Glioblastoma Samples Research Articles

Blockage of EGFR/AKT and mevalonate pathways synergize the antitumor effect of temozolomide by reprogramming energy metabolism in glioblastoma.

Metabolism reprogramming plays a vital role in glioblastoma (GBM) progression and recurrence by producing enough energy for highly proliferating tumor cells. In addition, metabolic reprogramming is crucial for tumor growth and immune-escape mechanisms. Epidermal growth factor receptor (EGFR) amplification and EGFR-vIII mutation are often detected in GBM cells, contributing to the malignant behavior. This study aimed to investigate the functional role of the EGFR pathway on fatty acid metabolism remodeling and energy generation. Clinical GBM specimens were selected for single-cell RNA sequencing and untargeted metabolomics analysis. A metabolism-associated RTK-fatty acid-gene signature was constructed and verified. MK-2206 and MK-803 were utilized to block the RTK pathway and mevalonate pathway induced abnormal metabolism. Energy metabolism in GBM with activated EGFR pathway was monitored. The antitumor effect of Osimertinib and Atorvastatin assisted by temozolomide (TMZ) was analyzed by an intracranial tumor model in vivo. GBM with high EGFR expression had characteristics of lipid remodeling and maintaining high cholesterol levels, supported by the single-cell RNA sequencing and metabolomics of clinical GBM samples. Inhibition of the EGFR/AKT and mevalonate pathways could remodel energy metabolism by repressing the tricarboxylic acid cycle and modulating ATP production. Mechanistically, the EGFR/AKT pathway upregulated the expressions of acyl-CoA synthetase short-chain family member 3 (ACSS3), acyl-CoA synthetase long-chain family member 3 (ACSL3), and long-chain fatty acid elongation-related gene ELOVL fatty acid elongase 2 (ELOVL2) in an NF-κB-dependent manner. Moreover, inhibition of the mevalonate pathway reduced the EGFR level on the cell membranes, thereby affecting the signal transduction of the EGFR/AKT pathway. Therefore, targeting the EGFR/AKT and mevalonate pathways enhanced the antitumor effect of TMZ in GBM cells and animal models. Our findings not only uncovered the mechanism of metabolic reprogramming in EGFR-activated GBM but also provided a combinatorial therapeutic strategy for clinical GBM management.

IFN-γ Triggered IFITM2 Expression to Induce Malignant Phenotype in Elderly GBM.

Advanced age is an important risk factor for the worse clinical presentation of gliomas, especially glioblastoma (GBM). The tumor microenvironment (TME) in elderly GBM (eGBM) patients is considerably different from that in young ones, which causes the inferior clinical outcome. Based on the data from the Chinese Glioma Genome Atlas RNA sequence (CGGA RNA-seq), the Cancer Genome Atlas RNA array (TCGA RNA-array), and gene set enrichment (GSE) 16011 array sets, the differential genes and function between eGBM (≥ 60years old) and young GBM (yGBM, 20-60years old) groups were explored. Immunohistochemistry (IHC) was utilized to depict the abundance of CD8+cells (the main resource of IFN-γ) and IFITM2 protein expression in GBM samples. Furthermore, reverse transcription-polymerase chain reaction (RT-PCR) and Western blotting (WB) were performed to verify the link between IFN-γ and IFITM2. Moreover, the small-interfering RNA (siRNA) of IFITM2 was used to explore the function of IFITM2 in GBM. Characterized by inflammatory TME and higher IFITM2 expression, eGBM harbored a shorter survival time. Chemotaxis and inflammatory cytokine-related genes were enriched in the eGBM group, with more infiltrative CD8+T cells. The IHC of CD8 and IFITM2-staining could demonstrate these results. In addition, the IFN-γ response pathway was activated in eGBM and resulted in a dismal outcome. Next, it was found that IFITM2 triggered by IFN-γ played a key role in IFN-γ-induced malignant phenotype in eGBM.

SUZ12 inhibition attenuates cell proliferation of glioblastoma via post-translational regulation of CDKN1B.

Human gliomas are aggressive brain tumors characterized by uncontrolled cell proliferation. Differential expression of Polycomb repressive complex 2 (PRC2) has been reported in various subtypes of glioma. However, the role of PRC2 in uncontrolled growth in glioma and its underlying molecular mechanisms remain to be elucidated. We aimed to investigate the functional role of PRC2 in human glioblastoma cell growth by silencing SUZ12, the non-catalytic core component of PRC2. Knockdown of SUZ12 was achieved by infecting T98G cells with lentivirus carrying sequences specifically targeting SUZ12 (shSUZ12). Gene expression was examined by quantitative PCR and western analysis. The impact of shSUZ12 on cell growth was assessed using a cell proliferation assay. Cell cycle distribution was analyzed by flow cytometry, and protein stability was evaluated in cycloheximide-treated cells. Subcellular localization was examined through immunofluorescence staining and biochemical cytoplasmic-nuclear fractionation. Gene expression analysis was also performed on human specimens from normal brain and glioblastoma patients. SUZ12 knockdown (SUZ12 KD) led to widespread decrease in the PRC2-specific histone mark, accompanied by a slowdown of cell proliferation through G1 arrest. In SUZ12 KD cells, the degradation of CDKN1B protein was reduced, resulting from alterations in the MYC-SKP2-CDKN1B axis. Furthermore, nuclear localization of CDKN1B was enhanced in SUZ12 KD cells. Analysis of human glioblastoma samples yielded increased expression of EZH2 and MYC along with reduced CDKN1B compared to normal human brain tissue. Our findings suggest a novel role for SUZ12 in cell proliferation through post-translational regulation of CDKN1B in glioblastoma.

Identification of Family with Sequence Similarity 110 Member C (FAM110C) as a Candidate Diagnostic and Prognostic Biomarker for Glioma.

Gliomas are the most frequent and dangerous primary cerebral tumors. Therefore, there is a need to develop molecular targets for the diagnosis and treatment for glioma. In September 2020, we retrieved the expression matrix of glioblastoma (GBM) sufferers and pertinent clinical data from the TCGA (The Cancer Genome Atlas) database. Prognostic differences between various families with sequence similarity 110 member C (FAM110C) expression groups were assessed by Kaplan-Meier with log-rank test. The R platform get used to assess the accuracy of FAM110C delivery in predicting the prognosis of PDAC using a time-dependent receptor operating characteristic (ROC) curve. The delivery level of FAM110C was determined by qRT-PCR and western blot. Gene set enrichment investigated possible mechanisms between different FAM110C expression groups in GBM (GSEA). The impact of FAM110C on glioma cell movement was discovered using migration test. The drug's gene-targeting impact was validated by the CCK8 test. A total of 173 GBM samples were obtained from the TCGA database, with 148 including information on IDH1 mutations and 151 containing information on overall survival. The mRNA expression level of FAM110C was greater in wild-type GBM, according to qRT-PCR data. The connection between FAM110C expression and Hallmark, GO, and KEGG pathway gene sets was investigated using GSEA software. We used migration test to assess the impact of FAM110C on glioma motility in order to confirm the findings of the GSEA analysis. FAM110C might get used as a possible diagnostic and prognostic biomarker for wild-type GBM, and its inhibition could be used to prevention and treatment wild-type GBM.

Single-cell and bulk sequencing analyses reveal the immune suppressive role of PTPN6 in glioblastoma.

Glioblastoma (GBM) is a highly malignant brain cancer with a poor prognosis despite standard treatments. This investigation aimed to explore the feasibility of PTPN6 to combat GBM with immunotherapy. Our study employed a comprehensive analysis of publicly available datasets and functional experiments to assess PTPN6 gene expression, prognostic value, and related immune characteristics in glioma. We evaluated the influence of PTPN6 expression on CD8+ T cell exhaustion, immune suppression, and tumor growth in human GBM samples and mouse models. Our findings demonstrated that PTPN6 overexpression played an oncogenic role in GBM and was associated with advanced tumor grades and unfavorable clinical outcomes. In human GBM samples, PTPN6 upregulation showed a strong association with immunosuppressive formation and CD8+ T cell dysfunction, whereas, in mice, it hindered CD8+ T cell infiltration. Moreover, PTPN6 facilitated cell cycle progression, inhibited apoptosis, and promoted glioma cell proliferation, tumor growth, and colony formation in mice. The outcomes of our study indicate that PTPN6 is a promising immunotherapeutic target for the treatment of GBM. Inhibition of PTPN6 could enhance CD8+ T cell infiltration and improve antitumor immune response, thus leading to better clinical outcomes for GBM patients.

Exploration of the radiosensitivity-related prognostic risk signature in patients with glioma: evidence from microarray data

BackgroundGene expression signatures can be used as prognostic biomarkers in various types of cancers. We aim to develop a gene signature for predicting the response to radiotherapy in glioma patients.MethodsRadio-sensitive and radio-resistant glioma cell lines (M059J and M059K) were subjected to microarray analysis to screen for differentially expressed mRNAs. Additionally, we obtained 169 glioblastomas (GBM) samples and 5 normal samples from The Cancer Genome Atlas (TCGA) database, as well as 80 GBM samples and 4 normal samples from the GSE7696 set. The “DESeq2” R package was employed to identify differentially expressed genes (DEGs) between the normal brain samples and GBM samples. Combining the prognostic-related molecules identified from the TCGA, we developed a radiosensitivity-related prognostic risk signature (RRPRS) in the training set, which includes 152 patients with glioblastoma. Subsequently, we validated the reliability of the RRPRS in a validation set containing 616 patients with glioma from the TCGA database, as well as an internal validation set consisting of 31 glioblastoma patients from the Nanfang Hospital, Southern Medical University.ResultsBased on the microarray and LASSO COX regression analysis, we developed a nine-gene radiosensitivity-related prognostic risk signature. Patients with glioma were divided into high- or low-risk groups based on the median risk score. The Kaplan–Meier survival analysis showed that the progression-free survival (PFS) of the high-risk group was significantly shorter. The signature accurately predicted PFS as assessed by time-dependent receiver operating characteristic curve (ROC) analyses. Stratified analysis demonstrated that the signature is specific to predict the outcome of patients who were treated using radiotherapy. Univariate and multivariate Cox regression analysis revealed that the predictor was an independent predictor for the prognosis of patients with glioma. The prognostic nomograms accompanied by calibration curves displayed the 1-, 2-, and 3-year PFS and OS in patients with glioma.ConclusionOur study established a new nine-gene radiosensitivity-related prognostic risk signature that can predict the prognosis of patients with glioma who received radiotherapy. The nomogram showed great potential to predict the prognosis of patients with glioma treated using radiotherapy.

P02.06.B DIELECTRIC PROPERTIES OF INTRACRANIAL TUMORS

Abstract BACKGROUND Recently, tumor treating fields (TTFields) were established for the treatment of newly diagnosed glioblastoma (GBM). One of the most crucial parameters defining the treatment efficacy of TTFields is the electric field intensity, which depends on the dielectric properties of the tumor tissue. In this study we determined the dieclectric properties of brain tumors by analyzing resected tissue following a fast acquisition protocol. In GBM patients, histological analysis for cellularity, vascularization and myelin content was performed. In addition, sensitivity analyses for specific parameters (tissue hydration, temperature, and saline irrigation) were conducted. MATERIAL AND METHODS A cohort of 130 patients with tumors of different histology and malignancy grade have been recruited (meningioma: n=36; brain metastases n=29; low grade glioma n=7; anaplastic glioma = 12; other= 7; glioblastoma n=39, all treated with TTFields). Tissue samples were placed into a cylindrical cell with a known diameter. The impedance was recorded at frequencies 20Hz-1MHz using a software specifically developed for this study. The measured impedance was translated into dielectric properties of the sample (conductivity and relative permittivity) based on the parallel plate model. Myelin, which is the most powerful electric isolator in the brain, was assessed in GBM samples by luxol fast blue staining, and quantified in a three-tier scale. To assess the impact of tissue conditions on the measurements, probes were warmed to 35 degree Celsius, dehydrated or irrigated with 0.9% saline solution. RESULTS We found significant differences between the conductivity of different types of tumors with meningiomas showing the lowest and GBM tissue exhibiting the highest conductivity values. GBM samples with very high median conductivity values displayed a consistently lower myelin content. In addition, GBM patients with particularly high conductivity had a significantly shorter overall survival (log rank analysis, p = 0.024). While tissue temperature had no detectable effects on the dielectric properties in GBM, saline irrigation tissue hydration significantly affected the results. CONCLUSION The dielectric properties of intracranial tumors depend on histological class and malignancy grade. GBM patients with high conductivity values showed a significantly poorer prognosis, indicating this parameter as potential marker for TTF efficacy.

P14.04.B CIRCULATING TUMOR CELLS AS A NON-INVASIVE GLIOBLASTOMA DIAGNOSTIC AND PROGNOSTIC TOOL

Abstract BACKGROUND Circulating tumor cells (CTCs) detection in the blood of glioblastoma (GBM) patients can facilitate the assessment of tumoral information without the need for invasive approaches. Our aim is to use GBM CTCs to obtain knowledge about the tumor and improve the current GBM diagnosis and prognosis. MATERIAL AND METHODS We collected primary, matched recurrent GBM and healthy controls blood samples. CTCs were enriched from whole blood controls with Parsortix Cell Separation System and analysed on DEPArray system. CTCs were recognized by immunofluorescence with GFAP.The CTCs have been counted for all the samples. Copy Number Aberrations (CNAs) analysis was performed on CTCs. To compare CTCs genetic background with the same patient’s primary and recurrence tissues, whole exome sequencing (WES) was realized on tumor tissues. Recurrence of GBM was diagnosed according to the RANO criteria +/-path proven second surgery +/- FET PET imaging proven. We assessed tumor volume of contrast enhanced (CE) (as a surrogate marker of BBB damage) and nCE at the time of the CTCs collection by seminautomatic segmentation on ITKsnap software. RESULTS We found CTCs in GBM patients newly diagnosed at the time of first surgery (86%) but also at recurrence (100 %). Cell counting demonstrated in both primary and recurrent GBM patients’ blood a statistically significantly higher number of CTCs compared to healthy controls (average of cells number 15.8 vs 2.3). Interestingly, recurrent GBM patients showed more CTCs than primary GBM patients (average 9 vs 22.7). CNAs analysis confirmed the tumor status of the selected CTCs. Some of GBM classical mutations (in HTERT promoter, BRAF, PTEN, PI3KCA and EGFR genes) have been searched both in the tumor tissues and in the CTCs. HTERT was found mutated in CTCs of one patient’s tumor carrying mutant HTERT and wild type IDH1/2. We also included three IDH1 mutant glioma patients and one BRAFV600E mutant GBM patient in order to track these in CTCs. We did not find BRAF mutations in CTCs isolated from one recurrent BRAFV600E GBM patient with a very low allele frequency in the recurrent tumor (<1%). CONCLUSION We found CTCs in GBM patients newly diagnosed and at recurrence. Several factors may affect CTCs release such as tumor volume, CE and the CTCs genetic BACKGROUND , which seems to be heterogeneous. CTCs detection by our methodology could be helpful in the clinical setting for diagnostic, prognostic and theranostic purposes and for patients monitoring. Prospective study is ongoing in order to assess CTCs variations on and after therapy. This study, first investigated the genetic heterogeneity of CTCs released in the blood flow from the parental tumor.

JS04.7.A ANALYSIS OF RNA CLASSIFIES NEWLY DIAGNOSED GLIOBLASTOMA PATIENTS AND IDENTIFIES PATIENTS VULNERABLE TO TARGETED METABOLIC THERAPIES

Abstract BACKGROUND Recent computational analysis of glioblastoma (GBM) single-cell RNA-sequencing data and bulk tumors identified four stable cellular states that embody metabolic (mitochondrial and glycolytic/plurimetabolic) and developmental (neuronal and proliferative/progenitor) attributes and generated a new GBM classification. Interestingly, the mitochondrial subtype depends on oxidative phosphorylation (OXPHOS), stratifies patients with specific metabolic pharmacologic vulnerabilities. Here, we set up and optimized RNA Sequencing of frozen and FFPE samples of GBM to apply the new proposed classification to a retrospective cohort of IDH wild type GBM patients. METHODS Tumor samples from IDH wild-type (wt) GBM patients diagnosed in Pitiè-Salpétrière Hospital from 2007 and 2019 and treated by first line standard radio-chemotherapy were analyzed by 3’ mRNA NGS. RESULTS Two hundreds thirty-two newly diagnosed (232) GBM IDH patients were analyzed. Median age was 60 years old (yo) (range 31-87 yo), median KPS at diagnosis was 80, Male/female ratio 1.5, pMGMT resulted hypermethylated in 48 out of 115 tested samples (42%). RNA analysis classified patients into molecular subgroups as it follows: 60 OXPHOS+, 48 Neuronal, 55 Proliferative and 58 Glycolytic; 11 unclassifiable. A subgroup of 28 patients was analyzed by RNA Sequencing from both FFPE and frozen tumors with concordant results in 25 patients (89%). Since F3T3 gene fusions correlate with hyperexpression of mitochondrial markers, we separately analyzed a subgroup of 20 IDHwt F3T3+ GBM patients. We found that F3T3+ are most frequently OXPHOS+ (50%) but not exclusively. Survival analysis shows significant longer overall survival (OS) of OXPHOS+F3T3- patients (22.9 months) compared to OXPHOS-F3T3-(18.4 months), (p=0.045) [including Neuronal patients (OS 18.4 months), Glycolytic (18.2 m), Proliferative 18.6 m)]. PFS after first line radio-chemotherapy did not differ significantly (p=0.11) between subgroups. Among F3T3+ patients, OXPHOS+ were long survivors with a median OS of 40.1 months while Glycolytic, Neuronal and Proliferative F3T3+ patients showed a worse prognosis similar to F3T3- patients (median OS 17.3 months; p=0.01). CONCLUSIONS In conclusions the RNA expression analysis classifies GBM patients from frozen and FFPE samples in metabolic subgroups. We identified OXPHOS+ GBM patients with more indolent clinical behavior compared to Glycolytic and other subgroups and with specific vulnerabilities to metabolic manipulations, as in the case of OXPHOS+ patients that could benefit from targeted metabolic therapies. RNA sequencing of FFPE is feasible in the setting of a prospectively clinical trial, aiming to select OXPHOS+ patients among newly diagnosed GBM patients, eligible for mitochondrial inhibitors add-on to first-line standard radio-chemotherapy (Optimum trial, NCT04945148).

P10.01.A A PATIENT-DERIVEDEX VIVO ORGANOID MODEL ALLOWS EVALUATION OF TUMOR TREATING FIELDS (TTFIELDS) EFFECTS ON GLIOBLASTOMA, ANAPLASTIC EPENDYMOMA AND OLIGODENDROGLIOMA SAMPLES

Abstract BACKGROUND The use of TTFields, which are alternating electric fields of low intensity (1-3 V/cm) and intermediate frequency (100-500 kHz), has shown its clinical efficacy in the treatment of newly diagnosed glioblastoma (GBM) patients at 200 kHz. Nevertheless, more knowledge is required for the prediction of individual tumors’ responsiveness to treatment. Recently, we developed patient-derived three-dimensional GBM tissue culture models to screen patient tumors for TTFields treatment susceptibility. Here, we extended these models to brain tumors beyond GBM, namely anaplastic ependymoma (AE) and oligodendroglioma. We cultured tumor-organoids as microtumors on murine organotypic hippocampal slice cultures (OHSCs) and subjected them to TTFields at 200 kHz. MATERIAL AND METHODS We prepared OHSCs by slicing the brains of mice 5-8 days postpartum to a thickness of 350 µm using a vibratome and culturing them for 14 days. We obtained organoids from freshly resected tumor tissue from three GBM, one AE, and one oligodendroglioma patient, each. The organoids were cultured for 14 days as well and then stained with carboxyfluorescein succinimidyl ester. Next, we placed the organoids onto the OHSCs and administered TTFields at 200 kHz and 1.5 V/cm for 72 h using the inovitro™ laboratory research system. We quantified microtumor growth by fluorescence imaging. RESULTS The three tumor entities, in particular organoids from GBM, AE and oligodendroglioma, grew as microtumors after being placed onto OHSCs. TTFields treatment at 200 kHz led to significantly decreased microtumor size of the three analysed GBM samples as well as the AE sample. However, the oligodendroglioma microtumor size was not significantly reduced. CONCLUSION Based on clinical experience, we expected that TTFields lead to a reduction in microtumor size. However, to our knowledge, we were the first to apply TTFields to patient-derived AE and oligodendroglioma samples. The lack of TTFields-effects on the latter might be due to different inter-patient sensitivity or suboptimal TTFields frequency. Nevertheless, our work is proof of principle that an organoid-based patient-derived 3D tissue culture model could be used to evaluate effects of TTFields on different brain tumor entities.

Development and validation a prognostic model based on natural killer T cells marker genes for predicting prognosis and characterizing immune status in glioblastoma through integrated analysis of single-cell and bulk RNA sequencing.

Glioblastoma (GBM) is an aggressive and unstoppable malignancy. Natural killer T (NKT) cells, characterized by specific markers, play pivotal roles in many tumor-associated pathophysiological processes. Therefore, investigating the functions and complex interactions of NKT cells is great interest for exploring GBM. We acquired a single-cell RNA-sequencing (scRNA-seq) dataset of GBM from Gene Expression Omnibus (GEO) database. The weighted correlation network analysis (WGCNA) was employed to further screen genes subpopulations. Subsequently, we integrated the GBM cohorts from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases to describe different subtypes by consensus clustering and developed a prognostic model by least absolute selection and shrinkage operator (LASSO) and multivariate Cox regression analysis. We further investigated differences in survival rates and clinical characteristics among different risk groups. Furthermore, a nomogram was developed by combining riskscore with the clinical characteristics. We investigated the abundance of immune cells in the tumor microenvironment (TME) by CIBERSORT and single sample gene set enrichment analysis (ssGSEA) algorithms. Immunotherapy efficacy assessment was done with the assistance of Tumor Immune Dysfunction and Exclusion (TIDE) and The Cancer Immunome Atlas (TCIA) databases. Real-time quantitative polymerase chain reaction (RT-qPCR) experiments and immunohistochemical profiles of tissues were utilized to validate model genes. We identified 945 NKT cells marker genes from scRNA-seq data. Through further screening, 107 genes were accurately identified, of which 15 were significantly correlated with prognosis. We distinguished GBM samples into two distinct subtypes and successfully developed a robust prognostic prediction model. Survival analysis indicated that high expression of NKT cell marker genes was significantly associated with poor prognosis in GBM patients. Riskscore can be used as an independent prognostic factor. The nomogram was demonstrated remarkable utility in aiding clinical decision making. Tumor immune microenvironment analysis revealed significant differences of immune infiltration characteristics between different risk groups. In addition, the expression levels of immune checkpoint-associated genes were consistently elevated in the high-risk group, suggesting more prominent immune escape but also a stronger response to immune checkpoint inhibitors. By integrating scRNA-seq and bulk RNA-seq data analysis, we successfully developed a prognostic prediction model that incorporates two pivotal NKT cells marker genes, namely, CD44 and TNFSF14. This model has exhibited outstanding performance in assessing the prognosis of GBM patients. Furthermore, we conducted a preliminary investigation into the immune microenvironment across various risk groups that contributes to uncover promising immunotherapeutic targets specific to GBM.

A three-stage eccDNA based molecular profiling significantly improves the identification, prognosis assessment and recurrence prediction accuracy in patients with glioma

Glioblastoma (GBM) progression is influenced by intratumoral heterogeneity. Emerging evidence has emphasized the pivotal role of extrachromosomal circular DNA (eccDNA) in accelerating tumor heterogeneity, particularly in GBM. However, the eccDNA landscape of GBM has not yet been elucidated. In this study, we first identified the eccDNA profiles in GBM and adjacent tissues using circle- and RNA-sequencing data from the same samples. A three-stage model was established based on eccDNA-carried genes that exhibited consistent upregulation and downregulation trends at the mRNA level. Combinations of machine learning algorithms and stacked ensemble models were used to improve the performance and robustness of the three-stage model. In stage 1, a total of 113 combinations of machine learning algorithms were constructed and validated in multiple external cohorts to accurately distinguish between low-grade glioma (LGG) and GBM in patients with glioma. The model with the highest area under the curve (AUC) across all cohorts was selected for interpretability analysis. In stage 2, a total of 101 combinations of machine learning algorithms were established and validated for prognostic prediction in patients with glioma. This prognostic model performed well in multiple glioma cohorts. Recurrent GBM is invariably associated with aggressive and refractory disease. Therefore, accurate prediction of recurrence risk is crucial for developing individualized treatment strategies, monitoring patient status, and improving clinical management. In stage 3, a large-scale GBM cohort (including primary and recurrent GBM samples) was used to fit the GBM recurrence prediction model. Multiple machine learning and stacked ensemble models were fitted to select the model with the best performance. Finally, a web tool was developed to facilitate the clinical application of the three-stage model.

Prognostic Analysis of a Hypoxia-Associated lncRNA Signature in Glioblastoma and its Pan-Cancer Landscape.

Hypoxia is an important clinical feature of glioblastoma (GBM), which regulates a variety of tumor processes and is inseparable from radiotherapy. Accumulating evidence suggests that long noncoding RNAs (lncRNAs) are strongly associated with survival outcomes in GBM patients and modulate hypoxia-induced tumor processes. Therefore, the aim of this study was to establish a hypoxia-associated lncRNAs (HALs) prognostic model to predict survival outcomes in GBM patients. LncRNAs in GBM samples were extracted from The Cancer Genome Atlas database. Hypoxia-related genes were downloaded from the Molecular Signature Database. Co-expression analysis of differentially expressed lncRNAs and hypoxia-related genes in GBM samples was performed to determine HALs. Six optimal lncRNAs were selected for building HALs models by univariate Cox regression analysis. The prediction model has a good predictive effect on the prognosis of GBM patients. Meanwhile, LINC00957 among the six lncRNAs was selected and subjected to pan-cancer landscape analysis. Taken together, our findings suggest that the HALs assessment model can be used to predict the prognosis of GBM patients. In addition, LINC00957 included in the model may be a useful target to study the mechanism of cancer development and design individualized treatment strategies.

Identification of cytokine-predominant immunosuppressive class and prognostic risk signatures in glioma.

The advent of immune checkpoint blockade (ICB) therapies this year has changed the way glioblastoma (GBM) is treated. Meanwhile, some patients with strong PD-L1 expression remain immune checkpoint resistant. To better understand the molecular processes that influence the immune environment, there is an urgent need to characterize the immunosuppressive tumor microenvironment and identify biomarkers to predict patient survival outcomes. Our study analyzed RNA-sequencing data from 178 GBM samples. Their unique gene expression patterns in the tumor microenvironment were analyzed by an unsupervised clustering algorithm. Through these expression patterns, a panel of T-cell exhaustion signatures, immunosuppressive cells, and clinical features correlates with immunotherapy response. The presence or absence of immune status and prognostic signatures was then validated with the test dataset. 38.2% of GBM patients showed increased expression of anti-inflammatory cytokines, significant enrichment of T cell exhaustion signals, higher proportion of immunosuppressive cells (macrophages and CD4 regulatory T cells) and nine inhibitory checkpoints (CTLA4, PDCD1, LAG3, BTLA, TIGIT, HAVCR2, IDO1, SIGLEC7, and VISTA). The immunodepleted class (IDC) was used to classify these immunocompromised individuals. Despite the high density of tumor-infiltrating lymphocytes shown by IDC, such patients have a poor prognosis. Although PD-L1 was highly expressed in IDC, it suggested that there might be ICB resistance. There are many IDC predictive signatures to discover. PD-1 is strongly expressed in a novel immunosuppressive class of GBM, but this cluster may be resistant to ICB therapy. A comprehensive description of this drug-resistant tumor microenvironment could provide new insights into drug resistance mechanisms and improved immunotherapy techniques.

Development of an MCL-1-related prognostic signature and inhibitors screening for glioblastoma.

Introduction: The effect of the conventional treatment methods of glioblastoma (GBM) is poor and the prognosis of patients is poor. The expression of MCL-1 in GBM is significantly increased, which shows a high application value in targeted therapy. In this study, we predicted the prognosis of glioblastoma patients, and therefore constructed MCL-1 related prognostic signature (MPS) and the development of MCL-1 small molecule inhibitors. Methods: In this study, RNA-seq and clinical data of 168 GBM samples were obtained from the TCGA website, and immunological analysis, differential gene expression analysis and functional enrichment analysis were performed. Subsequently, MCL-1-associated prognostic signature (MPS) was constructed and validated by LASSO Cox analysis, and a nomogram was constructed to predict the prognosis of patients. Finally, the 17931 small molecules downloaded from the ZINC15 database were screened by LibDock, ADME, TOPKAT and CDOCKER modules and molecular dynamics simulation in Discovery Studio2019 software, and two safer and more effective small molecule inhibitors were finally selected. Results: Immunological analysis showed immunosuppression in the MCL1_H group, and treatment with immune checkpoint inhibitors had a positive effect. Differential expression gene analysis identified 449 differentially expressed genes. Build and validate MPS using LASSO Cox analysis. Use the TSHR HIST3H2A, ARGE OSMR, ARHGEF25 build risk score, proved that low risk group of patients prognosis is better. Univariate and multivariate analysis proved that risk could be used as an independent predictor of patient prognosis. Construct a nomogram to predict the survival probability of patients at 1,2,3 years. Using a series of computer-aided techniques, two more reasonable lead compounds ZINC000013374322 and ZINC000001090002 were virtually selected. These compounds have potential inhibitory effects on MCL-1 and provide a basis for the design and further development of MCL-1 specific small molecule inhibitors. Discussion: This study analyzed the effect of MCL-1 on the prognosis of glioblastoma patients from the perspective of immunology, constructed a new prognostic model to evaluate the survival rate of patients, and further screened 2 MCL-1 small molecule inhibitors, which provides new ideas for the treatment and prognosis of glioblastoma.

ENTPD1 (CD39) and NT5E (CD73) expression in human glioblastoma: an in silico analysis.

Glioblastoma (GB) is the most common primary brain tumor in adults and carries a dismal prognosis, despite the best available treatment. The 2021 WHO Classification of CNS tumors incorporated molecular profiling to better define the characteristics and prognosis of tumor types and subtypes. These recent advances in diagnosis have not yet resulted in breakthrough therapies capable of shifting the treatment paradigm. NT5E/CD73 is a cell surface enzyme that participates in a complex purinergic pathway in synergy with ENTPD1/CD39 producing extracellular adenosine (ADO) from ATP. ADO promotes tumor progression by inducing immunosuppression, stimulating adhesion, invasion, and angiogenesis. In this study, we performed an in silico analysis of 156 human glioblastoma samples in an unexplored public database to investigate the transcriptional levels of NT5E and ENTPD1. The analysis revealed a significant increase in transcription levels of the genes under study in GB samples versus non-tumor brain tissue samples, in concordance with previous studies. High transcriptional levels of NT5E or ENTPD1 were independently related to a decrease in overall survival (p = 5.4e-04; 1.1e-05), irrespective of the IDH mutation status. NT5E transcriptional levels were significantly higher in GB IDH wild-type patients compared to GB IDH-mutant; however, ENTPD1 levels showed no significant difference, p ≤ 0.001. This in silico study indicates the need for a deeper understanding of the purinergic pathway relation to GB development, also inspiring future population studies that could explore ENTPD1 and NT5E not only as prognostic markers but also as potential therapeutic targets.

CDH1 hypermethylation: a potential molecular pathway for invasiveness in glioblastoma.

Glioblastoma is the most aggressive central nervous system primary tumor. Prognosis is poor, mainly due to the malignant characteristics of the tumor, such as high cell proliferation and invasiveness. CDH1 hypermethylation is linked to the invasive potential in various cancer types, but its importance is still unknown in glioblastoma. In this context, the methylation status of CDH1 was analyzed using MSP-PCR (Methylation-specific Polymerase Chain Reaction) in glioblastoma (n = 34) and normal glial tissue samples (n = 11). CDH1 hypermethylation was found in 39.4% (13/34) of the tumor samples and none of the normal glial tissue, suggesting a relation between CDH1 hypermethylation and glioblastoma ( P = 0.0195). Finally, this study showed unprecedented information that could contribute to clarifying the molecular pathways involved in the invasiveness and aggressiveness of this type of cancer.

Increased expression of ubiquitin C-terminal hydrolase L1 in astrocytomas of ascending grades.

The ubiquitin-proteasome pathway controls the monitoring and degradation of important proteins and is involved in several cellular processes, such as development, differentiation, and transcriptional regulation. Recent evidence has shown that ubiquitin carboxy-terminal hydrolase-L1 (UCH-L1), a member of the deubiquitinating enzyme family that removes ubiquitin from protein substrates, is overexpressed in many types of cancer. This study thus examined the expression of UCH-L1 in human astrocytoma tissues. Formalin-fixed, paraffin-embedded astrocytoma samples were obtained from 40 patients, after which histopathological examination, typing, and grading were performed. The study group included 10 histologically normal brain tissues, which served as the control group, and 10 WHO grade II, 10 WHO grade III, and 10 WHO grade IV (glioblastoma) samples. Normal brain tissue samples were obtained from the histologically normal, non-tumoral portion of the pathology specimens. UCH-L1 expression was evaluated using quantitative reverse transcription-polymerase chain reaction and immunohistochemistry. Astrocytoma tissues exhibited higher UCH-L1 expression compared to the control group. UCH-L1 overexpression increased significantly together with the increase in astrocytoma grades (from II to IV). UCH-L1 could be a good diagnostic and therapeutic marker for determining astrocytoma development and progression.

Parathyroid Hormone-Related Protein Promotes the Proliferation of Patient-Derived Glioblastoma Stem Cells via Activating cAMP/PKA Signaling Pathway.

Glioblastoma (GBM) is an aggressive primary brain tumor characterized by its heterogeneity and high recurrence and lethality rates. Glioblastoma stem cells (GSCs) play a crucial role in therapy resistance and tumor recurrence. Therefore, targeting GSCs is a key objective in developing effective treatments for GBM. The role of Parathyroid hormone-related peptide (PTHrP) in GBM and its impact on GSCs remains unclear. This study aimed to investigate the effect of PTHrP on GSCs and its potential as a therapeutic target for GBM. Using the Cancer Genome Atlas (TCGA) database, we found higher expression of PTHrP in GBM, which correlated inversely with survival. GSCs were established from three human GBM samples obtained after surgical resection. Exposure to recombinant human PTHrP protein (rPTHrP) at different concentrations significantly enhanced GSCs viability. Knockdown of PTHrP using target-specific siRNA (siPTHrP) inhibited tumorsphere formation and reduced the number of BrdU-positive cells. In an orthotopic xenograft mouse model, suppression of PTHrP expression led to significant inhibition of tumor growth. The addition of rPTHrP in the growth medium counteracted the antiproliferative effect of siPTHrP. Further investigation revealed that PTHrP increased cAMP concentration and activated the PKA signaling pathway. Treatment with forskolin, an adenylyl cyclase activator, nullified the antiproliferative effect of siPTHrP. Our findings demonstrate that PTHrP promotes the proliferation of patient-derived GSCs by activating the cAMP/PKA signaling pathway. These results uncover a novel role for PTHrP and suggest its potential as a therapeutic target for GBM treatment.

TGF-β based risk model to predict the prognosis and immune features in glioblastoma.

Transforming growth factor-β (TGF-β) is a multifunctional cytokine with an important role in tissue development and tumorigenesis. TGF-β can inhibit the function of many immune cells, prevent T cells from penetrating into the tumor center, so that the tumor cells escape from immune surveillance and lead to low sensitivity to immunotherapy. However, its potential roles in predicting clinical prognosis and tumor microenvironment (TME) immune features need to be deeply investigated in glioblastoma (GBM). The TCGA-GBM dataset was obtained from the Cancer Genome Atlas, and the validation dataset was downloaded from Gene Expression Omnibus. Firstly, differentially expressed TGF-β genes (DEGs) were screened between GBM and normal samples. Then, univariate and multivariate Cox analyses were used to identify prognostic genes and develop the TGF-β risk model. Subsequently, the roles of TGF-β risk score in predicting clinical prognosis and immune characteristics were investigated. The TGF-β risk score signature with an independent prognostic value was successfully developed. The TGF-β risk score was positively correlated with the infiltration levels of tumor-infiltrating immune cells, and the activities of anticancer immunity steps. In addition, the TGF-β risk score was positively related to the expression of immune checkpoints. Besides, the high score indicated higher sensitivity to immune checkpoint inhibitors. We first developed and validated a TGF-β risk signature that could predict the clinical prognosis and TME immune features for GBM. In addition, the TGF-β signature could guide a more personalized therapeutic approach for GBM.