Overall Survival Of Glioblastoma Multiforme Patients Research Articles

Predominance of MGMT promoter methylation among Pakistani glioblastoma patients.

Glioblastoma multiforme (GBM), the most prevalent subgroup of neuroepithelial tumors, is characterized by dismal overall survival (OS). Several studies have linked O6-methylguanine-DNA-methyltransferase (MGMT) promoter methylation to OS in GBM patients. However, MGMT methylation frequencies vary geographically and across ethnicities, with limited data for South Asian populations, including Pakistan. This study aimed to analyze MGMT promoter methylation in Pakistani GBM patients. Consecutive primary GBM patients diagnosed ≥ 18 years-of-age, with no prior chemotherapy or radiotherapy history, were retrospectively selected. DNA was isolated from formalin-fixed-paraffin-embedded tissues. MGMT promoter methylation was analyzed using methylation-specific PCR. Clinical, pathological, and treatment data were assessed using Fisher's exact/Chi-squared tests. OS was calculated using Kaplan-Meier analysis in SPSS 27.0.1. The study included 48 GBM patients, comprising 38 (79.2%) males and 10 (20.8%) females. The median diagnosis age was 49.5 years (range 18-70). MGMT methylation was observed in 87.5% (42/48) of all cases. Patients with MGMT methylation undergoing radiotherapy or radiotherapy plus chemotherapy exhibited significantly improved median OS of 7.2 months (95% CI, 3.7-10.7; P < 0.001) and 16.9 months (95% CI, 15.9-17.9; P < 0.001), respectively, compared to those undergoing surgical resection only (OS: 2.2 months, 95% CI, 0.8-3.6). This is the first comprehensive study highlighting a predominance of MGMT methylation in Pakistani GBM patients. Furthermore, our findings underscore the association of MGMT methylation with improved OS across diverse treatment modalities. Larger studies are imperative to validate our findings for better management of Pakistani GBM patients.

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

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

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

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

TMEM2 induces epithelial-mesenchymal transition and promotes resistance to temozolomide in GBM cells

Glioblastoma multiforme (GBM) is the most common intracranial malignant tumor and is notorious for its poor prognosis. An important element in the short overall survival of GBM patients is the lack of understanding the pathogenesis and progression of tumor and deficiency biomarkers that can be used for early diagnosis and therapeutic sensitivity monitoring. Studies have shown that transmembrane protein 2 (TMEM2) is participated in tumorigenesis of various human tumors, including rectal and breast cancers. Although Qiuyi Jiang et al. have reported that TMEM2 combined with IDH1/2 and 1p19q can predict the survival time of glioma patients based on bioinformatics, its expression and biological role of glioma remain unclear. In our study, we investigated the effect of TMEM2 expression level on glioma malignancy in public datasets and an independent internal dataset. We revealed TEMM2 expression was higher in GBM tissues than in non-tumor brain tissues (NBT). Moreover, the increase in TMEM2 expression level was closely related to tumor malignancy. The survival analysis showed that TMEM2 high expression reduces survival time in all glioma patients, including GBM and LGG patients. Subsequent experiments demonstrated that knockdown TMEM2 inhibited proliferation of GBM cells. In addition, we analyzed TMEM2 mRNA levels in different GBM subtypes, and demonstrated that TMEM2 expression was upregulated in mesenchymal subtype. Meanwhile, bioinformatics analysis and transwell assay indicated that knockdown TMEM2 suppressed epithelial-mesenchymal transition (EMT) in GBM. Importantly, Kaplan–Meier analysis demonstrated that TMEM2 high expression reduced the treatment response to TMZ in GBM patients. Knockdown of TMEM2 alone did not reduce apoptosis GBM cells, but significant apoptotic cells were observed in the group treated with additional TMZ. These studies may contribute to improving the accuracy of early diagnosis and evaluating the effectiveness of TMZ treatment in GBM patients.

Identification of the cuproptosis-related gene signature associated with the tumor environment and prognosis of patients with glioblastoma multiforme (GBM) (S17.003)

<h3>Objective:</h3> In this study, we aimed to develop a cuproptosis gene-signature risk score using bioinformatics analysis and machine learning. <h3>Background:</h3> Glioblastoma multiforme (GBM) is the most common tumor of the central nervous system with poor prognosis. Cuproptosis is a novel programmed cell death pathway targeting lipoylated tricarboxylic acid cycle proteins. Previous studies have found that it participates in tumor progression, but its role in GBM is still elusive. <h3>Design/Methods:</h3> We acquired transcriptomic data and clinical information of GBM patients from The Cancer Genome Atlas (TCGA). A total of 2283 differentially expressed genes (DEGs) were obtained from the GEPIA2 database. 26 cuproptosis-related genes (CRGs) were retrieved from literature. A correlation analysis between the 26 CRGs and the DEGs were conducted to retrieve the cuproptosis-related DEGs. Then, a univariate cox analysis was conducted to obtain the prognostic-related DEGs for overall survival (OS). The least absolute shrinkage and selection operator (LASSO) were conducted for regularization and the gene risk score was constructed using the multivariate cox coefficients. <h3>Results:</h3> A total of 731 downregulated DEGs were correlated with CRGs, while 68 upregulated DEGs were correlated with CRGs and were further screened for prognostic value using the univariate cox analysis. A total of 70 prognostic related CRGs were identified and were further screened using the LASSO cox analysis. After multivariate cox analysis, a total of seven genes were significantly associated with survival (p-value&lt;0.01). A risk-score gene signature was constructed from the cox coefficients multiplied by the expression of the following genes: −0.0012*DPP10+0.0021*EGR4+0.0015*ITPKA+ 0.0003* PTPRN+ 0.0007* STEAP2+ 0.0006* TENM2+− 0.0017* ZNF540. <h3>Conclusions:</h3> Univariate and multivariate Cox regression analyses showed the CRGs-based prognostic signature independently functioned as a risk factor for OS in GBM patients. Furthermore, our results gave a promising understanding of cuproptosis in GBM, as well as a tailored prediction tool for prognosis and immunotherapeutic responses in patients. <b>Disclosure:</b> Ms. Al-Majali has nothing to disclose. Mr. Ahmed has nothing to disclose. Mr. Ababneh has nothing to disclose. Mr. Khalefa has nothing to disclose. Miss Alzghoul has nothing to disclose. Miss Al-Bzour has nothing to disclose.

Abstract 2046: Identification of the cuproptosis-related gene signature associated with the tumor environment and prognosis of patients with glioblastoma multiforme (GBM)

Abstract Background: Glioblastoma multiforme (GBM) is the most common tumor of the central nervous system with poor prognosis. Cuproptosis is a novel programmed cell death pathway targeting lipoylated tricarboxylic acid cycle proteins. Previous studies have found that it participates in tumor progression, but its role in GBM is still elusive. In this study, we aimed to develop a cuproptosis gene-signature risk score using bioinformatics analysis and machine learning. Methods: We acquired transcriptomic and clinical information of GBM patients from The Cancer Genome Atlas (TCGA). A total of 2283 differentially expressed genes (DEGs) were obtained from the GEPIA2 database. 26 cuproptosis-related genes (CRGs) were retrieved from literature. A correlation analysis between the 26 CRGs and the DEGs were conducted to retrieve the cuproptosis-related DEGs. Then, a univariate cox analysis was conducted to obtain the prognostic-related DEGs for overall survival (OS). The least absolute shrinkage and selection operator (LASSO) were conducted for regularization and the gene risk score was constructed using the multivariate cox coefficients. Results: A total of 731 downregulated DEGs were correlated with CRGs, while 68 upregulated DEGs were correlated with CRGs and were further screened for prognostic value using the univariate cox analysis. A total of 70 prognostic related CRGs were identified and were further screened using the LASSO cox analysis. After multivariate cox analysis, a total of seven genes were significantly associated with survival (p-value&amp;lt;0.01). A risk-score gene signature was constructed from the cox coefficients multiplied by the expression of the following genes: -0.0012*DPP10+0.0021*EGR4+0.0015*ITPKA+ 0.0003* PTPRN+ 0.0007* STEAP2+ 0.0006* TENM2+- 0.0017* ZNF540. Conclusion: Univariate and multivariate Cox regression analyses showed the CRGs-based prognostic signature independently functioned as a risk factor for OS in GBM patients. Furthermore, our results gave a promising understanding of cuproptosis in GBM, as well as a tailored prediction tool for prognosis and immunotherapeutic responses in patients. Citation Format: Yaman B. Ahmed, Ayah N. Al-Bzour, Ghayda'a N. Al-Majali, Zaid M. Khalefa, Saja M. Alzghoul. Identification of the cuproptosis-related gene signature associated with the tumor environment and prognosis of patients with glioblastoma multiforme (GBM) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2046.

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

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

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

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

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

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

LINC01711 promotes transforming growth factor-beta (TGF-β) induced invasion in glioblastoma multiforme (GBM) by acting as a competing endogenous RNA for miR-34a and promoting ZEB1 expression

GBM is the central nervous system's most aggressive and malignant tumor. TGF-β expression is elevated in GBM, and it promotes invasion and EMT. TGF-β regulates the expression of several lncRNAs, which promote glioma pathogenesis. Here we characterize the role of TGF-β-induced lncRNA- LINC01711 in glioma pathogenesis. We show that LINC01711 expression is significantly upregulated in GBM tissues and is associated with poor overall survival of GBM patients. Loss-of-function studies illustrate that LINC01711 promotes proliferation, migration, and invasion in GBM. In addition, LINC01711 depletion sensitizes glioma cells to Temozolomide (TMZ) induced apoptosis by inhibiting ZEB1 expression. LINC01711 functions as a competing endogenous RNA for miR-34a and promotes ZEB1 expression to regulate invasion. Our findings suggest that LINC01711 is an attractive therapeutic target for GBM.

Targeting TRAF3IP2 inhibits angiogenesis in glioblastoma.

Increased vascularization, also known as neoangiogenesis, plays a major role in many cancers, including glioblastoma multiforme (GBM), by contributing to their aggressive growth and metastasis. Although anti-angiogenic therapies provide some clinical improvement, they fail to significantly improve the overall survival of GBM patients. Since various pro-angiogenic mediators drive GBM, we hypothesized that identifying targetable genes that broadly inhibit multiple pro-angiogenic mediators will significantly promote favorable outcomes. Here, we identified TRAF3IP2 (TRAF3-interacting protein 2) as a critical regulator of angiogenesis in GBM. We demonstrated that knockdown of TRAF3IP2 in an intracranial model of GBM significantly reduces vascularization. Targeting TRAF3IP2 significantly downregulated VEGF, IL6, ANGPT2, IL8, FZGF2, PGF, IL1β, EGF, PDGFRB, and VEGFR2 expression in residual tumors. Our data also indicate that exogenous addition of VEGF partially restores angiogenesis by TRAF3IP2-silenced cells, suggesting that TRAF3IP2 promotes angiogenesis through VEGF- and non-VEGF-dependent mechanisms. These results indicate the anti-angiogenic and anti-tumorigenic potential of targeting TRAF3IP2 in GBM, a deadly cancer with limited treatment options.

Immune surveillance in glioblastoma: Role of the NKG2D system and novel cell-based therapeutic approaches.

Glioblastoma, formerly known as glioblastoma multiforme (GBM), is the most frequent and most aggressive brain tumour in adults. The brain is an immunopriviledged organ, and the blood-brain barrier shields the brain from immune surveillance. In this review, we discuss the composition of the immunosuppressive tumour micromilieu and potential immune escape mechanisms in GBM. In this respect, we focus on the role of the NKG2D receptor/ligand system. NKG2D ligands are frequently expressed on GBM tumour cells and can activate NKG2D-expressing killer cells including NK cells and γδ T cells. Soluble NKG2D ligands, however, contribute to tumour escape from immunological attack. We also discuss the current immunotherapeutic strategies to improve the survival of GBM patients. Such approaches include the modulation of the NKG2D receptor/ligand system, the application of checkpoint inhibitors, the adoptive transfer of ex vivo expanded and/or modified immune cells or the application of antibodies and antibody constructs to target cytotoxic effector cells in vivo. In view of the multitude of pursued strategies, there is hope for improved overall survival of GBM patients in the future.

Cumulative Scoring Systems and Nomograms for Predicating Survival in Patients With Glioblastomas: A Study Based on Peripheral Inflammatory Markers.

Inflammation is a hallmark of cancers. The purpose of the present study was to evaluate the prognostic potential of hematological inflammatory markers in glioblastoma multiforme (GBM) patients. The clinical data of 99 patients with lower-grade gliomas and 88 patients with GBMs were retrospectively analyzed. The optimal cutoff values for peripheral markers were determined by X-tile. Kaplan-Meier and Cox proportional hazard regression analyses were performed to identify markers with prognostic significance. Several scoring systems were constructed by combining these prognostic markers. The predictive accuracies of nomograms incorporating these scoring systems were evaluated by Harrell’s concordance index and receiver operating characteristic curve analysis. GBM patients exhibited higher neutrophil counts (p=0.001), neutrophil-to-lymphocyte ratio (NLR) (p<0.001), and platelet-to-lymphocyte ratio (PLR) (p=0.001), as well as lower lymphocyte counts (p=0.023), lymphocyte-to-monocyte ratio (LMR) (p=0.015), and albumin-to-globulin ratio (AGR) (p=0.003) than those with lower-grade gliomas. Multivariate analysis indicated that a high NLR (> 2.0) (Hazard ratio[HR]=2.519, 95% confidence interval (CI): 1.220-5.204, p=0.013), low LMR (< 2.3) (HR=2.268, 95%CI: 1.172-4.386, p=0.015), or low AGR (< 1.7) (HR=2.924, 95%CI: 1.389-6.135, p=0.005) were associated with poor overall survival in GBM patients. The scoring systems of AGR-NLR, AGR-LMR, and LMR-NLR were associated with GBM survival. The nomogram integrating AGR-NLR score had the best efficacy in predicting GBM survival (c-index=0.874). Pretreatment scores of AGR-NLR, AGR-LMR, and LMR-NLR may serve as prognostic factors for GBM patients, and a nomogram integrating AGR-NLR may provide a reliable tool to facilitate personalized preoperative evaluations.

Transforming Growth Factor-Beta-Regulated LncRNA-MUF Promotes Invasion by Modulating the miR-34a Snail1 Axis in Glioblastoma Multiforme.

Transforming growth factor beta (TGF-β)-regulated long-non-coding RNAs (lncRNAs) modulate several aspects of tumor development such as proliferation, invasion, metastasis, epithelial to mesenchymal transition (EMT), and drug resistance in various cancers, including Glioblastoma multiforme (GBM). We identified several novel differentially expressed lncRNAs upon TGF-β treatment in glioma cells using genome-wide microarray screening. We show that TGF-β induces lncRNA-MUF in glioma cells, and its expression is significantly upregulated in glioma tissues and is associated with poor overall survival of GBM patients. Knockdown of lncRNA-MUF reduces proliferation, migration, and invasion in glioma cells and sensitizes them to temozolomide (TMZ)-induced apoptosis. In addition, lncRNA-MUF downregulation impairs TGF-β-induced smad2/3 phosphorylation. In line with its role in regulating invasion, lncRNA-MUF functions as a competing endogenous RNA (ceRNA) for miR-34a and promotes Snail1 expression. Collectively, our findings suggest lncRNA-MUF as an attractive therapeutic target for GBM.

Recent Development in NKT-Based Immunotherapy of Glioblastoma: From Bench to Bedside.

Glioblastoma multiforme (GBM) is an aggressive and dismal disease with a median overall survival of around 15 months and a 5-year survival rate of 7.2%. Owing to genetic mutations, drug resistance, disruption to the blood–brain barrier (BBB)/blood–brain tumor barrier (BBTB), and the complexity of the immunosuppressive environment, the therapeutic approaches to GBM represent still major challenges. Conventional therapies, including surgery, radiotherapy, and standard chemotherapy with temozolomide, have not resulted in satisfactory improvements in the overall survival of GBM patients. Among cancer immunotherapeutic approaches, we propose that adjuvant NKT immunotherapy with invariant NKT (iNKT) and cytokine-induced killer (CIK) cells may improve the clinical scenario of this devastating disease. Considering this, herein, we discuss the current strategies of NKT therapy for GBM based primarily on in vitro/in vivo experiments, clinical trials, and the combinatorial approaches with future therapeutic potential.

The effect of preoperative prognostic nutritional index on outcome in glioblastoma multiforme patients

Background/Aim: Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor subtype with a poor prognosis despite various treatments. Some prognostic markers on survival (such as age, Eastern Cooperative Oncology Group Performance Score (ECOG-PS), isocitrated hydrogenase (IDH) status, alpha thalassemia/mental retardation syndrome X-linked (ATRX) mutation status, possibility of extensive surgery) have been defined. Prognostic Nutritional Index (PNI) has been evaluated in various cancers (such as lung, esophagus, and pancreas), and patients with a low PNI score have been associated with a poor prognosis for overall survival (OS). Our study aimed to examine the effectiveness of molecular and demographic characteristics and preoperative PNI score that may affect OS in GBM patients. Methods: In this retrospective cohort study, GBM diagnosed patients who were 18 years old or older, were included in the study. We measured their pretreatment PNI score and performed multivariate Cox regression analyses of OS in GBM patients. Results: A total of 107 patients were included in the study. Median age was 58 (range, 32-83) years. 72 patients (67.3%) were male and 35 patients (32.7%) were female. The mean preoperative PNI level was calculated as 50.5. The median overall-survival (mOS) was 19.7 months and the median time to progression (mTTP) was 8.1 months. There was no statistically significant result on overall survival in the univariate analysis of patients with PNI>50.5 (P=0.121). In multivariate analysis, being 70 years or older (P=0.012), IDH-1 wild and ATRX mutant patients (P=0.016), IDH-1 mutant and ATRX wild patients (P=0.037), and TTP 12 months and older (P<0.001) were considered as independent risk factors on overall survival. Conclusions: In our study, the effect of preoperative PNI score on survival could not be demonstrated. Further studies are needed to elucidate the potential impact of PNI on outcomes in patients with GBM.

Dissecting Prognosis Modules and Biomarkers in Glioblastoma Based on Weighted Gene Co-Expression Network Analysis.

IntroductionAs one of the most prevalent and malignant brain cancers, glioblastoma multiforme (GBM) presents a poor prognosis and the molecular mechanisms remain poorly understood. Consequently, molecular research, including various biomarkers, is essential to exploit the occurrence and development of glioma.MethodsWeighted gene co-expression network analysis (WGCNA) was used to construct gene co-expression modules and networks based on the Chinese Glioma Genome Atlas (CGGA) glioblastoma specimens. Then, protein–protein interaction (PPI) and gene ontology (GO) analyses were performed to mine hub genes. RT-PCR and immunohistochemistry were employed to examine the expression level of GRPR, CXCL5, and CXCL11 in glioma patients.ResultsWe confirmed two gene modules by protein–protein interaction networks. Functional enrichment analysis was performed to identify the significance of gene modules. Prognostic biomarkers GRPR, CXCL5, and CXCL11 related to the survival time of GBM samples were mined in The Cancer Genome Atlas (TCGA) dataset. qRT-PCR revealed that GRPR, CXCL5, and CXCL11 led to a significant increase in GBM sample compared to control.ConclusionIn this study, we developed and confirmed three mRNA signatures (GRPR, CXCL5, and CXCL11) for evaluating overall survival in GBM patients. Our research assists in existing understanding of GBM diagnosis and prognosis.

Identification of an IFN-β-associated gene signature for the prediction of overall survival among glioblastoma patients.

BackgroundBrain glioblastoma multiforme (GBM) is the most common primary malignant intracranial tumor. The prognosis of this disease is extremely poor. While the introduction of β-interferon (IFN-β) regimen in the treatment of gliomas has significantly improved the outcome of patients; The mechanism by which IFN-β induces increased TMZ sensitivity has not been described. Therefore, the main objective of the study was to elucidate the molecular mechanisms responsible for the beneficial effect of IFNβ in GBM.MethodsMessenger RNA expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas (TCGA) GBM and GSE83300 dataset from the Gene Expression Omnibus. Univariate Cox regression analysis and lasso Cox regression model established a novel 4-gene IFN-β signature (peroxiredoxin 1, Sec61 subunit beta, X-ray repair cross-complementing 5, and Bcl-2-like protein 2) for GBM prognosis prediction. Further, GBM samples (n=50) and normal brain tissues (n=50) were then used for real-time polymerase chain reaction experiments. Gene set enrichment analysis (GSEA) was performed to further understand the underlying molecular mechanisms. Pearson correlation was applied to calculate the correlation between the long non-coding RNAs (lncRNAs) and IFN-β-associated genes. An lncRNA with a correlation coefficient |R2|>0.3 and P<0.05 was considered to be an IFN-β-associated lncRNA.ResultsPatients in the high-risk group had significantly poorer survival than patients in the low-risk group. The signature was found to be an independent prognostic factor for GBM survival. Furthermore, GSEA revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust 4-gene IFN-β signature for GBM prognosis prediction. The signature might contain potential biomarkers for metabolic therapy and treatment response prediction for GBM patients.ConclusionsIn the present study, we established a novel IFN-β-associated gene signature to predict the overall survival of GBM patients, which may help in clinical decision making for individual treatment.

Prognostic and Therapeutic Roles of the Insulin Growth Factor System in Glioblastoma.

Glioblastoma multiforme (GBM) is the most common primary brain malignancy and is often resistant to conventional treatments due to its extensive cellular heterogeneity. Thus, the overall survival of GBM patients remains extremely poor. Insulin-like growth factor (IGF) signaling entails a complex system that is a key regulator of cell transformation, growth and cell-cycle progression. Hence, its deregulation is frequently involved in the development of several cancers, including brain malignancies. In GBM, differential expression of several IGF system components and alterations of this signaling axis are linked to significantly worse prognosis and reduced responsiveness to temozolomide, the most commonly used pharmacological agent for the treatment of the disease. In the present review we summarize the biological role of the IGF system in the pathogenesis of GBM and comprehensively discuss its clinical significance and contribution to the development of resistance to standard chemotherapy and experimental treatments.

A novel Foxp3-related immune prognostic signature for glioblastoma multiforme based on immunogenomic profiling.

Foxp3+ regulatory T cells (Treg) play an important part in the glioma immunosuppressive microenvironment. This study analyzed the effect of Foxsp3 on the immune microenvironment and constructed a Foxp3-related immune prognostic signature (IPS)for predicting prognosis in glioblastoma multiforme (GBM). Immunohistochemistry (IHC) staining for Foxp3 was performed in 72 high-grade glioma specimens. RNA-seq data from 152 GBM samples were obtained from The Cancer Genome Atlas database (TCGA) and divided into two groups, Foxp3 High (Foxp3_H) and Foxp3 Low (Foxp3_L), based on Foxp3 expression. We systematically analyzed the influence of Foxp3 on the immune microenvironment. Least Absolute Shrinkage and Selection Operator (LASSO) Cox analysis was conducted for immune-related genes that were differentially expressed between Foxp3_H and Foxp3_L GBM patients. We found a differential expression of Foxp3 in high-grade glioma tissues. The presence of Foxp3 was significantly associated with poor OS. From the four-gene IPS developed, GBM patients were stratified into low-risk and high-risk groups in both the training set and validation sets. Furthermore, we developed a novel nomogram to evaluate the overall survival in GBM patients. This study offers innovative insights into the GBM immune microenvironment and these findings contribute to individualized treatment and improvement in the prognosis for GBM patients.