Prognosis Of Glioblastoma Patients Research Articles

Unveiling the enigma of the blood-brain barrier in glioblastoma: current advances from preclinical and clinical studies.

Glioblastoma (GBM), the most prevalent primary brain malignancy in adults, poses significant challenges in terms of treatment. Current therapeutic strategies for GBM patients involve maximal safe resection, followed by radiotherapy with concurrent and adjuvant temozolomide. However, despite this multimodal approach for GBM, the prognosis of GBM patients remains dismal because of their inherent primary and secondary resistances to treatments. Several molecular and cellular mechanisms, including the presence of the blood-brain barrier (BBB), contribute to these resistances. The BBB, comprising multiple layers surrounding brain vessels, acts as a barrier limiting effective drug delivery to the brain. Invasive and noninvasive tools to deliver drugs and pharmaceutical formulations locally or systemically are continuously evolving to overcome the BBB in GBM toward improving drug bioavailability in the brain and reducing systemic toxicities. Preliminary studies utilizing these approaches have demonstrated promising results in terms of safety and signals of efficacy during early-phase clinical trials. However, further work through additional clinical trials is necessary to evaluate the potential clinical benefits for GBM patients.

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 qPCR technology for direct quantification of methylation in untreated DNA

DNA methylation is important for gene expression and alterations in DNA methylation are involved in the development and progression of cancer and other major diseases. Analysis of DNA methylation patterns has until now been dependent on either a chemical or an enzymatic pre-treatment, which are both time consuming procedures and potentially biased due to incomplete treatment. We present a qPCR technology, EpiDirect®, that allows for direct PCR quantification of DNA methylations using untreated DNA. EpiDirect® is based on the ability of Intercalating Nucleic Acids (INA®) to differentiate between methylated and unmethylated cytosines in a special primer design. With this technology, we develop an assay to analyze the methylation status of a region of the MGMT promoter used in treatment selection and prognosis of glioblastoma patients. We compare the assay to two bisulfite-relying, methyl-specific PCR assays in a study involving 42 brain tumor FFPE samples, revealing high sensitivity, specificity, and the clinical utility of the method.

PLK3 promotes the proneural-mesenchymal transition in glioblastoma via transcriptional regulation of C5AR1.

Accumulating evidence suggests that polo-like kinase 3 (PLK3) plays an essential role in tumor cells and induces cell proliferation and may have implications for the prognosis of various cancers. We sought to define the role of PLK3-dependent proneural-mesenchymal transition (PMT) in the glioblastoma (GBM) therapy. We analyzed the expression data for PLK3 by using the TCGA database. PLK3 expression in GBM cell lines was determined by qRT-PCR and Western blotting. PLK3 levels were modulated using Lentivirus infection, and the effects on symptoms, tumor volume, and survival in mice intracranial xenograft models were determined. Irradiation (IR) was performed to induce PMT. PLK3 expression was significantly elevated in mesenchymal subtype GBM and promoted tumor proliferation in GBM. Additionally enriched PLK3 expression could be associated with poor prognosis in GBM patients compared with those who have lower PLK3 expression. Mechanically, PLK3-dependent PMT induced radioresistance in GBM cells via transcriptional regulation of complement C5a receptor 1 (C5AR1). In therapeutic experiments conducted in vitro, targeting PLK3 by using small molecule inhibitor decreased tumor growth and radioresistance of GBM cells both in vitro and in vivo. PLK3-C5AR1 axis induced PMT thus enhanced radioresistance in GBM and could become a novel potential therapeutic target for GBM.

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.

Circular RNA encoded MET variant promotes glioblastoma tumorigenesis.

Activated by its single ligand, hepatocyte growth factor (HGF), the receptor tyrosine kinase MET is pivotal in promoting glioblastoma (GBM) stem cell self-renewal, invasiveness and tumorigenicity. Nevertheless, HGF/MET-targeted therapy has shown limited clinical benefits in GBM patients, suggesting hidden mechanisms of MET signalling in GBM. Here, we show that circular MET RNA (circMET) encodes a 404-amino-acid MET variant (MET404) facilitated by the N6-methyladenosine (m6A) reader YTHDF2. Genetic ablation of circMET inhibits MET404 expression in mice and attenuates MET signalling. Conversely, MET404 knock-in (KI) plus P53 knock-out (KO) in mouse astrocytes initiates GBM tumorigenesis and shortens the overall survival. MET404 directly interacts with the MET β subunit and forms a constitutively activated MET receptor whose activity does not require HGF stimulation. High MET404 expression predicts poor prognosis in GBM patients, indicating its clinical relevance. Targeting MET404 through a neutralizing antibody or genetic ablation reduces GBM tumorigenicity in vitro and in vivo, and combinatorial benefits are obtained with the addition of a traditional MET inhibitor. Overall, we identify a MET variant that promotes GBM tumorigenicity, offering a potential therapeutic strategy for GBM patients, especially those with MET hyperactivation.

Mechanistic insights into super-enhancer-driven genes as prognostic signatures in patients with glioblastoma.

Glioblastoma (GBM) is one of the most common malignant brain tumors in adults and is characterized by high aggressiveness and rapid progression, poor treatment, high recurrence rate, and poor prognosis. Although super-enhancer (SE)-driven genes haven been recognized as prognostic markers for several cancers, whether it can be served as effective prognostic markers for patients with GBM has not been evaluated. We first combined histone modification data with transcriptome data to identify SE-driven genes associated with prognosis in patients with GBM. Second, we developed a SE-driven differentially expressed genes (SEDEGs) risk score prognostic model by univariate Cox analysis, KM survival analysis, multivariate Cox analysis and least absolute shrinkage and selection operator (LASSO) regression. Its reliability in predicting was verified by two external data sets. Third, through mutation analysis, immune infiltration, we explored the molecular mechanisms of prognostic genes. Next, Genomics of Drug Sensitivity in Cancer (GDSC) and the Connectivity Map (cMap) database were employed to assess different sensitivities to chemotherapeutic agents and small-molecule drug candidates between high- and low-risk patients. Finally, SEanalysis database was chosen to identify SE-driven transcription factors (TFs) regulating prognostic markers which will reveal a potential SE-driven transcriptional regulatory network. First, we developed a 11-gene risk score prognostic model (NCF2, MTHFS, DUSP6, G6PC3, HOXB2, EN2, DLEU1, LBH, ZEB1-AS1, LINC01265, and AGAP2-AS1) selected from 1,154 SEDEGs, which is not only an independent prognostic factor for patients, but also can effectively predict the survival rate of patients. The model can effectively predict 1-, 2- and 3-year survival of patients and was validated in external Chinese Glioma Genome Atlas (CGGA) and Gene Expression Omnibus (GEO) datasets. Second, the risk score was positively correlated with the infiltration of regulatory T cell, CD4 memory activated T cell, activated NK cell, neutrophil, resting mast cell, M0 macrophage, and memory B cell. Third, we found that high-risk patients showed higher sensitivity than low-risk patients to both 27 chemotherapeutic agents and 4 small-molecule drug candidates which might benefit further precision therapy for GBM patients. Finally, 13 potential SE-driven TFs imply how SE regulates GBM patient's prognosis. The SEDEG risk model not only helps to elucidate the impact of SEs on the course of GBM, but also provides a bright future for prognosis determination and choice of treatment for GBM patients.

Therapeutic Hypothermia in Treating Glioblastoma: A Review.

Glioblastoma (GBM) is the most commonly occurring of all malignant central nervous system (CNS) tumors in adults. Considering the low median survival of only ∼15 months and poor prognosis in GBM patients, despite surgical resection with adjuvant radiation and chemotherapy, it is vital to seek brand new and innovative treatment in combination with already existing methods. Hypothermia participates in many metabolic pathways, inflammatory responses, and apoptotic processes, while also promoting the integrity of neurons. Following the successful application of therapeutic hypothermia across a spectrum of disorders such as traumatic CNS injury, cardiac arrest, and epilepsy, several clinical trials have set to evaluate the potency of hypothermia in treating a variety of cancers, including breast and ovaries cancer. In regard to primary neoplasms and more specifically, GBM, hypothermia has recently shown promising results as an auxiliary treatment, reinforcing chemotherapy's efficacy. In this review, we discuss the recent advances in utilizing hypothermia as treatment for GBM and other cancers.

The moonlighting function of glycolytic enzyme enolase-1 promotes choline phospholipid metabolism and tumor cell proliferation

Aberrantly upregulated choline phospholipid metabolism is a novel emerging hallmark of cancer, and choline kinase α (CHKα), a key enzyme for phosphatidylcholine production, is overexpressed in many types of human cancer through undefined mechanisms. Here, we demonstrate that the expression levels of the glycolytic enzyme enolase-1 (ENO1) are positively correlated with CHKα expression levels in human glioblastoma specimens and that ENO1 tightly governs CHKα expression via posttranslational regulation. Mechanistically, we reveal that both ENO1 and the ubiquitin E3 ligase TRIM25 are associated with CHKα. Highly expressed ENO1 in tumor cells binds to I199/F200 of CHKα, thereby abrogating the interaction between CHKα and TRIM25. This abrogation leads to the inhibition of TRIM25-mediated polyubiquitylation of CHKα at K195, increased stability of CHKα, enhanced choline metabolism in glioblastoma cells, and accelerated brain tumor growth. In addition, the expression levels of both ENO1 and CHKα are associated with poor prognosis in glioblastoma patients. These findings highlight a critical moonlighting function of ENO1 in choline phospholipid metabolism and provide unprecedented insight into the integrated regulation of cancer metabolism by crosstalk between glycolytic and lipidic enzymes.

Co-expression prognostic-related genes signature base on propofol and sevoflurane anesthesia predict prognosis and immunotherapy response in glioblastoma

Objectives Anesthetic drugs had been reported may impact the bio-behavior of the tumor. Propofol and sevoflurane are common anesthetics in the operation for glioblastoma (GBM). This study aims to establish a co-expression prognostic-related genes signature base on propofol and sevoflurane anesthesia to predict prognosis and immunotherapy response in GBM. Method GPM tissues with different anesthetics gene expression profiles (GSE179004) were obtained from the Gene Expression Omnibus (GEO) database. Core modules and central genes associated with propofol and sevoflurane anesthesia were identified by weighted gene coexpression network analysis (WGCNA) and establish a risk score prognostic model. Immune cell signature analysis in TCGA datasets was predicted via CIBERSORT. At last, serum methylation level of O6-methylguanine-DNA methyltransferase (MGMT) promoter was detected in GPM patient in different time during perioperative period. Results The burlywood1 group screened was significantly associated with sevoflurane-treated GBM tissue. 22 independent prognostic differential genes were construct a prognostic-related genes risk score in GBM, and showed good predictive ability. The risk score was strongly correlated with the age of the patients, but not with the sex of the patients. In addition, the differential responses to immunotherapy in high and low risk groups were analyzed, indicating that sevoflurane signature genes were consistent in the classification of gliomas. High-risk patients have high T-cell damage score and are less sensitive to immunotherapy. At last, serum methylation level of MGMT promoter was decreased in GBM patients during propofol and sevoflurane anesthesia. Conclusions Propofol and sevoflurane anesthesia associated impact on the gene expression of GBM, included the methylation level of MGMT promoter. Propofol and sevoflurane anesthesia-based risk score prognostic model, which has good prognostic power and is an independent prognostic factor in GBM patients. Therefore, this model can be used as a new biomarker for judging the prognosis of GBM patients. KEY MESSAGES Propofol and sevoflurane anesthesia-based risk score prognostic model has good prognostic power and is an independent prognostic factor in GBM patients. High Propofol and sevoflurane anesthesia-based risk score GBM patients have high T-cell damage scores and are less sensitive to immunotherapy. Serum methylation level of MGMT promoter decrease during propofol and sevoflurane anesthesia in GBM patients.

Recognition of Differentially Expressed Molecular Signatures and Pathways Associated with COVID-19 Poor Prognosis in Glioblastoma Patients.

Glioblastoma (GBM) is a type of brain cancer that is typically very aggressive and difficult to treat. Glioblastoma cases have been reported to have increased during COVID-19. The mechanisms underlying this comorbidity, including genomic interactions, tumor differentiation, immune responses, and host defense, are not completely explained. Therefore, we intended to investigate the differentially expressed shared genes and therapeutic agents which are significant for these conditions by using in silico approaches. Gene expression datasets of GSE68848, GSE169158, and GSE4290 studies were collected and analyzed to identify the DEGs between the diseased and the control samples. Then, the ontology of the genes and the metabolic pathway enrichment analysis were carried out for the classified samples based on expression values. Protein-protein interactions (PPI) map were performed by STRING and fine-tuned by Cytoscape to screen the enriched gene module. In addition, the connectivity map was used for the prediction of potential drugs. As a result, 154 overexpressed and 234 under-expressed genes were identified as common DEGs. These genes were found to be significantly enriched in the pathways involved in viral diseases, NOD-like receptor signaling pathway, the cGMP-PKG signaling pathway, growth hormone synthesis, secretion, and action, the immune system, interferon signaling, and the neuronal system. STAT1, CXCL10, and SAMDL were screened out as the top 03 out of the top 10 most critical genes among the DEGs from the PPI network. AZD-8055, methotrexate, and ruxolitinib were predicted to be the possible agents for the treatment. The current study identified significant key genes, common metabolic signaling networks, and therapeutic agents to improve our perception of the common mechanisms of GBM-COVID-19.

MicroRNAs (miRNAs) in Glioblastoma Multiforme (GBM)—Recent Literature Review

Glioblastoma multiforme (GBM) is the most common, malignant, poorly promising primary brain tumor. GBM is characterized by an infiltrating growth nature, abundant vascularization, and a rapid and aggressive clinical course. For many years, the standard treatment of gliomas has invariably been surgical treatment supported by radio- and chemotherapy. Due to the location and significant resistance of gliomas to conventional therapies, the prognosis of glioblastoma patients is very poor and the cure rate is low. The search for new therapy targets and effective therapeutic tools for cancer treatment is a current challenge for medicine and science. microRNAs (miRNAs) play a key role in many cellular processes, such as growth, differentiation, cell division, apoptosis, and cell signaling. Their discovery was a breakthrough in the diagnosis and prognosis of many diseases. Understanding the structure of miRNAs may contribute to the understanding of the mechanisms of cellular regulation dependent on miRNA and the pathogenesis of diseases underlying these short non-coding RNAs, including glial brain tumors. This paper provides a detailed review of the latest reports on the relationship between changes in the expression of individual microRNAs and the formation and development of gliomas. The use of miRNAs in the treatment of this cancer is also discussed.

Network Pharmacology Analysis and Experimental Pharmacology Study Explore the Mechanism of Asparagus against Glioblastoma

Glioblastoma (GBM) is the most malignant primary brain tumor, and there is an urgent need to explore effective therapeutic strategies to improve the prognosis of GBM patients. Asparagus (ASP) is a widely grown plant with a rich pharmacological profile that has been used to treat various cancers. However, its role in GBM remains unclear. In the study, we confirmed the inhibitory effect of ASP on GBM and explored the target and mechanism of ASP through network pharmacology, molecular docking, and molecular biology experiments. We found that P53 is a potent target of ASP for GBM treatment, and its pharmacological mechanism is the activation of p53-dependent apoptosis. Our research provides an objective basis for the development and clinical application of ASP in GBM patients.

Construction and validation of a cuproptosis-related prognostic model for glioblastoma.

Cuproptosis, a newly reported type of programmed cell death, takes part in the regulation of tumor progression, treatment response, and prognosis. But the specific effect of cuproptosis-related genes (CRGs) on glioblastoma (GBM) is still unclear. The transcriptome data and corresponding clinical data of GBM samples were downloaded from the TCGA and GEO databases. R software and R packages were used to perform statistical analysis, consensus cluster analysis, survival analysis, Cox regression analysis, Lasso regression analysis, and tumor microenvironment analysis. The mRNA and protein expression levels of model-related genes were detected by RT-qPCR and Western blot assays, respectively. The expression profile of CRGs in 209 GBM samples from two separate datasets was obtained. Two cuproptosis subtypes, CRGcluster A and CRGcluster B, were identified by consensus cluster analysis. There were apparent differences in prognosis, tumor microenvironment, and immune checkpoint expression levels between the two subtypes, and there were 79 prognostic differentially expressed genes (DEGs). According to the prognostic DEGs, two gene subtypes, geneCluster A and geneCluster B, were identified, and a prognostic risk score model was constructed and validated. This model consists of five prognostic DEGs, including PDIA4, DUSP6, PTPRN, PILRB, and CBLN1. Ultimately, to improve the applicability of the model, a nomogram was established. Patients with GBM in the low-risk cluster have a higher mutation burden and predict a longer OS than in the high-risk group. Moreover, the risk score was related to drug sensitivity and negatively correlated with the CSC index. We successfully constructed a cuproptosis-related prognostic model, which can independently predict the prognosis of GBM patients. These results further complement the understanding of cuproptosis and provide new theoretical support for developing a more effective treatment strategy.

The PTEN-associated immune prognostic signature reveals the landscape of the tumor microenvironment in glioblastoma

Glioblastoma (GBM) is a common brain tumor with a complex and diverse tumor microenvironment (TME). As PTEN mutation is the most common mutation in GBM, we aimed to investigate how PTEN mutation regulates the immune response in GBM TME and thus affects the prognosis of GBM patients. In this study, we conducted a comprehensive analysis of multiple levels of data, including whole-exome sequencing (WES), transcriptome RNA sequencing, patient survival and immune signatures, to study the relationship between PTEN mutation and TME in GBM. We developed an immune-related prognostic signature (IPS) based on the PTEN-associated immune-related genes (IRGs), and the IPS exhibited a powerful prognosis prediction capacity in different GBM cohorts. A scoring nomogram based on the IPS was also established for clinical application. In addition, the correlations of the IPS with tumor immune cell infiltration and immune checkpoints were systematically analyzed. This study illustrates the influence of PTEN mutation on the immune microenvironment of GBM. Our IPS, which is sensitive to PTEN mutation status, can enhance the prognosis prediction ability for GBM patients and provides potential targets for immunotherapy.

Clinical characteristics and prognosis of Glioblastoma patients with infratentorial recurrence

BackgroundGlioblastoma (GBM) infrequently recurs in the infratentorial region. Such Infratentorial recurrence (ITR) has some clinically unique characteristics, such as presenting unspecific symptoms and providing patients a chance to receive additional radiotherapy. However, the clinical significances of ITR are not well studied.MethodsWe reviewed newly diagnosed isocitrate dehydrogenase (IDH)-wildtype GBM patients treated at our institution between October 2008 and December 2018. ITR was defined as any type of recurrence in GBM, including dissemination or distant recurrence, which primarily developed in the supratentorial region and recurred in the infratentorial region.ResultsOf 134 patients with newly diagnosed IDH-wildtype GBM, six (4.5%) were classified as having ITR. There was no significant difference in median duration from the first surgery to ITR development between patients with and without ITR (12.2 vs. 10.2 months, P = 0.65). The primary symptoms of ITR were gait disturbance (100%, n = 6), dizziness (50.0%, n = 3), nausea (33.3%, n = 2), and cerebellar mutism (16.7%, n = 1). In four cases (66.7%), symptoms were presented before ITR development. All patients received additional treatments for ITR. The median post-recurrence survival (PRS) of ITR patients was significantly shorter than that of general GBM patients (5.5 vs. 9.1 months, P = 0.023). However, chemoradiotherapy contributed to palliating symptoms such as nausea.ConclusionsITR is a severe recurrence type in GBM patients. Its symptoms are neurologically unspecific and can be overlooked or misdiagnosed as side effects of treatments. Carefully checking the infratentorial region, especially around the fourth ventricle, is essential during the GBM patient follow-up.

The Potential of Seizure to Predict Prognosis in Glioblastoma Patients: A retrospective Study

Objective Glioma, particularly glioblastoma (GBM), represents a prevalent and aggressive primary brain tumor with limited curative options. Epileptic seizures often manifest as a common clinical symptom in these patients, but their prognostic significance remains debated. This study aimed to retrospectively analyze the clinical presentations, tumor characteristics, and the impact of seizures on the prognosis of GBM patients. Materials and Methods A total of 113 adult patients with histologically confirmed GBM treated between April 2018 and January 2022 were included in this retrospective analysis. Data encompassed clinical symptoms, preoperative and postoperative seizures, tumor localization, overall survival (OS), and other relevant factors. Statistical analysis was performed to assess the relationships between seizures and various prognostic parameters. Results Seizures were present in 39.8% of GBM patients, with 20 patients experiencing both preoperative and postoperative seizures. Preoperative and postoperative seizures were not significantly associated with differences in prognosis (p>0.05). Tumor localization, OS, Karnofsky performance score, and length of hospital stay showed significant differences between patients with and without seizures (p<0.05). Complete resection was achieved in 90.3% of patients, leading to a relatively lower incidence of postoperative seizures. Conclusion Seizures in GBM patients are associated with OS, Karnofsky performance score, and hospitalization duration, independently of other factors. The presence of seizures serves as a valuable prognostic indicator for GBM, prompting further investigation into its influence on patient outcomes, quality of life, and socioeconomic aspects of survival. These findings emphasize the importance of early seizure management in GBM patients and the need for tailored treatment strategies.

Development and validation of an inflammatory response-related prognostic model and immune infiltration analysis in glioblastoma.

Despite receiving standard treatment, the prognosis of glioblastoma (GBM) patients is still poor. Considering the heterogeneity of each patient, it is imperative to identify reliable risk model that can effectively predict the prognosis of each GBM patient to guide the personalized treatment. Transcriptomic gene expression profiles and corresponding clinical data of GBM patients were downloaded from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Inflammatory response-related genes were extracted from Gene Set Enrichment Analysis (GSEA) website. Univariate Cox regression analysis was used for prognosis-related inflammatory genes (P<0.05). A polygenic prognostic risk model was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Validation was performed through CGGA cohort. Overall survival (OS) was compared by Kaplan-Meier analysis. A nomogram was plotted to accurately predict the prognosis for each patient. GSEA was used for the pathway enrichment analysis. The single sample GSEA (ssGSEA) algorithm was implemented to conduct the immune infiltration analysis. The potential role of oncostatin M receptor (OSMR) in GBM was investigated through the in vitro experiment. A prognostic risk model consisting of 4 genes (PTPRN, OSMR, MYD88, and EFEMP2) was developed. GBM patients in the high-risk group had worse OS. The time-dependent ROC curves showed an area under the curve (AUC) of 0.782, 0.765, and 0.784 for 1-, 2-, and 3-year survival in TCGA cohort, while the AUC in the CGGA cohort was 0.589, 0.684, and 0.785 at 1, 2, and 3 years, respectively. The risk score, primary-recurrent-secondary (PRS) type, and isocitrate dehydrogenase (IDH) mutation could predict the prognosis of GBM patients well. The nomogram accurately predicted the 1-, 2-, and 3-year OS for each patient. Immune cell infiltration was associated with the risk score and the model could predict immunotherapy responsiveness. The expression of the prognostic gene was correlated with the sensitivity to antitumor drugs. Interference of OSMR inhibited proliferation and migration and promoted apoptosis of GBM cells. The prognostic model based on 4 inflammatory response-related genes had reliable predictive power to effectively predict clinical outcome in GBM patients and provided the guide for the personalized treatment.

Epigenetic Regulation of the Expression of T Cell Stimulatory and Inhibitory Factors by Histone H3 Lysine Modification Enzymes and Its Prognostic Roles in Glioblastoma.

This study aimed to identify the specific T cell co-stimulatory and co-inhibitory factors that play prognostic roles in patients with glioblastoma. Additionally, the unique histone H3 modification enzymes that regulate the expression levels of these specific co-stimulatory and co-inhibitory factors were investigated. The medical records of 84 patients newly diagnosed with glioblastoma at our institution from January 2006 to December 2020 were retrospectively reviewed. Immunohistochemical (IHC) staining for T cell co-stimulatory factors (CD27, CD28, CD137, OX40, and ICOS), T cell co-inhibitory factors (CTLA4, PD1, PD-L1, TIM3, and CD200R), and histone H3 lysine modification enzymes (MLL4, RIZ, EZH1, NSD2, KDM5c, JMJD1a, UTX, and JMJD5) was performed on archived paraffin-embedded tissues obtained by biopsy or resection. Quantitative real time-polymerase chain reaction (qRT-PCR) was performed for specific factors, which demonstrated causal relationships, in order to validate the findings of the IHC examinations. The mean follow-up duration was 27.5 months (range, 4.1-43.5 months). During this period, 76 patients (90.5%) died, and the mean OS was 19.4 months (95% confidence interval, 16.3-20.9 months). Linear positive correlations were observed between the expression levels of CD28 and JMJD1a (R2 linear = 0.982) and those of CD137 and UTX (R2 linear = 1.528). Alternatively, significant negative correlations were observed between the expression levels of CTLA4 and RIZ (R2 linear = -1.746) and those of PD-L1 and EZH1 (R2 linear = -2.118); these relationships were confirmed by qRT-PCR. In the multivariate analysis, increased expression levels of CD28 (P = 0.042), and CD137 (P = 0.009), and decreased expression levels of CTLA4 (P = 0.003), PD-L1 (P = 0.020), and EZH1 (P = 0.040) were significantly associated with longer survival. These findings suggest that the expression of certain T cell co-stimulatory factors, such as CD28 and CD 137, and co-inhibitory factors, such as CTLA4 and PD-L1 are associated with prognosis of glioblastoma patients.

Positive Expression of Retinol-Binding Protein 4 Is Related to the Malignant Clinical Features Leading to Poor Prognosis of Glioblastoma.

Retinol-binding protein 4 (RBP4) is a monomeric-binding protein belonging to the lipocalin protein family, which has been reported to be dysregulated in several malignancies such as breast cancer and lung cancer. However, the expression and function of RBP4 in glioblastoma (GBM) are completely unknown. TCGA datasets were used for analyzing the mRNA level of RBP4 in GBM and its clinical relevance. A retrospective GBM cohort (n = 73) was enrolled from our hospital to test the protein expression profile of RBP4 in GBM tissues as well as its correlation with patients' prognoses. Two human GBM cell lines, LN229 and U251, were collected to conduct overexpression and knockdown experiments targeting RBP4. The tumor-related effects of RBP4 in GBM were finally evaluated by proliferation and invasion assays. Both the higher mRNA level and protein level of RBP4 in GBM tissues were significantly correlated with poorer patients' overall survival. Multivariate analysis identified RBP4 as a novel independent prognostic predictor in GBM patients. Overexpression of RBP4 resulted in enhanced GBM proliferation capacity, which was consistent with clinical findings on the positive correlation between RBP4 level and tumor size. Meanwhile, overexpressing RBP4 promoted GBM cell migration and invasion, while silencing RBP4 led to the opposite results. RBP4 overexpression in tumor tissues is correlated with poorer prognosis of GBM patients, which functions by promoting GBM proliferation and invasion, thus, may serve as an invaluable predictive biomarker and therapeutic target.