Marker In Glioblastoma Research Articles

SPTSSA Is a Prognostic Marker for Glioblastoma Associated with Tumor-Infiltrating Immune Cells and Oxidative Stress.

SPTSSA encodes the small subunit A of serine palmitoyltransferase. It catalyzes the formation of sphingoid long-chain base backbone of sphingolipids. Its role in glioma prognosis and tumor-infiltrating immune cells remains unclear. We analyzed SPTSSA expression and association with clinical prognosis using GEPIA and CGGA database. Then, GSEA was performed to identify relevant biological functions of SPTSSA. The correlations between SPTSSA expression and tumor immune infiltrates were investigated using CIBERSORT and TIMER. Finally, IHC and IF were performed to confirm the value of prognosis and the correlation with immune infiltration. SPTSSA expression was significantly upregulated in diffuse glioma compared to normal tissues and associated with poor survival in GEPIA and CGGA database. Then, we identified biological processes and signaling pathways associated with SPTSSA expression. The result showed that SPTSSA enriched in the GO term like oxidative stress. Finally, we showed that SPTSSA expression was significantly associated with tumor-infiltrating immune cells and overall survival via IHC. These findings suggest that SPTSSA expression might be used as a prognostic biomarker for glioma and potential target for novel glioma therapy.

Pericyte mediates the infiltration, migration, and polarization of macrophages by CD163/MCAM axis in glioblastoma

SummaryMicroenvironment cells (MCs) play a critical role in tumor proliferation, progression, and metastasis. However, it has not been adequately addressed whether MCs could be used as a reliable prognostic marker in glioblastoma (GBM). In the current study, the cell pair (CP) score was constructed in 1137 GBM samples based on the cell pair algorithm and Gaussian finite mixture model (GMM) and was verified in 73 GBM samples from the Xiangya cohort. CP score predicted GBM patients’ survival and response to anti-PD-1 treatment with high sensitivity. Macrophage markers CD68 and CD163 were co-expressed with pericyte markers MCAM and MG2. Pericyte could mediate the infiltration, migration, and M2 type polarization of macrophages by MCAM. The CP score was a valuable tool for predicting survival outcomes and guiding immunotherapy for GBM patients. Cell pair pericyte/macrophage and gene pair CD163/MCAM were biologically significant in the tumor microenvironment of GBM.

Liquid Biopsy in Glioblastoma.

Simple SummaryGlioblastoma is the most common and malignant primary brain tumor. Despite intensive research for new treatments, the survival of patients has not significantly improved in recent decades. Currently, glioblastoma is mainly diagnosed by neuroimaging techniques followed by histopathological and molecular analysis of the resected or biopsied tissue. Both imaging and tissue-based methods have, despite their advantages, some important limitations highlighting the necessity for alternative techniques such as liquid biopsy. It appears as an attractive and non-invasive alternative to support the diagnosis and the follow-up of patients with glioblastoma and to identify early recurrence. Liquid biopsy, primarily through blood tests, involves the detection and quantification of tumoral content released by tumors into the biofluids. The aim of the present review is to discuss the biological bases, the advantages, and the disadvantages of the most important circulating biomarkers so far proposed for glioblastoma.Glioblastoma (GBM) is the most common and aggressive primary brain tumor. Despite recent advances in therapy modalities, the overall survival of GBM patients remains poor. GBM diagnosis relies on neuroimaging techniques. However, confirmation via histopathological and molecular analysis is necessary. Given the intrinsic limitations of such techniques, liquid biopsy (mainly via blood samples) emerged as a non-invasive and easy-to-implement alternative that could aid in both the diagnosis and the follow-up of GBM patients. Cancer cells release tumoral content into the bloodstream, such as circulating tumor DNA, circulating microRNAs, circulating tumor cells, extracellular vesicles, or circulating nucleosomes: all these could serve as a marker of GBM. In this narrative review, we discuss the current knowledge, the advantages, and the disadvantages of each circulating biomarker so far proposed.

The Progression Related Gene RAB42 Affects the Prognosis of Glioblastoma Patients

Background: Glioblastoma (GBM) represents the most malignant glioma among astrocytomas and is a lethal form of brain cancer. Many RAB genes are involved in different cancers but RAB42 (Ras-associated binding 42) is seldom studied in GBM. Our study aimed to explore the role of RAB42 expression in the development and prognosis of GBM. Methods: All GBM patient data were obtained from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. The relevance of RAB42 expression to the clinicopathologic characteristics of GBM patients was analyzed. The overall survival (OS) significance was determined using log-rank. Significantly enriched KEGG pathways were screened using gene set enrichment analysis (GSEA). Results: High expression of RAB42 was observed in GBM specimens compared with normal samples, which was also verified in cell lines and tissue samples. Elevated RAB42 expression was correlated with higher GBM histological grade. The prognosis of GBM patients with high RAB42 expression was worse than those with lower RAB42. A total of 35 pathways, such as the P53 pathway, were significantly activated in highly RAB42-expressed GBM samples. Conclusions: High RAB42 expression is related to the development of GBM, and RAB42 is a probable prognostic marker for GBM.

Conventional MRI features can predict the molecular subtype of adult grade 2–3 intracranial diffuse gliomas

PurposeMolecular biomarkers are important for classifying intracranial gliomas, prompting research into correlating imaging with genotype (“radiogenomics”). A limitation of the existing radiogenomics literature is the paucity of studies specifically characterizing grade 2–3 gliomas into the three key molecular subtypes. Our study investigated the accuracy of multiple different conventional MRI features for genotype prediction.MethodsGrade 2–3 gliomas diagnosed between 2007 and 2013 were identified. Two neuroradiologists independently assessed nine conventional MRI features. Features with better inter-observer agreement (κ ≥ 0.6) proceeded to consensus assessment. MRI features were correlated with genotype, classified as IDH-mutant and 1p/19q-codeleted (IDHmut/1p19qcodel), IDH-mutant and 1p/19q-intact (IDHmut/1p19qint), or IDH-wildtype (IDHwt). For IDHwt tumors, additional molecular markers of glioblastoma were noted.ResultsOne hundred nineteen patients were included. T2-FLAIR mismatch (stratified as > 50%, 25–50%, or < 25%) was the most predictive feature across genotypes (p < 0.001). All 30 tumors with > 50% mismatch were IDHmut/1p19qint, and all seven with 25–50% mismatch. Well-defined margins correlated with IDHmut/1p19qint status on univariate analysis (p < 0.001), but this related to correlation with T2-FLAIR mismatch; there was no longer an association when considering only tumors with < 25% mismatch (p = 0.386). Enhancement (p = 0.001), necrosis (p = 0.002), and hemorrhage (p = 0.027) correlated with IDHwt status (especially “molecular glioblastoma”). Calcification correlated with IDHmut/1p19qcodel status (p = 0.003). A simple, step-wise algorithm incorporating these features, when present, correctly predicted genotype with a positive predictive value 91.8%.ConclusionT2-FLAIR mismatch strongly predicts IDHmut/1p19qint even with a lower threshold of ≥ 25% mismatch and outweighs other features. Secondary features include enhancement, necrosis and hemorrhage (predicting IDHwt, especially “molecular glioblastoma”), and calcification (predicting IDHmut/1p19qcodel).

Biophysical insights into OR2T7: Investigation of a potential prognostic marker for glioblastoma

Glioblastoma multiforme (GBM) is the most aggressive and prevalent form of brain cancer, with an expected survival of 12–15 months following diagnosis. GBM affects the glial cells of the central nervous system, which impairs regular brain function including memory, hearing, and vision. GBM has virtually no long-term survival even with treatment, requiring novel strategies to understand disease progression. Here, we identified a somatic mutation in OR2T7, a G-protein-coupled receptor (GPCR), that correlates with reduced progression-free survival for glioblastoma (log rank p-value = 0.05), suggesting a possible role in tumor progression. The mutation, D125V, occurred in 10% of 396 glioblastoma samples in The Cancer Genome Atlas, but not in any of the 2504 DNA sequences in the 1000 Genomes Project, suggesting that the mutation may have a deleterious functional effect. In addition, transcriptome analysis showed that the p38α mitogen-activated protein kinase (MAPK), c-Fos, c-Jun, and JunB proto-oncogenes, and putative tumor suppressors RhoB and caspase-14 were underexpressed in glioblastoma samples with the D125V mutation (false discovery rate < 0.05). Molecular modeling and molecular dynamics simulations have provided preliminary structural insight and indicate a dynamic helical movement network that is influenced by the membrane-embedded, cytofacial-facing residue 125, demonstrating a possible obstruction of G-protein binding on the cytofacial exposed region. We show that the mutation impacts the “open” GPCR conformation, potentially affecting Gα-subunit binding and associated downstream activity. Overall, our findings suggest that the Val125 mutation in OR2T7 could affect glioblastoma progression by downregulating GPCR-p38 MAPK tumor-suppression pathways and impacting the biophysical characteristics of the structure that facilitates Gα-subunit binding. This study provides the theoretical basis for further experimental investigation required to confirm that the D125V mutation in OR2T7 is not a passenger mutation. With validation, the aforementioned mutation could represent an important prognostic marker and a potential therapeutic target for glioblastoma.

Bromodomain-containing protein 4 activates cardiotrophin-like cytokine factor 1, an unfavorable prognostic biomarker, and promotes glioblastoma in vitro

BackgroundGlioblastoma (GBM) is one of the most common and malignant brain tumors. Cardiotrophin-like cytokine factor 1 (CLCF1) is a member of the IL-6 superfamily. However, the clinical significance, potential role, and molecular mechanism of CLCF1 in GBM remain obscure. Here, the expression and prognostic significance of CLCF1 was investigated in GBM.MethodsThe Cancer Genome Atlas (TCGA) GBM and Chinese Glioma Genome Atlas (CGGA) datasets were downloaded and analyzed by using Gene Expression Profiling Interactive Analysis (GEPIA). Next, 3 shRNAs targeting CLCF1 were designed, and silencing efficiency was examined with real-time polymerase chain reaction (PCR). Cell Counting Kit 8 (CCK-8), flow cytometry, transwell, and wound healing assays were used to study the function of CLCF1 in glioma cells.ResultsWe found increased expression of CLCF1 as an unfavorable prognostic marker in GBM. Functionally, down-regulation of CLCF1 significantly reduced cell proliferation, induced cell apoptosis and cell cycle G2 phase arrest, and weakened the migration and invasion of GBM cells. Downstream pathway analysis was conducted, and potential targets in cytokine receptors, extracellular matrix (ECM) receptors, apoptosis, and the cell cycle were uncovered. Finally, transcriptional regulators were analyzed, and bromodomain-containing protein 4 (BRD4) was found to activate CLCF1 in GBM.ConclusionsCLCF1, transcriptionally activated by BRD4, promotes glioma and serves as an unfavorable marker in GBM.

Immune-related lncRNAs, LINC01268 and CTB-31O20.2, as favorable prognostic markers for glioma inhibition

BackgroundGlioblastoma (GBM) is the most common and fatal tumor in the central nervous system. Recent studies have found that long non-coding RNAs (lncRNAs) serve as competitive endogenous RNAs (ceRNAs) and play an important role in GBM by regulating immune responses. The aim of the present study was to identify lncRNAs with immune relevance and functions in GBM.MethodsWe analyzed GBM datasets from The Cancer Genome Atlas (TCGA) database to obtain 356 significantly differentially expressed lncRNAs (DE-lncRNAs), 4,951 DE-mRNAs, and 34 DE-miRNAs in GBM, respectively. For mRNAs, 369 DE-mRNAs were identified as immune-related genes in the ImmPort database. For DE-lncRNAs, univariate analysis identified 39 DE-lncRNAs with prognostic significance, and 9 DE-lncRNAs were included in the ImmLnc database. Combined analysis was then conducted by integrating 9 immune-related DE-lncRNAs, 369 immune-related DE-mRNAs, and 34 DE-miRNAs. A ceRNA network composed of 2 upregulated lncRNAs (LINC01268 and CTB-31O20.2), 3 downregulated miRNAs, and 5 upregulated mRNAs was generated.ResultsKaplan-Meier survival analysis and univariate and multivariate Cox regression analyses showed that LINC01268 and CTB-31O20.2 serve as independent favorable prognostic markers in GBM. LINC01268 and CTB-31O20.2 overexpression was conducted in GBM cell U251. Cell Counting Kit-8 (CCK8), Transwell assay, and scratch healing assay indicated that LINC01268 and CTB-31O20.2 inhibit GBM cell line, U251, proliferation, invasion, and migration.ConclusionsLINC01268 and CTB-31O20.2 are independent prognostic immune-related markers, and reduce cancer cell proliferation and metastasis in GBM.

Glioblastomas - Study Using Molecular Markers

Glioblastoma is a lethal tumor that can develop in the central nervous system. The most serious type of brain cancer is known as glioblastoma multiforme (GBM). This work aims to emphasize the usage of different glioblastoma markers during the diagnosis process and the treatment of the GBM. And also this may focus on the changes that are often associated with GBM pathogenesis and understanding the significance of glioblastoma markers, as well as their involvement in numerous critical cellular signaling pathways, can help direct potential research toward novel GBM treatments.

Integrated Analysis of ceRNA Network to Reveal Potential Prognostic Biomarkers for Glioblastoma

Glioblastoma (GBM), originating in the brain, is a universally aggressive malignant tumor with a particularly poor prognosis. Therefore, insight into the critical role of underlying genetic mechanisms is essential to developing new therapeutic approaches. This study aims to identify potential markers with clinical and prognostic significance in GBM. To this end, increasing numbers of differentially expressed RNA have been identified used to construct competitive endogenous RNA networks for prognostic analysis via comparison and analysis of RNA expression levels of tumor and normal tissues in glioblastoma. This analysis demonstrated that the RNA expression patterns of normal and tumor samples were significantly different. Thus, the resulting differentially expressed RNAs were used to construct competitive endogenous RNA (competing endogenous RNA, ceRNA) networks. The functional enrichment indicated mRNAs in the network are critically involved in a variety of biological functions. Additionally, the prognostic analysis suggested 27 lncRNAs, including LOXL1-AS1, AL356414.1, etc., were significantly associated with patient survival. Given the prognostic significance of these 27 lncRNAs in GBM, we sought to classify the samples. Importantly, Kaplan-Meier analysis revealed that survival times varied significantly among the different categories. Overall, these results identify that the candidate lncRNAs are potential prognostic markers of GBM and its corresponding mRNAs may be a potential target for therapy.

Exploring the potential biomarkers for prognosis of glioblastoma via weighted gene co-expression network analysis.

BackgroundGlioblastoma (GBM) is the most common malignant tumor in the central system with a poor prognosis. Due to the complexity of its molecular mechanism, the recurrence rate and mortality rate of GBM patients are still high. Therefore, there is an urgent need to screen GBM biomarkers to prove the therapeutic effect and improve the prognosis.ResultsWe extracted data from GBM patients from the Gene Expression Integration Database (GEO), analyzed differentially expressed genes in GEO and identified key modules by weighted gene co-expression network analysis (WGCNA). GSE145128 data was obtained from the GEO database, and the darkturquoise module was determined to be the most relevant to the GBM prognosis by WGCNA (r = − 0.62, p = 0.01). We performed enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) to reveal the interaction activity in the selected modules. Then Kaplan-Meier survival curve analysis was used to extract genes closely related to GBM prognosis. We used Kaplan-Meier survival curves to analyze the 139 genes in the darkturquoise module, identified four genes (DARS/GDI2/P4HA2/TRUB1) associated with prognostic GBM. Low expression of DARS/GDI2/TRUB1 and high expression of P4HA2 had a poor prognosis. Finally, we used tumor genome map (TCGA) data, verified the characteristics of hub genes through Co-expression analysis, Drug sensitivity analysis, TIMER database analysis and GSVA analysis. We downloaded the data of GBM from the TCGA database, the results of co-expression analysis showed that DARS/GDI2/P4HA2/TRUB1 could regulate the development of GBM by affecting genes such as CDC73/CDC123/B4GALT1/CUL2. Drug sensitivity analysis showed that genes are involved in many classic Cancer-related pathways including TSC/mTOR, RAS/MAPK.TIMER database analysis showed DARS expression is positively correlated with tumor purity (cor = 0.125, p = 1.07e−02)), P4HA2 expression is negatively correlated with tumor purity (cor =−0.279, p = 6.06e−09). Finally, GSVA analysis found that DARS/GDI2/P4HA2/TRUB1 gene sets are closely related to the occurrence of cancer.ConclusionWe used two public databases to identify four valuable biomarkers for GBM prognosis, namely DARS/GDI2/P4HA2/TRUB1, which have potential clinical application value and can be used as prognostic markers for GBM.

SETMAR Shorter Isoform: A New Prognostic Factor in Glioblastoma.

Recent evidence suggests that the chimeric protein SETMAR is a factor of interest in cancer, especially in glioblastoma. However, little is known about the expression of this protein in glioblastoma tissues, and no study has been done to assess if SETMAR could be a prognostic and/or diagnostic marker of glioblastoma. We analyzed protein extracts of 47 glioblastoma samples coming from a local and a national cohort of patients. From the local cohort, we obtained localized biopsies from the central necrosis area, the tumor, and the perilesional brain. From the French Glioblastoma Biobank (FGB), we obtained three types of samples: from the same tumors before and after treatment, from long survivors, and from very short survivors. We studied the correlations between SETMAR amounts, clinical profiles of patients and other associated proteins (PTN, snRNP70 and OLIG2). In glioblastoma tissues, the shorter isoform of SETMAR (S-SETMAR) was predominant over the full-length isoform (FL-SETMAR), and the expression of both SETMAR variants was higher in the tumor compared to the perilesional tissues. Data from the FGB showed that SETMAR amounts were not different between the initial tumors and tumor relapses after treatment. These data also showed a trend toward higher amounts of S-SETMAR in long survivors. In localized biopsies, we found a positive correlation between good prognosis and large amounts of S-SETMAR in the perilesional area. This is the main result presented here: survival in Glioblastoma is correlated with amounts of S-SETMAR in perilesional brain, which should be considered as a new relevant prognosis marker.

Temporal muscle thickness as an independent prognostic imaging marker in newly diagnosed glioblastoma patients: A validation study.

BackgroundPrevious studies have recognized temporal muscle thickness (TMT) as a prognostic marker in glioblastoma, but clinical implementation is hampered due to studies’ heterogeneity and lack of established cutoff values. The aim of this study was to assess the validity of recent proposed sex-specific TMT cutoff values in a real-world population of genotyped primary glioblastoma patients.MethodsWe measured TMT in preoperative MR images of 328 patients. Sex-specific TMT cutoff values were used to divide patients into “at risk of sarcopenia” or “normal muscle status”. Kaplan-Meier analyses and stepwise multivariate Cox-Regression analyses were used to assess the association with overall survival (OS) and progression-free survival (PFS). The association with occurrence of complications and discontinuation of glioblastoma treatment was investigated using odds ratios (OR).ResultsPatients at risk of sarcopenia had a significantly higher risk of progression and death than patients with normal muscle status, which remained significant in the multivariate analyses (OS HR = 1.437; 95%CI: 1.046–1.973; P = .025 and PFS HR = 1.453; 95%CI: 1.037–2.036; P = .030). Patients at risk of sarcopenia also had a significantly higher risk of early discontinuation of treatment (OR = 2.45; 95%CI: 1.011–5.952; P = .042) and a significantly lower chance of receiving second-line treatment (OR = 0.23; 95%CI: 0.09–0.60; P = .001). There was no association with the occurrence of complications.ConclusionsOur study confirms external validity of the use of proposed sex-specific TMT cutoff values as an independent prognostic marker in newly diagnosed glioblastoma patients. This simple, noninvasive marker could improve patient counseling and aid in treatment decision processes or trial stratification.

Immune landscape-based machine-learning-assisted subclassification, prognosis, and immunotherapy prediction for glioblastoma.

As a malignant brain tumor, glioblastoma (GBM) is characterized by intratumor heterogeneity, a worse prognosis, and highly invasive, lethal, and refractory natures. Immunotherapy has been becoming a promising strategy to treat diverse cancers. It has been known that there are highly heterogeneous immunosuppressive microenvironments among different GBM molecular subtypes that mainly include classical (CL), mesenchymal (MES), and proneural (PN), respectively. Therefore, an in-depth understanding of immune landscapes among them is essential for identifying novel immune markers of GBM. In the present study, based on collecting the largest number of 109 immune signatures, we aim to achieve a precise diagnosis, prognosis, and immunotherapy prediction for GBM by performing a comprehensive immunogenomic analysis. Firstly, machine-learning (ML) methods were proposed to evaluate the diagnostic values of these immune signatures, and the optimal classifier was constructed for accurate recognition of three GBM subtypes with robust and promising performance. The prognostic values of these signatures were then confirmed, and a risk score was established to divide all GBM patients into high-, medium-, and low-risk groups with a high predictive accuracy for overall survival (OS). Therefore, complete differential analysis across GBM subtypes was performed in terms of the immune characteristics along with clinicopathological and molecular features, which indicates that MES shows much higher immune heterogeneity compared to CL and PN but has significantly better immunotherapy responses, although MES patients may have an immunosuppressive microenvironment and be more proinflammatory and invasive. Finally, the MES subtype is proved to be more sensitive to 17-AAG, docetaxel, and erlotinib using drug sensitivity analysis and three compounds of AS-703026, PD-0325901, and MEK1-2-inhibitor might be potential therapeutic agents. Overall, the findings of this research could help enhance our understanding of the tumor immune microenvironment and provide new insights for improving the prognosis and immunotherapy of GBM patients.

EFNB1 Acts as a Novel Prognosis Marker in Glioblastoma through Bioinformatics Methods and Experimental Validation.

Purpose Ephrin B1 (EFNB1), the Eph-associated receptor tyrosine kinase ligand, is suggested to have an important function in neurodevelopment. However, its contribution to glioblastoma multiforme (GBM) remains uncertain. This study aimed to determine the prognostic power and immune implication of EFNB1 in GBM. Methods We first identified differentially coexpressed genes within GBM relative to noncarcinoma samples from GEO and TCGA databases by WGCNA. The STRING online database and the maximum cluster centrality (MCC) algorithm in Cytoscape software were used to design for predicting protein-protein interactions (PPI) and calculating pivot nodes, respectively. The expression of hub genes in cancer and noncancer tissues was verified by an online tool gene expression profile interactive analysis (GEPIA). Thereafter, the TISIDB online tool with Cox correlation regression method was employed to screen for immunomodulators associated with EFNB1 and to model the risk associated with immunomodulators. Results Altogether 201 differentially expressed genes (DEGs) were discovered. After that, 10 hub genes (CALB2, EFNB1, ENO2, EPHB4, NES, OBSCN, RAB9B, RPL23A, STMN2, and THY1) were incorporated to construct the PPI network. As revealed by survival analysis, EFNB1 upregulation predicted poor overall survival (OS) for GBM cases. Furthermore, we developed a prognostic risk signature according to the EFNB1-associated immunomodulators. Kaplan–Meier survival analysis and receiver operating characteristic method were adopted for analysis, which revealed that our signature showed favorable accuracy of prognosis prediction. Finally, EFNB1 inhibition was found to block cell proliferation and migration in GBM cells. Conclusion The above results indicate that EFNB1 participates in cancer immunity and progression, which is the candidate biomarker for GBM.

BIOM-07. QUANTITATIVE MGMT PROMOTER METHYLATION INDEX INDICATES A NON-LINEAR PROGNOSTIC EFFECT IN GLIOBLASTOMA, SUGGESTING THAT USE OF OPTIMAL CUTOFF POINTS MAY BE CLINICALLY DISADVANTAGEOUS

Abstract BACKGROUND Epigenetic inhibition of the O6-methylguanine-DNA-methyltransferase (MGMT) gene has emerged as a clinically relevant prognostic marker in glioblastoma (GBM). Methylation of the MGMT promoter has been shown to increase chemotherapy efficacy. While traditionally reported as a binary marker, recent methodological advancements have led to quantitative approaches that measure methylation, providing clearer insights into methylation’s functional relationship with survival. METHODS A CLIA assay and bisulfite sequencing was utilized to develop a quantitative, 17-point MGMT promoter methylation index derived from the number of methylated CpG sites. Retrospective review of 240 newly diagnosed GBM patients was performed in order to discern how risk for mortality transforms as promoter methylation increases. Non-linearities were captured by fitting splines to Cox proportional hazard models, plotting smoothed residuals, and creating survival plots. Covariates included age, KPS, IDH1 mutation, and extent of resection. RESULTS Median follow-up time and progression free survival were 16 and 9 months, respectively. 176 subjects experienced death. A one-unit increase in CpG methylation on a scale of 1-17 resulted in a 4% reduction in hazard (95% CI 0.93–0.99, P&amp;lt; 0.005). Moreover, GBM patients with low-levels of methylation (1-6 CpG sites) fared markedly worse (HR=1.62, 95% CI 1.03-2.54, P&amp;lt; 0.036) than individuals who were unmethylated (reference group). Subjects with medium-levels of methylation (7-12 CpG sites) had the greatest reduction in hazard (HR=0.48, 95% CI 0.29-0.80, P&amp;lt; 0.004), followed by individuals in the highest methylation tertile (HR=0.62, 95% CI 0.40-0.97, P&amp;lt; 0.035). CONCLUSION This novel approach offers greater bisulfite conversion efficiency when compared to alternative methods, reducing the likelihood of false positives. Analysis of the resulting methylation index scores demonstrates a non-linear relationship between MGMT methylation and survival, suggesting conformation of the marker’s protective effect. These findings challenge the current understanding of MGMT’s functional form and underline why implementing an “optimal cutoff point” may be disadvantageous.

AEBP1 as a potential immune-related prognostic biomarker in glioblastoma: a bioinformatic analyses.

BackgroundAdipocyte enhancer binding protein 1 (AEBP1) has been shown to be closely related to cancer progression; however research on its potential role in glioblastoma (GBM) remains limited.MethodsFollowing an expression analysis of AEBP1 in GBM through the Oncomine database, other critical findings were accessed via The Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) databases. Specifically, in addition to identifying differentially expressed genes, the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) were further investigated. Additionally, a gene set enrichment analysis (GSEA) was performed to examine the enrichment pathways in the AEBP1 high-expression group. To examine the prognostic role of AEBP1 in GBM, survival information was obtained from the Chinese Glioma Genome Atlas (CGGA) database. Finally, the relationship between the expression of AEBP1 and immune infiltration in GBM was examined by using the “Gene Module”, “Survival Module”, and “SCNA Module” on the website “Tumor Immune Estimation Resource (TIMER)”.ResultsThe Oncomine database revealed that AEBP1 was highly expressed in GBM. The prognostic analyses of 4 independent databases (i.e., TCGA, GTEx, Oncomine, and CGGA) revealed that AEBP1 was an independent predictable marker of GBM. The results of the GSEA showed that protein export, prion disease, cytokine receptor interaction, hematopoietic cell lineage, cell adhesion molecules, apoptosis, and the complement and coagulation cascades were differentially enriched in highly expressed AEBP1 phenotypes. Hence the conclusion is that the high expression of AEBP1 is closely correlated to poor prognosis of GBM. The immune analysis demonstrated that AEBP1 copy number alteration might affect immune infiltration in GBM tissues, and thus the survival outcomes of GBM patients.ConclusionsHigh AEBP1 expression in GBM is closely correlated to patient prognosis. AEBP1 is a potential therapeutic target for the inhibition of cancerous progression and the development of new immunotherapies for GBM.

Caveolin-1, a Key Mediator Across Multiple Pathways in Glioblastoma and an Independent Negative Biomarker of Patient Survival.

Glioblastoma (GB) remains an aggressive malignancy with an extremely poor prognosis. Discovering new candidate drug targets for GB remains an unmet medical need. Caveolin-1 (Cav-1) has been shown to act variously as both a tumour suppressor and tumour promoter in many cancers. The implications of Cav-1 expression in GB remains poorly understood. Using clinical and genomic databases we examined the relationship between tumour Cav-1 gene expression (including its spatial distribution) and clinical pathological parameters of the GB tumour and survival probability in a TCGA cohort (n=155) and CGGA cohort (n=220) of GB patients. High expression of Cav-1 represented a significant independent predictor of shortened survival (HR = 2.985, 5.1 vs 14.9 months) with a greater statistically significant impact in female patients and in the Proneural and Mesenchymal GB subtypes. High Cav-1 expression correlated with other factors associated with poor prognosis: IDH w/t status, high histological tumour grade and low KPS score. A total of 4879 differentially expressed genes (DEGs) in the GB tumour were found to correlate with Cav-1 expression (either positively or negatively). Pathway enrichment analysis highlighted an over-representation of these DEGs to certain biological pathways. Focusing on those that lie within a framework of epithelial to mesenchymal transition and tumour cell migration and invasion we identified 27 of these DEGs. We then examined the prognostic value of Cav-1 when used in combination with any of these 27 genes and identified a subset of combinations (with Cav-1) indicative of co-operative synergistic mechanisms of action. Overall, the work has confirmed Cav-1 can serve as an independent prognostic marker in GB, but also augment prognosis when used in combination with a panel of biomarkers or clinicopathologic parameters. Moreover, Cav-1 appears to be linked to many signalling entities within the GB tumour and as such this work begins to substantiate Cav-1 or its associated signalling partners as candidate target for GB new drug discovery.

NGMA-6. Quantitative MGMT Promoter Methylation Index Indicates Non-Linear, Prognostic Effect in Glioblastoma

BackgroundEpigenetic inhibition of the O6-methylguanine-DNA-methyltransferase (MGMT) gene has emerged as a clinically relevant prognostic marker in glioblastoma (GBM). Methylation of the MGMT promoter has been shown to increase chemotherapy efficacy. While traditionally reported as a binary marker, recent methodological advancements have led to quantitative methods of measuring methylation, allowing for clearer insights into methylation’s functional relationship with survival.MethodsA CLIA assay and bisulfite sequencing was utilized to develop a quantitative, 17-point MGMT promoter methylation index derived from the number of methylated CpG sites. Retrospective review of 242 newly diagnosed GBM patients was performed in order to discern how risk for mortality transforms as promoter methylation increases. Non-linearities were captured by fitting splines to Cox proportional hazard models, plotting smoothed residuals, and creating survival plots. Covariates included age, KPS, IDH1 mutation, and extent of resection.ResultsMedian follow-up time and progression free survival were 15.9 and 9 months, respectively. 176 subjects experienced death. A one-unit increase in CpG methylation resulted in a 4% reduction in hazard (95% CI 0.93–0.99, P<0.005). Moreover, GBM patients with low levels of methylation (1–6 CpG islands) fared markedly worse (HR=1.62, 95% CI 1.03–2.54, P<0.036) than individuals who were unmethylated (reference group). Subjects with medium levels of methylation (7–12 sites) had the greatest reduction in hazard (HR=0.48, 95% CI 0.29–0.80, P<0.004), followed by individuals in the highest methylation tertile (HR=0.62, 95% CI 0.40–0.97, P<0.035).ConclusionThe extent of MGMT methylation shares a non-linear relationship with survival, suggesting conformation of the marker’s protective effect. This finding challenges the current understanding of MGMT and underlines the clinical importance of determining its prognostic utility. Potential limitations include censoring, sample size, and extraneous mutations. Future research is warranted to examine whether the location of CpG site methylation contributes to a reduction in mortality hazard.

Evaluation of Haptoglobin and Its Proteoforms as Glioblastoma Markers.

Haptoglobin (Hp) is a blood plasma glycoprotein that plays a critical role in tissue protection and the prevention of oxidative damage. Haptoglobin is an acute-phase protein, its concentration in plasma changes in pathology, and the test for its concentration is part of normal clinical practice. Haptoglobin is a conservative protein and is the subject of research as a potential biomarker of many diseases, including malignant neoplasms. The Human Hp gene is polymorphic and controls the synthesis of three major phenotypes—homozygous Hp1-1 and Hp2-2, and heterozygous Hp2-1, determined by a combination of allelic variants that are inherited. Numerous studies indicate that the phenotype of haptoglobin can be used to judge the individual’s predisposition to various diseases. In addition, Hp undergoes various post-translational modifications (PTMs). Glioblastoma multiform (GBM) is the most malignant primary brain tumor. In our study, we have analyzed the state of Hp proteoforms in plasma and cells using 1D (SDS-PAGE) and 2D electrophoresis (2DE) with the following mass spectrometry (LC ES-MS/MS) or Western blotting. We found that the levels of α2- and β-chain proteoforms are up-regulated in the plasma of GBM patients. An unprocessed form of Hp2-2 (PreHp2-2, zonulin) with unusual biophysical parameters (pI/Mw) was also detected in the plasma of GBM patients and glioblastoma cells. Altogether, this data shows the possibility to use proteoforms of haptoglobin as a potential GBM-specific plasma biomarker.