Prognostic Biomarker In Glioblastoma Research Articles

Unraveling molecular signatures and prognostic biomarkers in glioblastoma: a comprehensive study on treatment resistance and personalized strategies

BackgroundGlioblastoma (GBM) is a highly aggressive primary brain tumor with limited treatment success and poor prognosis. Despite surgical resection and adjuvant therapies, GBM often recurs, and resistance to radiotherapy and temozolomide presents significant challenges. This study aimed to elucidate molecular signatures associated with treatment responses, identify potential biomarkers, and enhance personalized treatment strategies for GBM.MethodsWe conducted a comprehensive analysis using the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The GEO dataset (GSE206225) was used to identify differentially expressed genes (DEGs) between radiation-sensitive/resistant and temozolomide-sensitive/resistant GBM samples. TCGA data were utilized for subsequent analyses, including Lasso-Cox regression, risk score model construction, Kaplan–Meier survival analysis, and gene set enrichment analysis (GSEA). Hub genes were identified through survival analysis, and a gene prognostic nomogram was developed. Additionally, validation of the three-gene risk signature through multiple external cohorts and validation of protein expression levels were performed.ResultsDEG analysis identified 111 genes associated with chemoradiotherapy resistance, providing insights into the complex landscape of GBM treatment response. The risk score model effectively stratified patients, showing significant differences in overall survival and progression-free survival. GSEA offered a deeper understanding of pathway activities, emphasizing the intricate molecular mechanisms involved. NNAT, IGFBP6, and CYGB were identified as hub genes, and a gene prognostic nomogram demonstrated predictive accuracy.ConclusionThis study sheds light on the molecular intricacies governing GBM treatment response. The identified hub genes and the gene prognostic nomogram offer valuable tools for predicting patient outcomes and guiding personalized treatment strategies. These findings contribute to advancing our understanding of GBM biology and may pave the way for improved clinical management.

Construction and validation of cell cycle-related prognostic genetic model for glioblastoma.

Glioblastoma (GBM) is a common primary malignant brain tumor and the prognosis of these patients remains poor. Therefore, further understanding of cell cycle-related molecular mechanisms of GBM and identification of appropriate prognostic markers and therapeutic targets are key research imperatives. Based on RNA-seq expression datasets from The Cancer Genome Atlas database, prognosis-related biological processes in GBM were screened out. Gene Set Variation Analysis (GSVA), LASSO-COX, univariate and multivariate Cox regression analyses, Kaplan-Meier survival analysis, and Pearson correlation analysis were performed for constructing a predictive prognostic model. A total of 58 cell cycle-related genes were identified by GSVA and analysis of differential expression between GBM and control samples. By univariate Cox and LASSO regression analyses, 8 genes were identified as prognostic biomarkers in GBM. A nomogram with superior performance to predict the survival of GBM patients was established regarding risk score, cancer status, recurrence type, and mRNAsi. This study revealed the prognostic value of cell cycle-related genes in GBM. In addition, we constructed a reliable model for predicting the prognosis of GBM patients. Our findings reinforce the relationship between cell cycle and GBM and may help improve the prognostic assessment of patients with GBM. Our predictive prognostic model, based on independent prognostic factors, enables tailored treatment strategies for GBM patients. It is particularly useful for subgroups with uncertain prognosis or treatment challenges.

Identification and validation of potential prognostic biomarkers in glioblastoma via the mesenchymal stem cell infiltration level.

Mesenchymal stem cells (MSCs) are key components in promoting glioblastoma (GBM) progression. This study aimed to explore new therapeutic targets and related pathogenic mechanisms based on different MSCs infiltration levels in GBM patients. We estimated the relationship between cell infiltration and prognosis of GBM. Subsequently, key risk genes were identified and prognostic models were constructed by LASSO-Cox analysis. The risk genes were validated by five independent external cohorts, single-cell RNA analysis, and immunohistochemistry of human GBM tissues. TIDE analysis predicted responsiveness to immune checkpoint inhibitors in different risk groups. The MSCs infiltration level was negatively associated with survival in GBM patients. LOXL1, LOXL4, and GUCA1A are key risk genes that promote GBM progression and may act through complex intercellular communication. This research has provided a comprehensive study for exploring the MSCs infiltration environment on GBM progression, which could shed light on novel biomarkers and mechanisms involved in GBM progression.

Knockdown of Anoikis-Associated Gene OCIAD2 Reduces Proliferation and Migration of Glioblastoma Cell Lines.

Glioblastoma (GBM) severely disrupts the quality of life of patients. Anoikis represents a significant mechanism in cancer invasion and metastasis. Our study focused on the prognostic relationship between the anoikis-associated gene and GBM and its effect on GBM cell progression. We downloaded 656 and 979 GBM sample data from TCGA and CGGA cohort datasets, respectively. Fifteen anoikis-associated genes were obtained from the GeneCards database and were subsequently clustered to identify differential genes associated with them. After LAASO screening, the expression values of the 5 differential genes were the sum of LASSO regression coefficients. Survival analysis and ROC curve analysis of anoikis scores were performed using the TCGA training and CGGA validation sets. The prognostic factors were analyzed using Cox regression analysis in GBM. Moreover, CCK-8, colony formation, wound healing, and transwell assay were used to evaluate GBM cell proliferation and migration. Significant differences were observed in the 5-year survival of GBM patients between the two subgroups. Then, our analysis demonstrated that high OCIAD2, FTLP3, IGFBP2, and H19 levels were associated with lower 5-year GBM survival rates, whereas high SFRP2 levels were associated with higher survival rates. Univariate Cox analysis indicated that GBM risk was linked to both anoikis score and grade, while multivariate Cox analysis indicated that GBM risk was associated with both anoikis score and age. Additionally, OCIAD2 was highly expressed in U251MG and T98G cells. Moreover, OCIAD2 silencing inhibited GBM cell proliferation and migration. This study demonstrated the potential of the anoikis-associated gene OCIAD2 as a prognostic biomarker for GBM. Furthermore, we validated in vitro that OCIAD2 promoted GBM cell progression.

T-antigen as a biomarker of progression-free survival in patients with glioblastoma.

Glioblastoma (GBM) is one of the most aggressive brain tumors and often leads to poor outcomes. Studies have indicated that glycan levels are significantly correlated with the pathogenesis and development of cancers. However, whether glycan levels can serve as diagnostic or prognostic biomarkers in GBM remains unclear. We obtained glycomic profiles in tissue and serum samples from 55 individuals with GBM using a well-established lectin biochip platform probing with 11 specific lectins. Our univariate analysis showed that 5 out of the 11 lectin-probed glycans (LPGs) were significantly higher in GBM tissues than in peri-tumoral tissues. After logistic regression analyses, only the Jacalin-probed T-antigen difference between the two groups remained significant (p = 0.037). Moreover, survival-related analyses showed that the level of Jacalin-probed T-antigen was significantly associated with the progression-free survival (p = 0.038) of patients. However, none of the LPG levels were correlated with the overall survival or the chemosensitivity to temozolomide therapy. The correlation coefficient analysis showed a moderate-to-strong correlation in the Jacalin-probed T-antigen levels between GBM tissues and serum samples, indicating its potential usefulness as a non-invasive GBM progression biomarker. Glycomics analyses can be helpful in the prediction of GBM recurrences and may provide information useful for GBM glycan-based target therapies or vaccine development.

BIOM-17. WHOLE GAIN OF CHROMOSOME 19, NOT CO-GAIN OF CHROMOSOMES 19 AND 20, IS A FAVORABLE PROGNOSTIC BIOMARKER IN IDH-WILDTYPE GLIOBLASTOMA

Abstract BACKGROUND IDH-wildtype glioblastoma carries an abysmal prognosis, but long-term survivors exist. Previous studies have suggested that co-gain of chromosomes 19 and 20 (+19/+20) is a favorable prognostic marker. We sought to validate this claim and investigate whether gain of chromosome 19 (+19) is a superior prognostic indicator. METHODS We collected somatic copy number alteration data for primary glioblastoma tumors from TCGA (N = 356) and paired primary (N = 169) and recurrent (N = 167) glioblastoma tumors from GLASS. We assessed the frequency of these events and conducted survival analyses with univariate and multivariate Cox proportional hazard models to examine the association between +19/+20 and +19 and patient outcomes. RESULTS Among primary glioblastomas, the frequency of +19/+20 (20.8% vs. 23.7%) and +19 (34.6% vs. 36.7%) did not differ between TCGA and GLASS. However, the presence of +19/+20 was significantly reduced in GLASS recurrent tumors compared to primary tumors (14.3%, p = 0.04), while +19 without +19/+20 was unchanged (13.8%), indicating that +19/+20 is selected against in recurrent glioblastoma. Tumors harboring +19/+20 did not exhibit improved overall survival (OS) or progression-free survival (PFS) in TCGA (p = 0.21 and p = 0.14, respectively), nor did they demonstrate improved OS in GLASS (p = 0.32). Conversely, tumors with +19 showed significantly better OS and PFS in TCGA (HR = 1.4, p< 0.01; HR = 1.5, p< 0.01, respectively) and improved OS in GLASS (HR = 1.6, p< 0.01). In multivariate survival analyses, +19 remained a prognostic factor independent of +19/+20, patient sex, age, MGMT promoter methylation status, and KPS in TCGA (p< 0.05) and +19/+20 and age in GLASS (p = 0.01). CONCLUSION Although previous studies have suggested that +19/+20 is a positive prognostic biomarker in glioblastoma, our findings indicate that this assertion should be reevaluated. Our results demonstrate that +19 is associated with favorable outcomes in the TCGA and GLASS datasets, whereas +19/+20, while possibly selected against in recurrent glioblastomas, is not.

BIOM-34. NON-NEOPLASTIC PLASMA EV SUBPOPULATIONS CORRELATE WITH TUMOR SIZE AND OVERALL SURVIVAL IN GLIOBLASTOMA INDEPENDENT OF STEROID USAGE

Abstract INTRODUCTION Extracellular Vesicles (EVs) are lipid bilayer particles present in almost all biofluids and carry tumor-specific molecular cargo. Plasma EVs are promising sources of biomarker discovery for glioblastoma (GBM). The clinical relevance of non-neoplastic plasma EVs in GBM remains unclear. In this study, we determine if associations exist between plasma EV subpopulation frequencies in GBM and clinical factors. METHODS Platelet-free plasma samples of 63 GBM, 43 Grade 2 glioma, and 45 healthy donors were included. All glioma plasma samples were collected before surgery. Plasma EV subpopulation frequency based on surface expression of EV-associated tetraspanins (CD9/CD81/CD63) and markers of cell origin (CD45/CD41a/CD11b/CD31) was determined by spectral flow cytometry. Clinical information including age, surgery, MGMT methylation status, corticosteroid usage, tumor volume, and overall survival data was recorded. RESULTS Compared to normal donors, GBM patients had increased CD9+, CD63+, CD81+, and CD11b+ EVs but decreased CD41a+ EVs. GBM patients also had the highest frequency of myeloid-derived CD9+CD11b+ EVs (20.17%) followed by grade 2 gliomas (13.78%) and normal donors (2.76%, p< 0.001). More GBM patients (29/63, 46.0%) received preoperative corticosteroids than grade 2 glioma patients (9/43, 20.9%; p = 0.008). Corticosteroid use was not associated with differences in EV subpopulation frequencies in GBM or grade 2 glioma patients. Pearson correlation analysis showed weak but statistically significant correlations between GBM total tumor volume and CD9+ EVs (p = 0.014), CD41a+ EVs (p = 0.037), and CD9+/CD63-/CD81- EVs (p = 0.007). In GBM patients, younger age (< 60 yrs, p = 0.01), gross total resection (p = 0.02), MGMT methylation (p = 0.002), high CD9+ EV expression (p = 0.011), low CD9+CD63-CD81- EV expression (p = 0.009) and low CD9+CD11b+ EV expression (p = 0.006) correlated with longer overall survival. CONCLUSION Differentially expressed non-neoplastic plasma EV subpopulations have significant clinical relevance in GBM including association with tumor volume and predicting survival. Validation of those markers in larger cohorts could reveal new prognostic biomarkers for GBM.

β3GNT9 as a prognostic biomarker in glioblastoma and its association with glioblastoma immune infiltration, migration and invasion.

Studies have shown that the immune infiltration of tumor microenvironment is related to the prognosis of glioblastoma, which is characterized by high heterogeneity, high recurrence rate and low survival rate. To unravel the role of β1,3-N-acetylglucosaminyltransferase-9 (β3GNT9) in the progression of glioblastoma, this study identifies the value of β3GNT9 as a prognostic biomarker in glioblastoma, and investigates the relationship between β3GNT9 expression and glioblastoma immune infiltration, migration and invasion. β3GNT9 expression in glioblastoma was analyzed using the GEPIA database. The clinical features of glioblastoma were screened out from the TCGA database. The relationship between β3GNT9 expression and clinical features was analyzed. The relationship between β3GNT9 and the prognosis of glioblastoma was evaluated through univariate and multivariate COX regression analyses, and the survival analysis was conducted using the Kaplan-Meier method. GSEA was employed to predict the signaling pathway of β3GNT9 in glioblastoma. The correlation between β3GNT9 and tumor immune infiltration was analyzed using the related modules of CIBERSORT and TIMER. A172, U87MG and U251 cell lines were selected to verify β3GNT9 expression in vitro. The effects of β3GNT9 on the migration and invasion of glioblastoma were investigated through cell scratch and invasion assays. β3GNT9 expression in glioblastoma group was significantly higher than that in normal brain tissue group (P<0.05). The overall survival rate in high β3GNT9 expression group was significantly lower than that in low β3GNT9 expression group (P<0.05). Regression analyses suggested that β3GNT9, involved primarily in glucosamine degradation and extracellular matrix receptor interaction, could be an independent prognostic factor for glioblastoma. CIBERSORT and GEPIA database analyses showed that β3GNT9 was correlated with tumor infiltrating immune cells such as T follicular helper cells, activating natural killer cells, monocytes, macrophages, and eosinophils, thus affecting the immune microenvironment of glioblastoma. Cell experiments confirmed that β3GNT9 was highly expressed in A172, U87MG and U251 cell lines (P<0.05), and promoted the migration and invasion of glioblastoma (P<0.05). The increased expression of β3GNT9 in glioblastoma can affect the immune microenvironment of glioblastoma and promote its migration and invasion. β3GNT9 can be used as a potential independent prognostic biomarker for patients with glioblastoma.

Novel exosome-related risk signature as prognostic biomarkers in glioblastoma.

Exosomes are progressively being detected as an indicator for the diagnosis and prognosis of cancer in clinical settings. Many clinical trials have confirmed the impact of exosomes on tumor growth, particularly in anti-tumor immunity and immunosuppression of exosomes. Therefore, we developed a risk score based on genes found in glioblastoma-derived exosomes. In this study, we used the TCGA dataset as the training queue and GSE13041, GSE43378, GSE4412, and CGGA datasets as the external validation queue. Based on machine algorithms and bioinformatics methods, an exosome-generalized risk score was established. We found that the risk score could independently predict the prognosis of patients with glioma, and there were significant differences in the outcomes of patients in the high- and low-risk groups. Univariate and multivariate analyses showed that risk score is a valid predictive biomarker for gliomas. Two immunotherapy datasets, IMvigor210 and GSE78220, were obtained from previous studies. A high-risk score showed a significant association with multiple immunomodulators that could act on cancer immune evasion. The exosome-related risk score could predict the effectiveness of anti-PD-1 immunotherapy. Moreover, we compared the sensitivity of patients with high- and low-risk scores to various anti-cancer drugs and found that patients with high-risk scores had better responses to a variety of anti-cancer drugs. The risk-scoring model established in this study provides a useful tool to predict the total survival time of patients with glioma and guide immunotherapy.

Flotillin-1 is a prognostic biomarker for glioblastoma and promotes cancer development through enhancing invasion and altering tumour microenvironment.

Flotillin-1(FLOT1) has long been recognized as a tumour-promoting gene in several types of cancer. However, the expression and function of FLOT1 in glioblastomas (GBM) has not been elucidated. Here, in this study, we find that the expression level of FLOT1 in GBM tissue was much higher than that in normal brain, and the expression was even higher in the more aggressive subtypes and IDH status of glioma. Kaplan-Meier survival revealed that high FLOT1 expression is closely associated with poor outcome in GBM patients. FLOT1 knockdown markedly reduced the proliferation, migration and invasiveness of GBM cells, while FLOT1 overexpression significantly increases GBM cell proliferation, migration and invasiveness. Mechanistically, FLOT1 expression may play a potential role in the microenvironment of GBM. Therefore, FLOT1 promotes GBM proliferation and invasion in vitro and in vivo and may serve as a biomarker of prognosis and therapeutic potential in the fight against GBM.

E3 ubiquitin ligase TRIM21 targets TIF1γ to regulate β-catenin signaling in glioblastoma.

Background: Elucidation of the mechanism of ubiquitation has led to novel ways to treat glioblastoma (GBM). A tripartite motif (TRIM) protein mediates a reversible, stringent ubiquitation which is closely related to glioma malignancy. This study intends to screen the most vital and abnormal regulating component of the tripartite motif protein and to explore its underlying mechanisms. Methods: TRIM21 is identified as an important oncogene that accelerates the progression of glioma cell through database in a multidimensional way and this is confirmed in human samples and cells. Tandem Mass Tags (TMT) and MS analysis are performed to discover the substrates of TRIM21.The underlying mechanisms are further investigated by CO-IP, luciferase reporter assays and gain and loss of function assays. In vivo treatment with siRNA is applied to evaluate the therapeutic significance of TRIM21. Result: We screened a panel of TRIM proteins and identified TRIM21, a E3 ubiquitin-protein ligase and autoantigen, as well as a prognostic biomarker for GBM. Functionally, high expression of wild-type TRIM21 accelerates tumor progression in vitro and in vivo, whereas TRIM21 mutants, including one with a critical RING-finger deletion, do not. Mechanistically, TRIM21 stimulates K63-linked ubiquitination and subcellular translocation of active β-catenin from the cytoplasm to the nucleus. Moreover, TRIM21 forms a complex with the β-catenin upstream regulator, TIF1γ, in the nucleus and accelerated its degradation by inducing K48-linked ubiquitination at K5 site, consequently increasing further nuclear β-catenin presence. Endogenous TRIM21 levels are found to be inversely correlated with TIF1γ but positively correlated with β-catenin in glioma tissue microarray experiments. Furthermore, direct injection of TRIM21 small interfering RNA (siRNA) into U87 cell-derived tumors (in vivo treatment with siRNA) is proved to inhibit tumor growth in nude mice. Conclusion: This work suggests that TRIM21/TIF1γ/β-catenin axis is involved in the progression of human GBM. TRIM21 is a promising therapeutic and prognostic biomarker for glioma with hyperactive β-catenin.

P11.24.A The anisotropic component of the decomposed diffusion tensor predicts overall survival in patients with glioblastoma

Abstract Background The diffusion tensor can be decomposed into isotropic (DTI-p) and anisotropic (DTI-q) components (Peña et al., 2006). Regions of DTI-q abnormality have a high tumour burden and increased surgical resection of abnormal DTI-q positively correlates with overall survival (OS) (Yan et al., 2017). We aimed to establish if median voxel DTI-q (MVQ) or a distribution measure of DTI-q (DMQ) could act as a neuro-imaging biomarker, to predict OS in patients with glioblastoma. Material and Methods Diffusion tensor decomposition was used to create DTI-p and DTI-q maps, using FSL software (FMRIB Software Library, Oxford). MVQ and DMQ (the 95th centile minus the 5th centile of the DTI-q distribution, divided by the MVQ) were calculated from the preoperative whole brain (WB), contrast-enhancing (CE), and non-contrast-enhancing (NCE) hemisphere DTI-q maps, using fslstats, for 33 patients with glioblastoma. Using R Studio, multiple linear regression (MLR) models were computed to establish if MVQ or DMQ of the WB, CE and NCE hemispheres or age, significantly predicts OS. The Breusch-Pagan Test, on package “lmtest” in R, was calculated for all MLR models, to determine if heteroscedasticity was present and, if so, bootstrapped multiple regression (BMR) models were computed using package “boot” in R. Results Evidence for heteroscedasticity was present in MLRs that modelled the relationship between DMQ of WB, age, and OS (BP = 6.032, p = 0.014) and DMQ of CE hemisphere, age, and OS (BP = 7.163, p = 0.028). In the BMR of WB DMQ, age, and OS, the 95% bias-corrected accelerated confidence intervals (BCa-CI) for the WB DMQ regression coefficient was 133.5 - 1851.4 and included the WB DMQ estimated coefficient of 803.9. The BMR of CE hemisphere DMQ, age, and OS, computed a 95% BCa-CI for the CE hemisphere DMQ coefficient of 101.8 - 1579.6, containing the CE hemisphere DMQ coefficient estimate of 612.414. For both BMRs, the 95% BCa-CI for age coefficients crossed a value of 0. From computed MLR models, WB MVQ (t = -2.569, p = 0.015), CE hemisphere MVQ (t = -2.143, p = 0.040), NCE hemisphere MVQ (t = -2.567, p = 0.015) and NCE hemisphere DMQ (t = 2.39, p = 0.024) were significant predictors of OS. Age did not significantly predict OS in any models and was not significantly related to WB, CE and NCE hemisphere MVQ or DMQ. Conclusion WB, CE and NCE hemisphere MVQ and DMQ predict OS in our tested subgroup of patients with glioblastoma. Age is not a significant predictor of OS and does not significantly correlate with MVQ or DMQ. The median value and distribution spread of DTI-q may act as a prognostic biomarker in glioblastoma, facilitating patient stratification.

Development of a Novel Prognostic Model of Glioblastoma Based on m6A-Associated Immune Genes and Identification of a New Biomarker.

BackgroundAccumulating evidence shows that m6A regulates oncogene and tumor suppressor gene expression, thus playing a dual role in cancer. Likewise, there is a close relationship between the immune system and tumor development and progression. However, for glioblastoma, m6A-associated immunological markers remain to be identified.MethodsWe obtained gene expression, mutation, and clinical data on glioblastoma from The Cancer Genome Atlas and Chinese Glioma Genome Atlas databases. Next, we performed univariate COX–least absolute shrinkage and selection operator (LASSO)–multivariate COX regression analyses to establish a prognostic gene signature and develop a corresponding dynamic nomogram application. We then carried out a clustering analysis twice to categorize all samples according to their m6A-regulating and m6A-associated immune gene expression levels (high, medium, and low) and calculated their m6A score. Finally, we performed quantitative reverse transcription-polymerase chain reaction, cell counting kit-8, cell stemness detection, cell migration, and apoptosis detection in vitro assays to determine the biological role of CD81 in glioblastoma cells.ResultsOur glioblastoma risk score model had extremely high prediction efficacy, with the area under the receiver operating characteristic curve reaching 0.9. The web version of the dynamic nomogram application allows rapid and accurate calculation of patients’ survival odds. Survival curves and Sankey diagrams indicated that the high-m6A score group corresponded to the groups expressing medium and low m6A-regulating gene levels and high m6A-associated prognostic immune gene levels. Moreover, these groups displayed lower survival rates and higher immune infiltration. Based on the gene set enrichment analysis, the pathophysiological mechanism may be related to the activation of the immunosuppressive function and related signaling pathways. Moreover, the risk score model allowed us to perform immunotherapy benefit assessment. Finally, silencing CD81 in vitro significantly suppressed proliferation, stemness, and migration and facilitated apoptosis in glioblastoma cells.ConclusionWe developed an accurate and efficient prognostic model. Furthermore, the correlation analysis of different stratification methods with tumor microenvironment provided a basis for further pathophysiological mechanism exploration. Finally, CD81 may serve as a diagnostic and prognostic biomarker in glioblastoma.

LncRNA-FAM66C Was Identified as a Key Regulator for Modulating Tumor Microenvironment and Hypoxia-Related Pathways in Glioblastoma.

Although the role of hypoxia has been greatly explored and unveiled in glioblastoma (GBM), the mechanism of hypoxia-related long non-coding (lnc) RNAs has not been clearly understood. This study aims to reveal the crosstalk among hypoxia-related lncRNAs, tumor microenvironment (TME), and tumorigenesis for GBM. Gene expression profiles of GBM patients were used as a basis for identifying hypoxia-related lncRNAs. Unsupervised consensus clustering was conducted for classifying samples into different molecular subtypes. Gene set enrichment analysis (GSEA) was performed to analyze the enrichment of a series of genes or gene signatures. Three molecular subtypes were constructed based on eight identified hypoxia-related lncRNAs. Oncogenic pathways, such as epithelial mesenchymal transition (EMT), tumor necrosis factor-α (TNF-α) signaling, angiogenesis, hypoxia, P53 signaling, and glycolysis pathways, were significantly enriched in C1 subtype with poor overall survival. C1 subtype showed high immune infiltration and high expression of immune checkpoints. Furthermore, we identified 10 transcription factors (TFs) that were highly correlated with lncRNA-FAM66C. Three key lncRNAs (ADAMTS9-AS2, LINC00968, and LUCAT1) were screened as prognostic biomarkers for GBM. This study shed light on the important role of hypoxia-related lncRNAs for TME modulation and tumorigenesis in GBM. The eight identified hypoxia-related lncRNAs, especially FAM66C may serve as key regulators involving in hypoxia-related pathways.

Androgen Receptor Activity Is Associated with Worse Survival in Glioblastoma.

Some evidence about the role of the androgen receptor (AR) in pathogenesis of glioblastoma have been reported, but no study has focused on measuring the activity of the AR in GB. Therefore, the aim of this work is to study the role of AR and its activity as prognostic biomarkers in glioblastoma (GB). Molecular and clinical data from The Cancer Genome Atlas database were used. The AR-expression at protein-level was obtained from reversed phase protein array (RPPA) assays. The AR-activity was determined by calculating the AR-score, an index calculated by using the expression (at RNA-level) of 13 androgen-responsive-genes. Univariate and multivariate Cox-regression analyses were performed. Finally, a correlation analysis was conducted between protein expression data and the AR-score. Two-hundred and thirty-three patients were included. RPPA data showed a mean AR abundance of 0.027(Statistical Deviation = 0.38) in GB. The univariate Cox-regression analysis showed that the AR-Score was associated with a worse prognosis (Hazard Ratio (HR) = 1.070) while the AR-expression did not show any relationship with survival (HR = 0.869). The association of the AR-score with worse overall survival (OS) was still significant in the multivariate analysis (HR = 1.054). The highest correlation coefficients between the AR-score and RPPA were identified in a group of proteins involved in apoptotic process regulation. GB patients with a high AR-activity present a worse prognosis in terms of OS. Thus, the activity of the AR may have a pathogenic role in GB. In this regard, the activation of the AR in GB may be associated with a dysregulation of apoptosis.

Expression profile and prognostic values of Chromobox family members in human glioblastoma

Glioblastoma (GBM) is the most common and deadly malignant primary brain tumor. Chromobox (CBX) family proteins are essential components of the epigenetic regulatory complex and are involved in the occurrence and development of various cancers. However, the roles of CBX members in GBM is little known. In this analysis, we synthesized several mainstream bioinformatics databases to comprehensively explore the expression profiles, prognostic implications, genetic alterations, immune infiltration, and potential biological functions of the CBXs in GBM, and cell experiments were also conducted to investigate the role of CBX8 in GBM. We found that the elevated mRNA expression of CBX2/3/5/8 and reduced mRNA expression of CBX6/7 were found in GBM. The protein levels of CBX2/3/5/8 were elevated in GBM tissues, whereas the protein levels of CBX6/7 showed no significant difference. The upregulated expression of CBX2/3/8 was found to be both correlated with the tumor grade and recurrent status. The overexpression of CBX3/8 and underexpression of CBX6 mRNA were associated with the poor prognosis. These findings suggested that CBX3 and CBX8 might be useful diagnostic and prognostic biomarkers in GBM. Further cell experiment results supported that CBX8 promoted the proliferation of glioma cells. Moreover, a high genetic alteration rate of CBXs (37%) was found in GBM and to varying degrees. The expression of CBXs was significantly related to the immune cells infiltration. CBX7 methylation level was significantly increased in GBM tissues. Our results may provide novel ideas to find potential prognostic markers and new therapeutic targets among CBX family members in glioblastoma.

Bioinformatic identification of differentially expressed genes regulated by DNA-methylation in glioblastoma.

DNA methylation-driven differentially expressed genes (DEGs) play potentially important roles in glioblastoma (GBM). In the present study, we applied bioinformatic analyses to identify key methylation-regulated DEGs (MeDEGs) in glioblastoma and elucidate their functions. Gene expression and methylation profile data from glioblastoma samples along with clinical information were obtained from GEO and TCGA databases. A total of 65 genes were identified as MeDEGs from the aforementioned data. Subsequently, gene ontology and kyoto encyclopaedia of genes and genomes enrichment analyses of these MeDEGs exhibited that MeDEGs were mostly enriched in several tumour-related terms such as 'activation of cysteine-type endopeptidase activity involved in apoptotic process' and 'phospholipid scrambling'. Kaplan-Meier survival analysis demonstrated significant correlation of CASP1, CFH and TTLL7 hyper-methylation with patient prognosis. Finally, CASP1 protein could indirectly interact with CFH protein, but interaction of TTLL7 protein with CASP1 or CFH protein was not evident. Based on gene set enrichment analysis, hyper-methylation of CASP1, CFH and TTLL7 were found enriched in tumour-related KEGG terms, such as 'RNA degradation', 'apyruvate metabolism' and 'nitrogen metabolism'. Methylation levels of CASP1, CFH and TTLL7 were addressed to negatively correlate with their mRNA levels in GBM cell lines. In sum, the present identification of MeDEGs associated with overall survival put forth potential molecular targets for translation towards improved diagnosis and treatment of GBM, and specifically, methylation levels of CASP1, CFH and TTLL7 genes could serve as key prognostic biomarkers in GBM.

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.

Radiologically Defined Tumor-habitat Adjacency as a Prognostic Biomarker in Glioblastoma.

Intratumor heterogeneity in glioblastoma (GBM) has been linked to adverse clinical outcomes including poor survival and sub-optimal response to therapies. Different techniques, such as radiomics, have been used to characterize GBM phenotype. However, the spatial diversity and the interaction between different sub-regions within the tumor (habitats) and its microenvironment has been relatively unexplored. Besides, existing approaches have mainly focused on the radiomic analysis within globally defined regions without considering local heterogeneity. In this paper, we developed a 3D spatial co-localization descriptor based on the adjacency of "habitats" to quantify the diversity of physiologically similar sub-regions on multi-protocol magnetic resonance imaging. We demonstrated the utility of this spatial phenotype descriptor in predicting overall patient survival. Our experimental results on N=236 treatment-naïve MRI scans suggest that the co-localization features in conjunction with traditional clinical measures, such as age and tumor volume, outperform texture based radiomic features. The presented descriptor provides a tool for more complete characterization of intratumor heterogeneity in solid cancers.

Construction and comprehensive analysis of a competitive endogenous RNA network to reveal potential biomarkers for the malignant differentiation of glioma.

Background:Long noncoding RNAs (lncRNAs) can act as microRNA (miRNA) sponges to regulate protein-coding gene expression; therefore, lncRNAs are considered major components of the competitive endogenous RNA (ceRNA) network and have attracted growing attention. This study explored the regulatory mechanisms and functional roles of lncRNAs as ceRNAs in the malignant differentiation of low-grade glioma (LGG) to glioblastoma (GBM) and their potential impact on the prognosis of patients with GBM.Methods:LncRNA and messenger RNA (mRNA) data were extracted from the Cancer Genome Atlas (TCGA) database from 156 GBM samples and 529 LGG samples. Separately, the miRNA expression data were downloaded from the Gene Expression Omnibus database, with the GSE112009 dataset containing miRNA expression data from 10 GBM samples and 15 LGG samples. Weighted gene coexpression network analysis was performed to screen the glioma grade-related lncRNAs. Then, a ceRNA network was established. The database for annotation, visualization, and integrated discovery was adopted to conduct functional enrichment analysis based on 57 upregulated differentially expressed mRNAs in the ceRNA network. Finally, Kaplan–Meier curves were created for the survival analysis of 13 hub lncRNA by combining the clinical data of GBM patients in TCGA.Results:A ceRNA network including 16 lncRNAs, 18 miRNAs, and 78 mRNAs specific to the malignant differentiation of LGG to GBM was established. The 57 upregulated differentially expressed mRNAs in the ceRNA network were significantly enriched in 35 gene ontology terms and 5 pathways. The survival analysis showed that 2 lncRNAs (LINC00261 and HOXA10-AS) were prognostic biomarkers for patients with GBM in TCGA.Conclusion:The proposed ceRNA network may help elucidate the regulatory mechanism by which lncRNAs function as ceRNAs and contribute to the malignant differentiation of LGG to GBM. Importantly, the candidate lncRNAs, miRNAs, and mRNAs involved in the ceRNA network can be further evaluated as potential therapeutic targets and prognostic biomarkers for GBM.