually based on the radiological evaluation of step II images. Extracted texture features from Step I and Step II images were analyzed to determine the distinctive features for each group of responsive or progressive diseases. MATLAB 2020 was applied to feature extraction. SPSS version 26 was used for the statistical analysis. P value < 0.05 was considered statistically significant. Despite no statistically significant differences between Step I texture features for two considered groups, almost all step II extracted GLCM-based texture features in addition to entropy M and skewness were significantly different between responsive and progressive disease groups. GLCM-based texture features extracted from MR images of GBM patients can be used with automatic algorithms for the expeditious prediction or interpretation of response to the treatment quantitatively besides qualitative evaluations.

Dendritic Cells as Adjuvant Therapy to Decrease Mortality for Glioblastoma Patients: Meta-Analysis

Highlight: Dendritic cells (DC) are one type of immune therapy that is being explored to improve treatment effectiveness in glioblastoma multiforme (GBM). DC was predicted to improve survival rates in GBM patients within 3 years. Effects of DC in the fifth year need to be explored to prove their. effectiveness in increasing the GBM survival rate. ABSTRACT Introduction: Glioblastoma multiforme (GBM) is a primary neoplasm of the central nervous system with a low survival rate, requiring more effective treatment to improve long-term survival. Dendritic cell (DC) therapy is expected to reduce tumor progressivity. Obective: The purpose of this meta-analysis was to analyze the administration of DC in reducing mortality in GBM patients. Methods: A systematic literature search was conducted using the PRISMA method through the Embase database, PubMed, and the Cochrane Controlled Trials Register for relevant studies between giving DC to GBM patients with conventional therapy on the number of living patients compared to controls. Article quality was assessed using the Newcastle-Ottawa Scale and statistically analyzed using RevMan 5.4. Results: Of the 14 articles, the rates of reduction in the probability of death during the first three years after initiation of therapy were 26%, 36%, and 38%, respectively [1st-y HR: 0.74 (0.57-0.95), I2: 15%, p=0.02; 2nd-y HR: 0.64 (0.51-0.81), I2: 14%, p=0.0002; 3rd-y HR: 0.62 (0.48-0.81), I2: 0%, p=0.0004]. However, there was no difference after 5 years [HR 0.81 (0.62-1.06), I2: 0%, p=0.13]. Conclusion: The DC vaccine reduces the likelihood of death in the early years of therapy but has not been proven for long-term therapy.

Bias-force guided simulations combined with experimental validations towards GPR17 modulators identification

Glioblastoma Multiforme (GBM) is known to be by far the most aggressive brain tumor to affect adults. The median survival rate of GBM patient’s is < 15 months, while the GBM cells aggressively develop resistance to chemo- and radiotherapy with their self-renewal capacity which suggests the pressing need to develop novel preventative measures. We have recently proved that GPR17 —an orphan G protein-coupled receptor— is highly expressed on the GBM cell surface and it has a vital role to play in the disease progression. Despite the progress made on GBM downregulation, there still remain difficulties in developing a promising modulator for GPR17, till date. Here, we have performed robust virtual screening combined with biased-force pulling molecular dynamic (MD) simulations to predict high-affinity GPR17 modulators followed by experimental validation. Initially, the database containing 1379 FDA-approved drugs were screened against the orthosteric binding pocket of the GPR17. The external bias-potentials were then applied to the screened hits during the MD simulations which enabled to predict a spectrum of rupture peak force values that were used to select four approved drugs –ZINC000003792417 (Sacubitril), ZINC000014210457 (Victrelis), ZINC000001536109 (Pralatrexate) and ZINC000003925861 (Vorapaxar)– as top hits. The hits selected turns out to demonstrate unique dissociation pathways, interaction pattern, and change in polar network over time. Subsequently the selected hits with GPR17 were measured by inhibiting the forskolin-stimulated cAMP accumulation in GBM cell lines, LN229 and SNB19. The ex vivo validations shows that Sacubitril drug can act as a full agonist, while Vorapaxar functions as a partial agonist for GPR17. The pEC50 of Sacubitril was identified as 4.841 and 4.661 for LN229 and SNB19, respectively. Small interference of the RNA (siRNA)– silenced the GPR17 to further validate the targeted binding of Sacubitril with GPR17. In the current investigation, we have identified new repurposable GPR17 specific drugs which are likely to increase the opportunity to treat orphan deadly diseases.

Frequency and reasons for unplanned transfer to the primary acute care service of inpatient rehabilitation glioblastoma multiforme patients.

To determine the percentage of and factors associated with unplanned transfer to the acute care service of glioblastoma multiforme acute rehabilitation inpatients. Retrospective review of glioblastoma multiforme acute rehabilitation inpatients admitted 4/1/2016-3/31/2020 at a National Cancer Institute Comprehensive Cancer Center. One hundred thirty-nine consecutive admissions of unique glioblastoma multiforme acute rehabilitation inpatients were analyzed. Fifteen patients (10.7%, 95% confidence interval 6.5-17.1%) were transferred to the acute care service for unplanned reasons. The most common reasons for transfer back were neurosurgical complication 6/15(40%), neurologic decline due to mass effect 4/15(26.7%), and pulmonary embolism 2/15(13.3%). Older age (p = 0.010), infection prior to acute inpatient rehabilitation transfer (p = 0.020), and lower activity measure of post-acute care 6-click basic mobility scores (p = 0.048) were significantly associated with transfer to the acute care service. Patients who transferred to the acute care service had significantly lower overall survival than patients who did not transfer off (log-rank test p = 0.001). Acute inpatient physiatrists should closely monitor patients for neurosurgical and neurologic complications. The variables significantly associated with transfer to the acute care service may help identify patients at increased risk for medical complications who may require closer observation.

Case report of intracranial large vessel occlusion in glioblastoma multiforme patient after radiation therapy.

Vasculopathy of the large arteries is a prominent complication of radiotherapy. Radiation-induced cerebral vasculopathy can cause arterial stenosis/occlusion, cerebral hemorrhage, and aneurysm formation. We report a cases of glioblastoma multiforme (GBM) with occlusive radiation vasculopathy (ORV). This 28-year-old patient who suffered from GBM had surgery for cytoreduction and received postoperative CCRT. We adopted the radiotherapy and oncology group radiation guideline. This patient had cerebrovascular accident episodes without any known risk. Therefore, ORV was highly suspected and vascular stenosis was confirmed using magnetic resonance angiography (MRA) or digital subtraction angiography. Extracranial-intracranial bypass was performed and patency was confirmed. The patient had not suffered from recurrent symptoms of transient ischemic attack or ischemic stroke for 1.5 years. This is the first article to report bypass surgery for GBM patients. Although the median survival rate of GBM is approximately 15 months, the short survival time may be sufficient for occlusive vasculopathy to occur. Regular follow-up magnetic resonance imaging assessments are recommended, as is MRA as a screening tool for the early diagnosis of ORV.The Stenting versus Aggressive Medical Management for Preventing Recurrent Stroke in Intracranial Stenosis (SAMMPRIS) trial focused on atherosclerotic intracranial arterial stenosis, revealing that aggressive medical management was superior to stenting for secondary stroke prevention; however, it did not mention radiation-induced vasculopathy. Bypass surgery has yielded some positive outcomes. In the absence of contraindications, antiplatelet or anticoagulation agents could be added, and bypass surgery could be performed because there was no stent in the distal intracranial arteries. MRA is a potential screening tool for ORV in GBM patients and bypass surgery could be performed to improve brain perfusion. Bypass surgery could help patient with occlusive radiation vasculopathy.

Evaluation of the Expression of miRNAs, LncRNAs, and their Target Gene, Caspase 3 in Glioblastoma Multiform: A Case-Control Study.

Glioblastoma multiform (GBM) is an invasive cancer that causes high mortality in patients. Disruption of the apoptosis process is one of the main pathogenesis of the disease. Recently, LncRNAs and miRNAs have been shown to play an important role in the process of apoptosis. To follow the aim of study, 100 patients participated in the two groups of 50 individuals, including 50 GBM patients and 50 healthy individuals as the control group. Mononuclear cells were isolated from peripheral blood samples and RNA extraction was done. The expression changes of miR-17-5p, miR-20-5p, LINC01605, FAS-AS1, and Caspase 3 were examined using RT-PCR in both groups. Expression of LINC01605, miR-20-5p, and miR-17-5p increased in patients, while Caspase 3 and FAS-AS1 decreased; the difference was statistically significant between the two groups. In addition, it was found that these factors have the appropriate sensitivity and specificity as diagnostic markers. Finally, It is suggested to use the LINC01605, FAS-AS1, miR-20-5p, miR-17-5p, and Caspase 3 as apoptosis predictors in the GM patients.

Comprehensive analysis of the prognosis and immune infiltrates for the BET protein family reveals the significance of BRD4 in glioblastoma multiforme.

Background: Glioblastoma multiforme (GBM) is the most common and invasive primary central nervous system tumor. The prognosis after surgery, radiation and chemotherapy is very poor. Bromodomain (BRD) proteins have been identified in oncogenic rearrangements, and play a key role in the development of multiple cancers. However, the relationship between BET proteins and prognosis of GBM are still worth exploring, and the distinct functions of BET proteins and tumor immunology in GBM have not been fully elucidated. Therefore, it is particularly important to develop effective biomarkers to predict the prognosis of GBM patients. Methods: Metascape, David, Kaplan-Meier Plotter, Oncomine, GEPIA, TCGA, TIMER, and LinkedOmics databases were used to assess the expression and prognosis for BET proteins in GBM. ROC analysis of risk model was established to identify the correlation between BET genes and overall survival in GBM patients. TIMER and GEPIA databases were used to comprehensively investigate the correlation between BET genes and tumor immune infiltration cells. Moreover, the image of immunohistochemistry staining of BET proteins in normal tissue and tumor tissue were retrived from the HPA database. In addition, differential analysis and pathway enrichment analysis of BRD4 gene expression profile were also carried out. Finally, immune-fluorescence and Western blot were used to clarify the expression of BRD4 in GBM cells. Results: Bioinformatics analysis showed that the expression levels of BET genes in GBM may play an important role in oncogenesis. Specifically, bioinformatic and immunohistochemistry analysis showed that BRD4 protein was more highly expressed in tumor tissues than that in normal tissues. The high expression of BRD4 was associated with poor prognosis in GBM. The expression of BET genes were closely related to the immune checkpoint in GBM. The correlation effect of BRD4 was significantly higher than other BET genes, which represented negative correlation with immune checkpoint. The expression of BRD4 was positively associated with tumor purity, and negatively associated with immune infiltration abundance of macrophage, neutrophil and CD8+ T-cell, respectively. Cox analysis showed that the model had good survival prediction and prognosis discrimination ability. In addition, the expression levels of BRD4 protein was significantly higher in U-251MGcells than that in normal cells, which was consistent with the results of bioinformatics data. Conclusion: This study implied that BRD4 could be hopeful prognostic biomarker in GBM. The increased expression of BRD4 may act as a molecular marker to identify GBM patients with high-risk subgroups. BRD4 may be a valuable prognostic biomarker, and a potential target of precision therapy against GBM.

Comprehensive analysis of prognosis of patients with GBM based on 4 m6A-related lncRNAs and immune cell infiltration

ObjectiveTo investigate the immune cell infiltration status in glioblastoma multiforme (GBM) and construct a novel prognostic risk model that can predict patients’ prognosis. MethodsThe Cancer Genome Atlas (TCGA) database was used to obtain RNA-sequence information and relevant clinical data. We performed Pearson correlation, univariate Cox regression to screen m6A-related prognostic lncRNA. GMB patients’ samples were separated into different clusters through the ConsensusClusterPlus package. The risk score model was established through LASSO regression analysis. Besides, KEGG pathway enrichment analysis was implemented. CIBERSORT algorithm was used to analyze the difference of 22 types of immune cell infiltration in different cluster of GBM patient. Cox regression analyses were used to verify the independence of the model and correlation analysis was performed to demonstrate the link between our model and clinical characteristics of GBM patients. Experiments were used to validate the differential expression of the model lncRNA in patients with different prognosis. Results17 lncRNA related to prognosis were screened from 1021 m6A-related lncRNAs. Further, four m6A-related lncRNAs that were significantly correlated with GBM prognosis were selected to establish our prognostic risk model, which had excellent accuracy and can independently predict the prognosis of GBM patients. The infiltration fractions of T regulatory cells, T cells CD4 memory activated and neutrophils were positively associated with risk score, which suggested a significant relationship between the model and tumor immune microenvironment. ConclusionThe m6A-related RNA risk model offered potential for identifying biomarkers of therapy and predicting prognosis of GBM patients.

Fused deep learning paradigm for the prediction of o6-methylguanine-DNA methyltransferase genotype in glioblastoma patients: A neuro-oncological investigation

BackgroundThe O6-methylguanine-DNA methyltransferase (MGMT) is a deoxyribonucleic acid (DNA) repairing enzyme that has been established as an essential clinical brain tumor biomarker for Glioblastoma Multiforme (GBM). Knowing the status of MGMT methylation biomarkers using multi-parametric MRI (mp-MRI) helps neuro-oncologists to analyze GBM and its treatment plan. MethodThe hand-crafted radiomics feature extraction of GBM's subregions, such as edema(ED), tumor core (TC), and enhancing tumor (ET) in the machine learning (ML) framework, was investigated using support vector machine(SVM), K-Nearest Neighbours (KNN), random forest (RF), LightGBM, and extreme gradient boosting (XGB). For tissue-level analysis of the promotor genes in GBM, we used the deep residual neural network (ResNet-18) with 3D architecture, followed by EfficientNet-based investigation for variants as B0 and B1. Lastly, we analyzed the fused deep learning (FDL) framework that combines ML and DL frameworks. ResultStructural mp-MRI consisting of T1, T2, FLAIR, and T1GD having a size of 400 and 185 patients, respectively, for discovery and replication cohorts. Using the CV protocol in the ResNet-3D framework, MGMT methylation status prediction in mp-MRI gave the AUC of 0.753 (p < 0.0001) and 0.72 (p < 0.0001) for the discovery and replication cohort, respectively. We presented that the FDL is ∼7% superior to solo DL and ∼15% to solo ML. ConclusionThe proposed study aims to provide solutions for building an efficient predictive model of MGMT for GBM patients using deep radiomics features obtained from mp-MRI with the end-to-end ResNet-18 3D and FDL imaging signatures.

Significant Systemic Insulin Resistance is Associated With Unique Glioblastoma Multiforme Phenotype.

Some glioblastoma multiforme (GBM) are characterized by the presence of gemistocytes (GCs), a unique phenotype of reactive astrocytes. Certain GCs can be identified as neoplastic cells but these cells were also found to be associated with diabetes in non-neoplastic lesions of the central nervous system. Our aim was to find a correlation between insulin - resistance metabolic features and the presence of GCs in patients with newly diagnosed GBM. Medical records from histologically confirmed GBM patients were retrospectively extracted for different systemic metabolic variables. A statistic-based comparison was made between GBM, diabetic patients with and without GC. Patients with poorly controlled diabetes (ie, hemoglobin A1C ⩾ 8.0) were also compared between the 2 groups. A total of 220 newly diagnosed GBM patients were included in our study. 58 (26.3%) patients had a history of diabetes mellitus type 2 (DM2) at the time of admission. The rate of poorly-controlled DM2 was nearly as twice in the GC-GBM group than in the non-GC GBM group (18.75% vs 9.5%; P = .130). In the DM2 cohort, the subgroup of GC-GBM was significantly associated with demographic and metabolic features related to insulin resistance such as male gender predominance (89% vs 50%, P = .073) and morbid obesity (weight ⩾85 kg: OR 6.16; P = .0019 and mean BMI: 34.1 ± 11.42 vs 28.7 ± 5.44; P = .034 for group with and without GCs, respectively). In the poorly-controlled DM2 group, none of the GC-GBM patients were using insulin prior to diagnosis, compared to 61.1% in the non-GC GBM patients (OR = 0.04, P = .045). Systemic metabolic factors related to marked insulin resistance (DM2, morbid obesity, male gender) are associated with a unique histologic phenotype of GBM, characterized by the presence of GCs. This feature is prominent in poorly-controlled DM2 GBM patients who are not using synthetic insulin. This novel finding may add to the growing data on the relevance of glucose metabolism in astrocytes and in astrocytes associated with high-grade gliomas. In GBM patients, a correlation between patients' metabolic status, tumor's histologic phenotype, tumor's molecular changes, use of anti-diabetic drugs and the respective impact of these factor on survival warrants further investigation.

The possible relation between temporal muscle mass and glioblastoma multiforme prognosis via sarcopenia perspective.

The optimal sarcopenia measurement method in patients with a diagnosis of glioblastoma multiforme (GBM) is unknown. It has been found that temporal muscle thickness (TMT) may reflect sarcopenia and be associated with survival, but the relationship between temporal muscle area (TMA) and GBM prognosis has never been evaluated before. The primary outcome of the study was to evaluate the relationship between TMA/TMT and overall survival (OS) time in newly diagnosed GBM patients. The data of patients who presented at the university hospital between January 2009 and January 2019 with a confirmed diagnosis of glioblastoma multiforme at the time of diagnosis were analyzed retrospectively. Temporal muscle thickness and TMA were measured retrospectively from preoperative MRIs of patients diagnosed with GBM. Due to the small number of patients and the failure to determine a cut-off value with acceptable sensitivity and specificity using ROC analysis, the median values were chosen as the cut-off value. The patients were basically divided into two according to their median TMT (6.6 mm) or TMA (452 mm2 ) values, and survival analysis was performed with the Kaplan-Meier analysis. The median TMT value was 6.6 mm, and the median TMA value was 452 mm2 . The median overall survival (OS) was calculated as 25.8 months in patients with TMT < 6.6 mm, and 15.8 months in patients with TMT ≥ 6.6 mm (p = 0.29). The median overall survival (OS) of patients with TMA < 452mm2 was 26.3 months, and the group with TMA ≥ 452mm2 was 14.6 months (p = 0.06). The median disease-free survival was 18.3 months (%95 CI: 13.2-23.4) in patients with TMT < 6.6mm, while mDFS was 10.9 (%95 CI: 8.0-13.8) months in patients with TMT ≥ 6.6mm (p = 0.21). The median disease-free survival was found to be 21.0 months (%95 CI: 15.8-26.1) in patients with TMA < 452 mm2 and 10.5 months (%95 CI: 7.8-13.2) in patients with TMA ≥ 452 mm2 (p = 0.018). No association could be demonstrated between TMT or TMA and OS of GBM patients. In addition, the median DFS was found to be longer in patients with low TMA. There is an unmet need to determine the optimal method of sarcopenia in GBM patients.

The neutrophil extracellular traps-related gene signature predicts the prognosis of glioblastoma multiforme.

This research hoped to explore the molecular mechanism of neutrophil extracellular traps (NETs) on glioblastoma multiforme (GBM) progression, and develop a promising prognostic signature for GBM based on NETs-related genes (NETGs). Gene expression data and clinical data of GBM tumour samples were downloaded from TCGA and CGGA databases. NETs-related molecular subtypes were explored by using ConsensusClusterPlus. The NETGs with a prognostic value were identified, and then a prognostic model was constructed using LASSO Cox regression. The predicted performance of the prognostic model was evaluated using TCGA training and CGGA validation cohorts. Moreover, independent prognostic indicators were identified by univariate and multivariate analysis to generate the nomogram model. The sensitivities for antitumor drugs and immunotherapy were predicted. Finally, hub genes in the prognostic model were validated using qPCR analysis. GBM patients were divided into two molecular subtypes with significant differences in tumour microenvironment (TME) score, survival, and immune infiltration. A NETGs signature was constructed based on seven genes (CPPED1, F3, G0S2, MME, MMP9, MAPK1, and MPO), which had a high value for predicting prognosis. A nomogram was constructed by two independent prognostic factors (age and risk score), which could be used to predict 1-, 2-, and 3-year survival probability of GBM. Patients in the high-risk group were more sensitive to bicalutamide, gefitinib, and dasatinib; patients in the low-risk group were associated with poor response to immunotherapy. The validation of the six genes in the prognostic model was consistent with the results of bioinformatics analysis. The NETs-based prognostic model and nomogram proposed in this study are promising prognostic prediction tools for GBM, which may provide new ideas for the development of precise tumour targeted therapy.

ALKBH5-PYCR2 Positive Feedback Loop Promotes Proneural-Mesenchymal Transition Via Proline Synthesis In GBM.

AlkB homolog 5, RNA demethylase (ALKBH5) is abnormally highly expressed in glioblastoma multiforme (GBM) and is negatively correlated with overall survival in GBM patients. In this study, we found a new mechanism that ALKBH5 and pyrroline-5-carboxylate reductase 2 (PYCR2) formed a positive feedback loop involved in proline synthesis in GBM. ALKBH5 promoted PYCR2 expression and PYCR2-mediated proline synthesis; while PYCR2 promoted ALKBH5 expression through the AMPK/mTOR pathway in GBM cells. In addition, ALKBH5 and PYCR2 promoted GBM cell proliferation, migration, and invasion, as well as proneural-mesenchymal transition (PMT). Furthermore, proline rescued AMPK/mTOR activation and PMT after silencing PYCR2 expression. Our findings reveal an ALKBH5-PYCR2 axis linked to proline metabolism, which plays an important role in promoting PMT in GBM cells and may be a promising therapeutic pathway for GBM.

Development of a novel angiogenesis-related lncRNA signature to predict the prognosis and immunotherapy of glioblastoma multiforme.

Long noncoding RNA (lncRNA) can regulate tumorigenesis, angiogenesis, proliferation, and other tumor biological behaviors, and is closely related to the growth and progression of glioma. The purpose of this research was to investigate the role of angiogenesis-related lncRNA in the prognosis and immunotherapy of glioblastoma multiforme (GBM). Differential analysis was carried out to acquire angiogenesis-related differentially expressed lncRNAs (AR-DElncRNAs). The AR-DElncRNAs were then subjected to univariate Cox and least absolute shrinkage and selection operator (LASSO) analyses to construct a prognostic model. Based on the median risk score, patients were classified into high-risk and low-risk groups. Kaplan-Meier survival analysis was conducted to estimate the prognostic value of the prognostic model. In addition, a nomogram was built to predict individual survival probabilities by combining clinicopathological characteristics and a prognostic model. Furthermore, immune infiltration, immunotherapy, and drug sensitivity analyses were administered to investigate the differences between the high- and low-risk groups. We identified 3 lncRNAs (DGCR5, PRKAG2-AS1, and ACAP2-IT1) that were significantly associated with the survival of GBM patients from the 255 AR-DElncRNAs based on univariate Cox and LASSO analyses. Then, a prognostic model was structured according to these 3 lncRNAs, from which we found that high-risk GBM patients had a worse prognosis than that of low-risk patients. Moreover, the risk score was determined to be an independent prognostic factor [hazard ratio (HR) =1.444; 95% confidence interval (CI): 1.014-2.057; P<0.05]. The immune microenvironment analysis revealed that the immune score, stromal score, and Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) score were significantly higher in the high-risk group than in the low-risk group. Neutrophils, macrophages, immature dendritic cells (iDCs), natural killer (NK) CD56dim cells, activated DCs (aDCs), and uncharacterized cells were different in the high- and low-risk groups. In addition, the high-risk group had a stronger sensitivity to immunotherapy. Furthermore, the sensitivity of 28 potential chemotherapeutic drugs differed significantly between the high- and low-risk groups. A novel angiogenesis-related lncRNA signature could be used to predict the prognosis and treatment of GBM.

A retrospective chart review to identify the involvement of palliative care with glioblastoma multiforme patients.

Glioblastoma Multiforme is a deadly brain cancer that is very challenging for patients and their families. It is associated with rapid progression, cognitive decline, and a low survival rate. To determine whether deceased Glioblastoma Multiforme patients had received follow-up service from the palliative care team before their death, whether invasive measures had been reduced, and whether the last antineoplastic treatment was given within 14-30 days before death. A retrospective study utilizing chart data from January 2020 to March 2022 from an institutional project. Data were gathered to reflect selected indicators of quality of care for palliative care patients. Of the 30 hospitalized patients with Glioblastoma Multiforme who had died while in hospital, 50% had received support from the palliative care team. Two patients (6.7%) had undergone antineoplastic treatment in the last 14 days of life and 13 (43.3%) had an order limiting invasive measures defined in the last two weeks of life. In half of the patients monitored by a palliative care team, antineoplastic treatment and limitation of invasive measures occurred in the last 14 days of life. This may be associated with increased suffering of patients, family members, and professionals. Discussions about end-of-life care-related choices and goals of care need to be respected.

Engineering the glioblastoma microenvironment with bioactive nanoparticles for effective immunotherapy.

While immunotherapies have revolutionized treatment for other cancers, glioblastoma multiforme (GBM) patients have not shown similar positive responses. The limited response to immunotherapies is partly due to the unique challenges associated with the GBM tumor microenvironment (TME), which promotes resistance to immunotherapies, causing many promising therapies to fail. There is, therefore, an urgent need to develop strategies that make the TME immune permissive to promote treatment efficacy. Bioactive nano-delivery systems, in which the nanoparticle, due to its chemical composition, provides the pharmacological function, have recently emerged as an encouraging option for enhancing the efficacy of immunotherapeutics. These systems are designed to overcome immunosuppressive mechanisms in the TME to improve the efficacy of a therapy. This review will discuss different aspects of the TME and how they impede therapy success. Then, we will summarize recent developments in TME-modifying nanotherapeutics and the in vitro models utilized to facilitate these advances.

Dosimetric comparative study of conformal radiation techniques in patients with glioblastoma multiforme

Purpose: The purpose of the current study is to determine whether patients diagnosed with glioblastoma multiforme (GBM) who underwent radiotherapy (RT) using intensity-modulated RT (IMRT) technique were benefitting from this highly conformal treatment in terms of doses received by planning target volume (PTV) and organs at risk (OARs) in comparison to three-dimensional conventional RT (3DCRT). Materials and Methods: Twelve patients treated with IMRT for GBM were selected for dosimetric comparison with 3DCRT. The prescribed dose was 60 Gy in 30 fractions and seven non-coplanner beams were used in IMRT to cover 95% of target volume. The irradiated patients of GBM were retrieved and replanned with 3DCRT techniques. Dosimetric comparison was done by performing two plans for the same patient; prescription dose and normal tissue constraints were identical for both plans. The dose–volume histograms of target volumes and OAR, dose conformity, and dose homogeneity with 3DCRT and IMRT planning were compared. Statistical analysis was performed to determine the differences. Results: The mean conformity index was 0.99 ± 0.001 for IMRT and 0.97 ± 0.002 for 3DCRT, P = 0.001. The mean homogeneity index was 1.03 ± 0.02 for IMRT and 1.06 ± 0.009 for 3DCRT, P = 0.003, which is statistically significant. The IMRT technique enables dose reduction of normal tissues including brainstem (Dmean by 33.78 ± 5.34 and Dmax 51.84 ± 4.43), optic chiasm (Dmean by 36.92 ± 1.99 and Dmax 44.61 ± 3.72), left optic nerve (Dmean by 28.97 ± 6.51 and Dmax 46.08 ± 10.58), right optic nerve (Dmean by 31.93 ± 11.68 and Dmax 44.63 ± 13.54), left eye (Dmean by 18.66 ± 8.92 and Dmax 37.43 ± 13.47), right eye (Dmean by 14.40 ± 4.87 and Dmax 40.37 ± 11.37), left lens (by Dmax 5.45 ± 1.85), and right lens (Dmax 5.07 ± 0.63). Conclusion: The IMRT provides a real dosimetric advantage, especially for normal brain tissue, and in terms of target coverage. It allows treatment of tumors while respecting OARs' dose constraints. The IMRT technique shows significant advantage in PTV coverage, dose homogeneity, and conformity. In IMRT, the coverage is better where PTV was overlapping with critical OARs.