Glioblastoma Patients Research Articles

Role of glioma stem cells in promoting tumor chemo- and radioresistance: A systematic review of potential targeted treatments.

Gliomas pose a significant challenge to effective treatment despite advancements in chemotherapy and radiotherapy. Glioma stem cells (GSCs), a subset within tumors, contribute to resistance, tumor heterogeneity, and plasticity. Recent studies reveal GSCs' role in therapeutic resistance, driven by DNA repair mechanisms and dynamic transitions between cellular states. Resistance mechanisms can involve different cellular pathways, most of which have been recently reported in the literature. Despite progress, targeted therapeutic approaches lack consensus due to GSCs' high plasticity. To analyze targeted therapies against GSC-mediated resistance to radio- and chemotherapy in gliomas, focusing on underlying mechanisms. A systematic search was conducted across major medical databases (PubMed, Embase, and Cochrane Library) up to September 30, 2023. The search strategy utilized relevant Medical Subject Heading terms and keywords related to including "glioma stem cells", "radiotherapy", "chemotherapy", "resistance", and "targeted therapies". Studies included in this review were publications focusing on targeted therapies against the molecular mechanism of GSC-mediated resistance to radiotherapy resistance (RTR). In a comprehensive review of 66 studies on stem cell therapies for SCI, 452 papers were initially identified, with 203 chosen for full-text analysis. Among them, 201 were deemed eligible after excluding 168 for various reasons. The temporal breakdown of studies illustrates this trend: 2005-2010 (33.3%), 2011-2015 (36.4%), and 2016-2022 (30.3%). Key GSC models, particularly U87 (33.3%), U251 (15.2%), and T98G (15.2%), emerge as significant in research, reflecting their representativeness of glioma characteristics. Pathway analysis indicates a focus on phosphoinositide 3-kinase/protein kinase B/mammalian target of rapamycin (mTOR) (27.3%) and Notch (12.1%) pathways, suggesting their crucial roles in resistance development. Targeted molecules with mTOR (18.2%), CHK1/2 (15.2%), and ATP binding cassette G2 (12.1%) as frequent targets underscore their importance in overcoming GSC-mediated resistance. Various therapeutic agents, notably RNA inhibitor/short hairpin RNA (27.3%), inhibitors (e.g., LY294002, NVP-BEZ235) (24.2%), and monoclonal antibodies (e.g., cetuximab) (9.1%), demonstrate versatility in targeted therapies. among 20 studies (60.6%), the most common effect on the chemotherapy resistance response is a reduction in temozolomide resistance (51.5%), followed by reductions in carmustine resistance (9.1%) and doxorubicin resistance (3.0%), while resistance to RTR is reduced in 42.4% of studies. GSCs play a complex role in mediating radioresistance and chemoresistance, emphasizing the necessity for precision therapies that consider the heterogeneity within the GSC population and the dynamic tumor microenvironment to enhance outcomes for glioblastoma patients.

Automated glioblastoma patient classification using hypoxia levels measured through magnetic resonance images

IntroductionThe challenge of treating Glioblastoma (GBM) tumors is due to various mechanisms that make the tumor resistant to radiation therapy. One of these mechanisms is hypoxia, and therefore, determining the level of hypoxia can improve treatment planning and initial evaluation of its effectiveness in GBM. This study aimed to design an intelligent system to classify glioblastoma patients based on hypoxia levels obtained from magnetic resonance images with the help of an artificial neural network (ANN).Material and methodMR images and PET measurements were available for this study. MR images were downloaded from the Cancer Imaging Archive (TCIA) database to classify glioblastoma patients based on hypoxia. The images in this database were prepared from 27 patients with glioblastoma on T1W + Gd, T2W-FLAIR, and T2W. Our designed algorithm includes various parts of pre-processing, tumor segmentation, feature extraction from images, and matching these features with quantitative parameters related to hypoxia in PET images. The system’s performance is evaluated by categorizing glioblastoma patients based on hypoxia.ResultsThe results of classification with the artificial neural network (ANN) algorithm were as follows: the highest sensitivity, specificity, and accuracy were obtained at 86.71, 85.99 and 83.17%, respectively. The best specificity was related to the T2W-EDEMA image with the tumor to blood ratio (TBR) as a hypoxia parameter. T1W-NECROSIS image with the TBR parameter also showed the highest sensitivity and accuracy.ConclusionThe results of the present study can be used in clinical procedures before treating glioblastoma patients. Among these treatment approaches, we can mention the radiotherapy treatment design and the prescription of effective drugs for the treatment of hypoxic tumors.

Radiotherapy Plan Quality Assurance in NRG Oncology Trials for Brain and Head/Neck Cancers: An AI-Enhanced Knowledge-Based Approach.

The quality of radiation therapy (RT) treatment plans directly affects the outcomes of clinical trials. KBP solutions have been utilized in RT plan quality assurance (QA). In this study, we evaluated the quality of RT plans for brain and head/neck cancers enrolled in multi-institutional clinical trials utilizing a KBP approach. The evaluation was conducted on 203 glioblastoma (GBM) patients enrolled in NRG-BN001 and 70 nasopharyngeal carcinoma (NPC) patients enrolled in NRG-HN001. For each trial, fifty high-quality photon plans were utilized to build a KBP photon model. A KBP proton model was generated using intensity-modulated proton therapy (IMPT) plans generated on 50 patients originally treated with photon RT. These models were then applied to generate KBP plans for the remaining patients, which were compared against the submitted plans for quality evaluation, including in terms of protocol compliance, target coverage, and organ-at-risk (OAR) doses. RT plans generated by the KBP models were demonstrated to have superior quality compared to the submitted plans. KBP IMPT plans can decrease the variation of proton plan quality and could possibly be used as a tool for developing improved plans in the future. Additionally, the KBP tool proved to be an effective instrument for RT plan QA in multi-center clinical trials.

P.097 Glioblastoma treatment, end-of-life resource utilization, and outcomes in Ontario

Background: The end-of-life (EoL) phase of care is pivotal for glioblastoma (GBM) patients. While early integration of palliative care has shown benefits in various cancer types, its role in GBM care remains underexplored. This study aims to characterize EoL care patterns in GBM patients, assessing their temporal evolution, regional disparities, and socioeconomic influences. Methods: This is retrospective study of all patients with GBM treated in Ontario between 1994 and 2018 using the ICES data repository. Variables analyzed included patient demographics, comorbidities, palliative care utilization, and aggressive/supportive care components. Results: We identified 9,013 GBM patients within the study period. There was a gradual increase in palliative care utilization over time, accompanied by a decrease in in-hospital deaths. However, the proportion of patients receiving chemotherapy in the last 14 days of life increased. Multivariate logistic regression found socioeconomic status influenced palliative care access and rural patients also had a higher rate of in-hospital deaths, possibly due to limitations in outpatient palliative care services. Conclusions: The findings in this study clarify the status of EoL care for GBM patients within Ontario, and demonstrates key areas for future research, underscoring the need for standardized EoL care practices to enhance the quality of care for GBM patients.

P.091 Synthetic data reliably reproduces brain tumor primary research data

Background: Synthetic data has garnered heightened attention in contemporary research due to confidentiality barriers and its capacity to simulate variables challenging to obtain. This study aimed to evaluate the reliability and validity of synthetic data in the context of neuro-oncology research, comparing findings from two published studies with results from synthetic datasets. Methods: Two published neuro-oncology studies focusing on prognostic factors such as serum albumin and systemic inflammation scores were selected, and their methodologies were replicated using MDClone Platform to generate five synthetic datasets for each. We used Chi-Square test to assess inter-variability between synthetic datasets. Survival outcomes were evaluated using Kaplan-Meier and t-test was used to determine statistical significance. Results: Findings from synthetic data consistently matched outcomes from both original articles, with serum albumin and systemc inflammation scores correlating with survival prognosis in glioblastoma and metastasis patients (p<0.05) Reported findings, demographic trends and survival outcomes showed significant similarity (P > 0.05) with synthetic datasets. Conclusions: Synthetic data consistently reproduced the statistical attributes of real patient data. Integrating synthetic data into clinical research offers excellent potential for providing accurate predictive insights without compromising patient privacy. In neuro-oncology, where patient follow-up pose challenges, the adoption of synthetic datasets can be transformative.

Cytotoxic effects of nerve growth factor and its combinations with chemotherapeutic drugs on anaplastoc astrocytoma, glioblastoma and medubloblastoma cells <i>in vitro</i>

BACKGROUND: Currently, the effectiveness of the treatment of malignant tumors using surgical resection, radiotherapy and chemotherapy is insufficient. Therefore, new research is needed to find alternative molecules with antitumor effects. It is known that nerve growth factor (NGF) inhibits invasion, migration, and angiogenesis of tumor cells. Studying the effects of NGF on brain tumors, as well as its combinations with chemotherapy drugs used in medicine, may contribute to the development of new treatment regimens for malignant neoplasms in the central nervous system. AIM: The purpose of this study is an exploration the molecular and cellular mechanisms of anticancer effects of individual and combined preparations of NGF and chemotherapeutic drugs on brain tumor cells (gliomas C6, U251, anaplastic astrocytoma, glioblastoma and medulloblastoma). MATERIALS AND METHODS: The study was performed on rat glioma C6, human U251 glioma cell lines, as well as on primary cells of anaplastic astrocytoma (n = 9), glioblastoma (n = 9) and medulloblastoma (n = 38) patients. The cytotoxicity of chemotherapeutic drugs, NGF and their combinations against tumor cells was assessed using the MTT assay. The expression of TrkA and p75 receptors on anaplastic astrocytoma, glioblastoma and medulloblastoma cells was assessed by immunofluorescence analysis using anti-TrkA and anti-p75 monoclonal antibodies. RESULTS: Nerve growth factor exhibits in vitro cytotoxic activity that exceeds the activity of chemotherapy drugs towards rat glioma C6, human U251, anaplastic astrocytoma (AA), glioblastoma (GBM) and medulloblastoma cells. The cytotoxic activity of NGF in combination with chemotherapy drugs is significantly higher than the activity of the individual NGF drug against medulloblastoma cells, while against anaplastic astrocytoma cells it is comparable to the indicators of the isolated action of NGF, and lower for glioblastoma cells. The effectiveness of the cytotoxic effect of the combinations NGF + cisplatin and NGF + temozolomide (TMZ) on AA and GBM cells correlates with both the expression of TrkA, p75 receptors, and their coexpression, indicating that expression indicators can be considered as markers of tumor cell sensitivity to NGF. CONCLUSIONS: The data obtained allow us to consider NGF as a potential anticancer drug for the treatment of brain tumors. Thus, NGF can act as a potential anticancer drug for the development of new therapeutic regimens for brain tumors.

Advances in Anti-Cancer Drug Development: Metformin as Anti-Angiogenic Supplemental Treatment for Glioblastoma.

According to the WHO 2016 classification, glioblastoma is the most prevalent primary tumor in the adult central nervous system (CNS) and is categorized as grade IV. With an average lifespan of about 15 months from diagnosis, glioblastoma has a poor prognosis and presents a significant treatment challenge. Aberrant angiogenesis, which promotes tumor neovascularization and is a prospective target for molecular target treatment, is one of its unique and aggressive characteristics. Recently, the existence of glioma stem cells (GSCs) within the tumor, which are tolerant to chemotherapy and radiation, has been linked to the highly aggressive form of glioblastoma. Anti-angiogenic medications have not significantly improved overall survival (OS), despite various preclinical investigations and clinical trials demonstrating encouraging results. This suggests the need to discover new treatment options. Glioblastoma is one of the numerous cancers for which metformin, an anti-hyperglycemic medication belonging to the Biguanides family, is used as first-line therapy for type 2 diabetes mellitus (T2DM), and it has shown both in vitro and in vivo anti-tumoral activity. Based on these findings, the medication has been repurposed, which has shown the inhibition of many oncopromoter mechanisms and, as a result, identified the molecular pathways involved. Metformin inhibits cancer cell growth by blocking the LKB1/AMPK/mTOR/S6K1 pathway, leading to selective cell death in GSCs and inhibiting the proliferation of CD133+ cells. It has minimal impact on differentiated glioblastoma cells and normal human stem cells. The systematic retrieval of information was performed on PubMed. A total of 106 articles were found in a search on metformin for glioblastoma. Out of these six articles were Meta-analyses, Randomized Controlled Trials, clinical trials, and Systematic Reviews. The rest were Literature review articles. These articles were from the years 2011 to 2024. Appropriate studies were isolated, and important information from each of them was understood and entered into a database from which the information was used in this article. The clinical trials on metformin use in the treatment of glioblastoma were searched on clinicaltrials.gov. In this article, we examine and evaluate metformin's possible anti-tumoral effects on glioblastoma, determining whether or not it may appropriately function as an anti-angiogenic substance and be safely added to the treatment and management of glioblastoma patients.

Identification of established and novel extracellular matrix components in glioblastoma as targets for angiogenesis and prognosis.

Glioblastomas (GBM) are aggressive tumors known for their heterogeneity, rapid proliferation, treatment resistance, and extensive vasculature. Angiogenesis, the formation of new vessels, involves endothelial cell (EC) migration and proliferation. Various extracellular matrix (ECM) molecules regulate EC survival, migration, and proliferation. Culturing human brain EC (HBMEC) on GBM-derived ECM revealed a decrease in EC numbers compared to controls. Through in silico analysis, we explored ECM gene expression differences between GBM and brain normal glia cells and the impact of GBM microenvironment on EC ECM transcripts. ECM molecules such as collagen alpha chains (COL4A1, COL4A2, p < 0.0001); laminin alpha (LAMA4), beta (LAMB2), and gamma (LAMC1) chains (p < 0.0005); neurocan (NCAN), brevican (BCAN) and versican (VCAN) (p < 0.0005); hyaluronan synthase (HAS) 2 and metalloprotease (MMP) 2 (p < 0.005); MMP inhibitors (TIMP1-4, p < 0.0005), transforming growth factor beta-1 (TGFB1) and integrin alpha (ITGA3/5) (p < 0.05) and beta (ITGB1, p < 0.0005) chains showed increased expression in GBM. Additionally, GBM-influenced EC exhibited elevated expression of COL5A3, COL6A1, COL22A1 and COL27A1 (p < 0.01); LAMA1, LAMB1 (p < 0.001); fibulins (FBLN1/2, p < 0.01); MMP9, HAS1, ITGA3, TGFB1, and wingless-related integration site 9B (WNT9B) (p < 0.01) compared to normal EC. Some of these molecules: COL5A1/3, COL6A1, COL22/27A1, FBLN1/2, ITGA3/5, ITGB1 and LAMA1/B1 (p < 0.01); NCAN, HAS1, MMP2/9, TIMP1/2 and TGFB1 (p < 0.05) correlated with GBM patient survival. In conclusion, this study identified both established and novel ECM molecules regulating GBM angiogenesis, suggesting NCAN and COL27A1 are new potential prognostic biomarkers for GBM.

Prognostic stratification of glioblastoma patients by unsupervised clustering of morphology patterns on whole slide images furthering our disease understanding.

Glioblastoma (GBM) is a highly aggressive malignant tumor of the central nervous system that displays varying molecular and morphological profiles, leading to challenging prognostic assessments. Stratifying GBM patients according to overall survival (OS) from H&E-stained whole slide images (WSI) using advanced computational methods is challenging, but with direct clinical implications. This work is focusing on GBM (IDH-wildtype, CNS WHO Gr.4) cases, identified from the TCGA-GBM and TCGA-LGG collections after considering the 2021 WHO classification criteria. The proposed approach starts with patch extraction in each WSI, followed by comprehensive patch-level curation to discard artifactual content, i.e., glass reflections, pen markings, dust on the slide, and tissue tearing. Each patch is then computationally described as a feature vector defined by a pre-trained VGG16 convolutional neural network. Principal component analysis provides a feature representation of reduced dimensionality, further facilitating identification of distinct groups of morphology patterns, via unsupervised k-means clustering. The optimal number of clusters, according to cluster reproducibility and separability, is automatically determined based on the rand index and silhouette coefficient, respectively. Our proposed approach achieved prognostic stratification accuracy of 83.33% on a multi-institutional independent unseen hold-out test set with sensitivity and specificity of 83.33%. We hypothesize that the quantification of these clusters of morphology patterns, reflect the tumor's spatial heterogeneity and yield prognostic relevant information to distinguish between short and long survivors using a decision tree classifier. The interpretability analysis of the obtained results can contribute to furthering and quantifying our understanding of GBM and potentially improving our diagnostic and prognostic predictions.

Enhancing Temozolomide (TMZ) chemosensitivity using CRISPR-dCas9-mediated downregulation of O6-methylguanine DNA methyltransferase (MGMT).

Glioblastoma (GBM) stands out as the most prevalent and aggressive intracranial tumor, notorious for its poor prognosis. The current standard-of-care for GBM patients involves surgical resection followed by radiotherapy, combined with concurrent and adjuvant chemotherapy using Temozolomide (TMZ). The effectiveness of TMZ primarily relies on the activity of O6-methylguanine DNA methyltransferase (MGMT), which removes alkyl adducts from the O6 position of guanine at the DNA level, thereby counteracting the toxic effects of TMZ. In this study, we employed fusions of catalytically-inactive Cas9 (dCas9) to DNA methyltransferases (dCas9-DNMT3A) to selectively downregulation MGMT transcription by inducing methylation at MGMT promoter and K-M enhancer. Our findings demonstrate a significant reduction in MGMT expression, leading to intensified TMZ sensitivity in the HEK293T cell line. This study serves as a proof of concept for the utilization of CRISPR-based gene suppression to overcome TMZ resistance and enhance the lethal effect of TMZ in glioblastoma tumor cells.

Evaluation of the clinical use of MGMT methylation in extracellular vesicle-based liquid biopsy as a tool for glioblastoma patient management

Glioblastoma (GB) is a devastating tumor of the central nervous system characterized by a poor prognosis. One of the best-established predictive biomarker in IDH-wildtype GB is O6-methylguanine-DNA methyltransferase (MGMT) methylation (mMGMT), which is associated with improved treatment response and survival. However, current efforts to monitor GB patients through mMGMT detection have proven unsuccessful. Small extracellular vesicles (sEVs) hold potential as a key element that could revolutionize clinical practice by offering new possibilities for liquid biopsy. This study aimed to determine the utility of sEV-based liquid biopsy as a predictive biomarker and disease monitoring tool in patients with IDH-wildtype GB. Our findings show consistent results with tissue-based analysis, achieving a remarkable sensitivity of 85.7% for detecting mMGMT in liquid biopsy, the highest reported to date. Moreover, we suggested that liquid biopsy assessment of sEV-DNA could be a powerful tool for monitoring disease progression in IDH-wildtype GB patients. This study highlights the critical significance of overcoming molecular underdetection, which can lead to missed treatment opportunities and misdiagnoses, possibly resulting in ineffective therapies. The outcomes of our research significantly contribute to the field of sEV-DNA-based liquid biopsy, providing valuable insights into tumor tissue heterogeneity and establishing it as a promising tool for detecting GB biomarkers. These results have substantial implications for advancing predictive and therapeutic approaches in the context of GB and warrant further exploration and validation in clinical settings.

A longer time to relapse is associated with a larger increase in differences between paired primary and recurrent IDH wild-type glioblastomas at both the transcriptomic and genomic levels

Glioblastoma (GBM) is the most common malignant brain tumor in adults, which remains incurable and often recurs rapidly after initial therapy. While large efforts have been dedicated to uncover genomic/transcriptomic alternations associated with the recurrence of GBMs, the evolutionary trajectories of matched pairs of primary and recurrent (P-R) GBMs remain largely elusive. It remains challenging to identify genes associated with time to relapse (TTR) and construct a stable and effective prognostic model for predicting TTR of primary GBM patients. By integrating RNA-sequencing and genomic data from multiple datasets of patient-matched longitudinal GBMs of isocitrate dehydrogenase wild-type (IDH-wt), here we examined the associations of TTR with heterogeneities between paired P-R GBMs in gene expression profiles, tumor mutation burden (TMB), and microenvironment. Our results revealed a positive correlation between TTR and transcriptomic/genomic differences between paired P-R GBMs, higher percentages of non-mesenchymal-to-mesenchymal transition and mesenchymal subtype for patients with a short TTR than for those with a long TTR, a high correlation between paired P-R GBMs in gene expression profiles and TMB, and a negative correlation between the fitting level of such a paired P-R GBM correlation and TTR. According to these observations, we identified 55 TTR-associated genes and thereby constructed a seven-gene (ZSCAN10, SIGLEC14, GHRHR, TBX15, TAS2R1, CDKL1, and CD101) prognostic model for predicting TTR of primary IDH-wt GBM patients using univariate/multivariate Cox regression analyses. The risk scores estimated by the model were significantly negatively correlated with TTR in the training set and two independent testing sets. The model also segregated IDH-wt GBM patients into two groups with significantly divergent progression-free survival outcomes and showed promising performance for predicting 1-, 2-, and 3-year progression-free survival rates in all training and testing sets. Our findings provide new insights into the molecular understanding of GBM progression at recurrence and potential targets for therapeutic treatments.

A Novel Predictive Model Utilizing Retinal Microstructural Features for Estimating Survival Outcome in Patients with Glioblastoma.

Glioblastoma is a highly aggressive brain tumor with poor prognosis despite surgery and chemoradiation. The visual sequelae of glioblastoma have not been well characterized. This study assessed visual outcomes in glioblastoma patients through neuro-ophthalmic exams, imaging of the retinal microstructures/microvasculature, and perimetry. A total of 19 patients (9 male, 10 female, average age at diagnosis 69 years) were enrolled. Best-corrected visual acuity ranged from 20/20-20/50. Occipital tumors showed worse visual fields than frontal tumors (mean deviation - 14.9 and - 0.23, respectively, p < 0.0001). Those with overall survival (OS) < 15 months demonstrated thinner retinal nerve fiber layer and ganglion cell complex (p < 0.0001) and enlarged foveal avascular zone starting from 4 months post-diagnosis (p = 0.006). There was no significant difference between eyes ipsilateral and contralateral to radiation fields (average doses were 1370 cGy and 1180 cGy, respectively, p = 0.42). A machine learning algorithm using retinal microstructure and visual fields predicted patients with long (≥ 15 months) progression free and overall survival with 78% accuracy. Glioblastoma patients frequently present with visual field defects despite normal visual acuity. Patients with poor survival duration demonstrated significant retinal thinning and decreased microvascular density. A machine learning algorithm predicted survival; further validation is warranted.

The prognostic relevance of a gene expression signature in MRI-defined highly vascularized glioblastoma

BackgroundThe vascular heterogeneity of glioblastomas (GB) remains an important area of research, since tumor progression and patient prognosis are closely tied to this feature. With this study, we aim to identify gene expression profiles associated with MRI-defined tumor vascularity and to investigate its relationship with patient prognosis. MethodsThe study employed MRI parameters calculated with DSC Perfusion Quantification of ONCOhabitats glioma analysis software and RNA-seq data from the TCGA-GBM project dataset. In our study, we had a total of 147 RNA-seq samples, which 15 of them also had MRI parameter information. We analyzed the gene expression profiles associated with MRI-defined tumor vascularity using differential gene expression analysis and performed Log-rank tests to assess the correlation between the identified genes and patient prognosis. ResultsThe findings of our research reveal a set of 21 overexpressed genes associated with the high vascularity pattern. Notably, several of these overexpressed genes have been previously implicated in worse prognosis based on existing literature. Our log-rank test further validates that the collective upregulation of these genes is indeed correlated with an unfavorable prognosis. This set of genes includes a variety of molecules, such as cytokines, receptors, ligands, and other molecules with diverse functions. ConclusionsOur findings suggest that the set of 21 overexpressed genes in the High Vascularity group could potentially serve as prognostic markers for GB patients. These results highlight the importance of further investigating the relationship between the molecules such as cytokines or receptors underlying the vascularity in GB and its observation through MRI and developing targeted therapies for this aggressive disease.

Prognostic relevance of radiological findings on early postoperative MRI for 187 consecutive glioblastoma patients receiving standard therapy

Several prognostic factors are known to influence survival for patients treated with IDH-wildtype glioblastoma, but unknown factors may remain. We aimed to investigate the prognostic implications of early postoperative MRI findings. A total of 187 glioblastoma patients treated with standard therapy were consecutively included. Patients either underwent a biopsy or surgery followed by an early postoperative MRI. Progression-free survival (PFS) and overall survival (OS) were analysed for known prognostic factors and MRI-derived candidate factors: resection status as defined by the response assessment in neuro-oncology (RANO)-working group (no contrast-enhancing residual tumour, non-measurable contrast-enhancing residual tumour, or measurable contrast-enhancing residual tumour) with biopsy as reference, contrast enhancement patterns (no enhancement, thin linear, thick linear, diffuse, nodular), and the presence of distant tumours. In the multivariate analysis, patients with no contrast-enhancing residual tumour or non-measurable contrast-enhancing residual tumour on the early postoperative MRI displayed a significantly improved progression-free survival compared with patients receiving only a biopsy. Only patients with non-measurable contrast-enhancing residual tumour showed improved overall survival in the multivariate analysis. Contrast enhancement patterns were not associated with survival. The presence of distant tumours was significantly associated with both poor progression-free survival and overall survival and should be considered incorporated into prognostic models.

Sequential Evaluation of Hematology Markers as a Prognostic Factor in Glioblastoma Patients.

In our study, we investigated the prognostic significance of hematological markers-NLR (Neutrophil-to-Lymphocyte Ratio), PLR (Platelet-to-Lymphocyte Ratio), and RDW-CV (Red Blood Cell Distribution Width-Coefficient of Variation)-in 117 glioblastoma patients. The data collected from January 2016 to December 2018 included demographics, clinical scores, and treatment regimens. Unlike previous research, which often examined these markers solely before surgery, our unique approach analyzed them at multiple stages: preoperative, postoperative, and before adjuvant therapies. We correlated these markers with the overall survival (OS) and progression-free survival (PFS) using statistical tools, including ANOVA, Cox regression, and Kaplan-Meier survival analyses, employing SPSS version 29.0. Our findings revealed notable variations in the NLR, PLR, and RDW-CV across different treatment stages. The NLR and PLR decreased after surgery, with some stabilization post-STUPP phase (NLR: p = 0.007, η2p = 0.06; PLR: p = 0.001, η2p = 0.23), while the RDW-CV increased post-surgery and during subsequent treatments (RDW-CV: p < 0.001, η2p = 0.67). Importantly, we observed significant differences between the preoperative phase and other treatment phases. Additionally, a higher NLR and RDW-CV at the second-line treatment and disease progression were associated with an increased risk of death (NLR at 2nd line: HR = 1.03, p = 0.029; RDW-CV at progression: HR = 1.14, p = 0.004). We proposed specific marker cut-offs that demonstrated significant associations with survival outcomes when applied to Kaplan-Meier survival curves (NLR at 2nd line < 5: p < 0.017; RDW-CV at progression < 15: p = 0.007). An elevated NLR and RDW-CV at later treatment stages correlated with poorer OS and PFS. No significant preoperative differences were detected. These biomarkers may serve as non-invasive tools for glioblastoma management.

Profiling of Tumor-Infiltrating Immune Cells and Their Impact on Survival in Glioblastoma Patients Undergoing Immunotherapy with Dendritic Cells.

Glioblastomas (GBM) are the most common primary malignant brain tumors, comprising 2% of all cancers in adults. Their location and cellular and molecular heterogeneity, along with their highly infiltrative nature, make their treatment challenging. Recently, our research group reported promising results from a prospective phase II clinical trial involving allogeneic vaccination with dendritic cells (DCs). To date, six out of the thirty-seven reported cases remain alive without tumor recurrence. In this study, we focused on the characterization of infiltrating immune cells observed at the time of surgical resection. An analytical model employing a neural network-based predictive algorithm was used to ascertain the potential prognostic implications of immunological variables on patients' overall survival. Counterintuitively, immune phenotyping of tumor-associated macrophages (TAMs) has revealed the extracellular marker PD-L1 to be a positive predictor of overall survival. In contrast, the elevated expression of CD86 within this cellular subset emerged as a negative prognostic indicator. Fundamentally, the neural network algorithm outlined here allows a prediction of the responsiveness of patients undergoing dendritic cell vaccination in terms of overall survival based on clinical parameters and the profile of infiltrated TAMs observed at the time of tumor excision.

Comprehensive molecular characterization of long-term glioblastoma survivors

Fewer than 5 % glioblastoma (GBM) patients survive over five years and are termed long-term survivors (LTS), yet their molecular background is unclear. The present cohort included 72 isocitrate dehydrogenase (IDH)-wildtype GBM patients, consisting of 35 LTS and 37 short-term survivors (STS), and we employed whole exome sequencing, RNA-seq and DNA methylation array to delineate this largest LTS cohort to date. Although LTS and STS demonstrated analogous clinical characters and classical GBM biomarkers, CASC5 (P = 0.002) and SPEN (P = 0.013) mutations were enriched in LTS, whereas gene-to-gene fusions were concentrated in STS (P = 0.007). Importantly, LTS exhibited higher tumor mutation burden (P < 0.001) and copy number (CN) increase (P = 0.013), but lower mutant-allele tumor heterogeneity score (P < 0.001) and CN decrease (P = 0.026). Additionally, LTS demonstrated hypermethylated genome (P < 0.001) relative to STS. Differentially expressed and methylated genes both enriched in olfactory transduction. Further, analysis of the tumor microenvironment revealed higher infiltration of M1 macrophages (P = 0.043), B cells (P = 0.016), class-switched memory B cells (P = 0.002), central memory CD4+ T cells (P = 0.031) and CD4+ Th1 cells (P = 0.005) in LTS. We also separately analyzed a subset of patients who were methylation class-defined GBM, contributing 70.8 % of the entire cohort, and obtained similar results relative to prior analyses. Finally, we demonstrated that LTS and STS could be distinguished using a subset of molecular features. Taken together, the present study delineated unique molecular attributes of LTS GBM.

SMYD2 induced PGC1α methylation promotes stemness maintenance of glioblastoma stem cells.

The high fatality rate of glioblastoma (GBM) is attributed to glioblastoma stem cells (GSCs), which exhibit heterogeneity and therapeutic resistance. Metabolic plasticity of mitochondria is the hallmark of GSCs. Targeting mitochondrial biogenesis of GSCs is crucial for improving clinical prognosis in GBM patients. SMYD2-induced PGC1α methylation and followed nuclear export are confirmed by co-immunoprecipitation, cellular fractionation, and immunofluorescence. The effects of SMYD2/PGC1α/CRM1 axis on GSCs mitochondrial biogenesis are validated by oxygen consumption rate, ECAR, and intracranial glioma model. PGC1α methylation causes the disabled mitochondrial function to maintain the stemness, thereby enhancing the radio-resistance of GSCs. SMYD2 drives PGC1α K224 methylation (K224me), which is essential for promoting the stem-like characteristics of GSCs. PGC1α K224me is preferred binding with CRM1, accelerating PGC1α nuclear export and subsequent dysfunction. Targeting PGC1α methylation exhibits significant radiotherapeutic efficacy and prolongs patient survival. These findings unveil a novel regulatory pathway involving mitochondria that govern stemness in GSCs, thereby emphasizing promising therapeutic strategies targeting PGC1α and mitochondria for the treatment of GBM.

LINC00606 promotes glioblastoma progression through sponge miR-486-3p and interaction with ATP11B

BackgroundLncRNAs regulate tumorigenesis and development in a variety of cancers. We substantiate for the first time that LINC00606 is considerably expressed in glioblastoma (GBM) patient specimens and is linked with adverse prognosis. This suggests that LINC00606 may have the potential to regulate glioma genesis and progression, and that the biological functions and molecular mechanisms of LINC00606 in GBM remain largely unknown.MethodsThe expression of LINC00606 and ATP11B in glioma and normal brain tissues was evaluated by qPCR, and the biological functions of the LINC00606/miR-486-3p/TCF12/ATP11B axis in GBM were verified through a series of in vitro and in vivo experiments. The molecular mechanism of LINC00606 was elucidated by immunoblotting, FISH, RNA pulldown, CHIP-qPCR, and a dual-luciferase reporter assay.ResultsWe demonstrated that LINC00606 promotes glioma cell proliferation, clonal expansion and migration, while reducing apoptosis levels. Mechanistically, on the one hand, LINC00606 can sponge miR-486-3p; the target gene TCF12 of miR-486-3p affects the transcriptional initiation of LINC00606, PTEN and KLLN. On the other hand, it can also regulate the PI3K/AKT signaling pathway to mediate glioma cell proliferation, migration and apoptosis by binding to ATP11B protein.ConclusionsOverall, the LINC00606/miR-486-3p/TCF12/ATP11B axis is involved in the regulation of GBM progression and plays a role in tumor regulation at transcriptional and post-transcriptional levels primarily through LINC00606 sponging miR-486-3p and targeted binding to ATP11B. Therefore, our research on the regulatory network LINC00606 could be a novel therapeutic strategy for the treatment of GBM.Graphical LINC00606 is highly expressed in GBM patients with carcinogenic function and correlated with poor prognosis. LINC00606 regulates glioblastoma progression by sponging miR-486-3p and interacting with ATP11B.