Survival Prediction in Patients With Glioblastoma Multiforme by Human Telomerase Genetic Variation

Purpose Glioblastoma multiforme (GBM) is the most common and aggressive glioma with the poorest survival. Use of biomarkers for screening patients with GBM may be used to modify treatments and improve outcomes. The level of human telomerase (hTERT) expression is an independent predictor of outcome of many cancers, and a functional variant of hTERT MNS16A (shorter tandem repeats or short [S] allele) is associated with increased hTERT mRNA expression. We investigated whether hTERT MNS16A variant genotype predicted survival in GBM patients. Patients and Methods We genotyped hTERT MNS16A in 299 GBM patients using polymerase chain reaction and determined hTERT genotype by classifying the DNA band of 243 or 272 base pairs (bp) as S allele and 302 or 333 bp as long (L) allele. We compared overall survival using Kaplan-Meier estimates and equality of survival distributions using the log-rank test, and we computed univariate and multivariate Cox proportional hazards models to estimate the effects of selected variables. Results Overall survival differed significantly by hTERT MNS16A genotype, with median survivals of 25.1, 14.7, and 14.6 months for the SS, SL, and LL genotypes, respectively. Compared with the SS genotype, the hazard ratios for the SL and LL genotypes were 1.69 and 1.87, respectively, after adjustment for other factors. Multivariate Cox regression analysis showed an independent statistically significant association between the hTERT MNS16A variant genotype and outcome. Conclusion A functional hTERT MNS16A genotype is a potential biomarker for assessment of survival outcome of GBM. Larger studies are needed to verify these findings.

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A Novel Six-mRNA Signature Predicts Survival of Patients With Glioblastoma Multiforme

Frontiers in Genetics ◽

10.3389/fgene.2021.634116 ◽

2021 ◽

Vol 12 ◽

Author(s):

Zhentao Liu ◽

Hao Zhang ◽

Hongkang Hu ◽

Zheng Cai ◽

Chengyin Lu ◽

...

Keyword(s):

Glioblastoma Multiforme ◽

Cox Regression ◽

Methylation Status ◽

Survival Rates ◽

Gene Expression Omnibus ◽

The Cancer Genome Atlas ◽

Survival Prediction ◽

Lasso Regression ◽

Cox Regression Analysis ◽

Score Model

Glioblastoma multiforme (GBM) is a devastating brain tumor and displays divergent clinical outcomes due to its high degree of heterogeneity. Reliable prognostic biomarkers are urgently needed for improving risk stratification and survival prediction. In this study, we analyzed genome-wide mRNA profiles in GBM patients derived from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify mRNA-based signatures for GBM prognosis with survival analysis. Univariate Cox regression model was used to evaluate the relationship between the expression of mRNA and the prognosis of patients with GBM. We established a risk score model that consisted of six mRNA (AACS, STEAP1, STEAP2, G6PC3, FKBP9, and LOXL1) by the LASSO regression method. The six-mRNA signature could divide patients into a high-risk and a low-risk group with significantly different survival rates in training and test sets. Multivariate Cox regression analysis confirmed that it was an independent prognostic factor in GBM patients, and it has a superior predictive power as compared with age, IDH mutation status, MGMT, and G-CIMP methylation status. By combining this signature and clinical risk factors, a nomogram can be established to predict 1-, 2-, and 3-year OS in GBM patients with relatively high accuracy.

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miR-181 Expression is Associated With The Prognosis in Lung Cancer.

10.21203/rs.3.rs-44942/v1 ◽

2020 ◽

Author(s):

Yue Zhao ◽

Xiangjun Kong ◽

Hongbing Wang

Keyword(s):

Lung Cancer ◽

Overall Survival ◽

Prognostic Value ◽

Cox Regression ◽

Suppressor Gene ◽

Rank Test ◽

High Morbidity ◽

Cox Regression Analysis ◽

Log Rank Test ◽

Cancer Tissues

Abstract Background: Lung cancer is one of the most common cancers, with high morbidity and mortality. MiRNAs are proved to play important roles in various human cancers. In our study, we aimed to explore the prognostic value of miR-181 in lung cancerMethods: Quantitative real-time polymerase chain reaction (QRT-PCR) was used to detect the expression level of miR-181 in lung cancer tissues and the paired non-cancerous tissues. The relationship between miR-181 expression and clinicopathologic parameters were analyzed by chi-square test. Kaplan-Meier method with log rank test was applied for overall survival analysis. Furthermore, the Cox regression analyses were performed to evaluate the prognostic value of miR-181 in lung cancer.Results: Down-regulated miR-181 expression was observed in lung cancer tissues (P<0.001), moreover, its expression was significantly correlated with TNM stage (P=0.015) and metastasis (P=0.000). In addition, lung cancer patients with lower miR-181 expression level had poorer overall survival than those with higher expression (log rank test, P=0.011). Cox regression analysis suggested that miR-181 was an independent prognostic factor for lung cancer (HR=1.961, 95%CI=1.135-3.388, P=0.016).Conclusion: MiR-181 may be a tumor suppressor gene in lung cancer, which can predict outcomes for the patients.

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Clinicopathological and molecular differences in colorectal cancer according to location

The International Journal of Biological Markers ◽

10.1177/1724600818807164 ◽

2019 ◽

Vol 34 (1) ◽

pp. 47-53 ◽

Cited By ~ 5

Author(s):

Yu-Lun Hsu ◽

Chun-Chi Lin ◽

Jeng-Kai Jiang ◽

Hung-Hsin Lin ◽

Yuan-Tzu Lan ◽

...

Keyword(s):

Colorectal Cancer ◽

Colon Cancer ◽

Overall Survival ◽

Cox Regression ◽

Rank Test ◽

Advanced Tumor Stage ◽

Tumor Seeding ◽

Cox Regression Analysis ◽

Advanced Tumor ◽

Tumor Node Metastasis Stage

Purpose: The incidence, pathogenesis, molecular pathways, and outcomes of colorectal cancer vary depending on the location of the tumor. This study aimed to compare the difference in tumor characteristics and the outcome between right-sided colon cancer and left-sided colorectal cancer (LCRC). Materials and methods: A total of 1503 patients with colorectal cancer who underwent surgery at the Taipei Veterans General Hospital between 2000 and 2010 were enrolled in this study. Right-sided colon cancer was defined as cancers in the cecum, ascending colon, and transverse colon, while LCRC was defined as cancers in the splenic flexure colon, descending colon, sigmoid colon, and rectum. The endpoint was overall survival. The mutations were detected via polymerase chain reaction and MASS array. The prognostic value was determined using the log-rank test and the Cox regression analysis. Results: A total of 407 and 1096 cases were classified as right-sided colon cancer and LCRC, respectively. Compared to patients with LCRC, those with right-sided colon cancer had more mucinous type cancer (7.4% vs. 3.5%), poorly differentiated tumor (11.5% vs. 3.6%), and advanced tumor-node-metastasis stage. The risk for peritoneal tumor seeding was higher in the right-sided colon cancer group (12.8% vs. 5.7%). Overall survival was better in LCRC than in right-sided colon cancer ( P=0.036). Conclusions: In our study, right-sided colon cancer had a more advanced tumor stage, a higher risk of peritoneal metastasis, and a poorer outcome than LCRC. Moreover, right-sided colon cancer had more gene mutations in BRAF, KRAS, SMAD4, TGF-β, PIK3CA, PTEN, AKT1, and high microsatellite instability.

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Analysis of Immune-Related Signatures Related to CD4+ T Cell Infiltration With Gene Co-Expression Network in Pancreatic Adenocarcinoma

Frontiers in Oncology ◽

10.3389/fonc.2021.674897 ◽

2021 ◽

Vol 11 ◽

Author(s):

Zhen Tan ◽

Yubin Lei ◽

Bo Zhang ◽

Si Shi ◽

Jiang Liu ◽

...

Keyword(s):

Overall Survival ◽

Molecular Mechanisms ◽

Cox Regression ◽

Risk Groups ◽

Gene Signature ◽

Gene Expression Omnibus ◽

Ductal Adenocarcinoma ◽

Rank Test ◽

Cancer Center ◽

Cox Regression Analysis

BackgroundPancreatic ductal adenocarcinoma (PDAC) is one of the most invasive solid malignancies. Immunotherapy and targeted therapy confirmed an existing certain curative effect in treating PDAC. The aim of this study was to develop an immune-related molecular marker to enhance the ability to predict Stages III and IV PDAC patients.MethodIn this study, weighted gene co-expression network (WGCNA) analysis and a deconvolution algorithm (CIBERSORT) that evaluated the cellular constituent of immune cells were used to evaluate PDAC expression data from the GEO (Gene Expression Omnibus) datasets, and identify modules related to CD4+ T cells. LASSO Cox regression analysis and Kaplan–Meier curve were applied to select and build prognostic multi-gene signature in TCGA Stages III and IV PDAC patients (N = 126). This was followed by independent Stages III and IV validation of the gene signature in the International Cancer Genome Consortium (ICGC, N = 62) and the Fudan University Shanghai Cancer Center (FUSCC, N = 42) cohort. Inherited germline mutations and tumor immunity exploration were applied to elucidate the molecular mechanisms in PDAC. Univariate and Multivariate Cox regression analyses were applied to verify the independent prognostic factors. Finally, a prognostic nomogram was created according to the TCGA-PDAC dataset.ResultsA four-gene signature comprising NAPSB, ZNF831, CXCL9 and PYHIN1 was established to predict overall survival of PDAC. This signature also robustly predicted survival in two independent validation cohorts. The four-gene signature could divide patients into high and low-risk groups with disparity overall survival verified by a Log-rank test. Expression of four genes positively correlated with immunosuppression activity (PD-L1 and PD1). Immune-related genes nomogram and corresponding calibration curves showed significant performance for predicting 3-year survival in TCGA-PDAC dataset.ConclusionWe constructed a novel four-gene signature to predict the prognosis of Stages III and IV PDAC patients by applying WGCNA and CIBERSORT algorithm scoring to transcriptome data different from traditional methods of filtrating for differential genes in cancer and healthy tissues. The findings may provide reference to predict survival and was beneficial to individualized management for advanced PDAC patients.

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Integrated Analysis of lncRNA-Associated ceRNA Network Identifies Two lncRNA Signatures as a Prognostic Biomarker in Gastric Cancer

Disease Markers ◽

10.1155/2021/8886897 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Shuyan Zhang ◽

Shanshan Li ◽

Jian-Lin Guo ◽

Ningyi Li ◽

Cai-Ning Zhang ◽

...

Keyword(s):

Gastric Cancer ◽

Overall Survival ◽

Proportional Hazards ◽

Cox Regression ◽

Cox Proportional Hazards ◽

Integrated Analysis ◽

Survival Prediction ◽

Survival Prognosis ◽

Cox Regression Analysis ◽

Cerna Network

Background. Gastric cancer (GC) is a malignant tumour that originates in the gastric mucosal epithelium and is associated with high mortality rates worldwide. Long noncoding RNAs (lncRNAs) have been identified to play an important role in the development of various tumours, including GC. Yet, lncRNA biomarkers in a competing endogenous RNA network (ceRNA network) that are used to predict survival prognosis remain lacking. The aim of this study was to construct a ceRNA network and identify the lncRNA signature as prognostic factors for survival prediction. Methods. The lncRNAs with overall survival significance were used to construct the ceRNA network. Function enrichment, protein-protein interaction, and cluster analysis were performed for dysregulated mRNAs. Multivariate Cox proportional hazards regression was performed to screen the potential prognostic lncRNAs. RT-qPCR was used to measure the relative expression levels of lncRNAs in cell lines. CCK8 assay was used to assess the proliferation of GC cells transfected with sh-lncRNAs. Results. Differentially expressed genes were identified including 585 lncRNAs, 144 miRNAs, and 2794 mRNAs. The ceRNA network was constructed using 35 DElncRNAs associated with overall survival of GC patients. Functional analysis revealed that these dysregulated mRNAs were enriched in cancer-related pathways, including TGF-beta, Rap 1, calcium, and the cGMP-PKG signalling pathway. A multivariate Cox regression analysis and cumulative risk score suggested that two of those lncRNAs (LINC01644 and LINC01697) had significant prognostic value. Furthermore, the results indicate that LINC01644 and LINC01697 were upregulated in GC cells. Knockdown of LINC01644 or LINC01697 suppressed the proliferation of GC cells. Conclusions. The authors identified 2-lncRNA signature in ceRNA regulatory network as prognostic biomarkers for the prediction of GC patient survival and revealed that silencing LINC01644 or LINC01697 inhibited the proliferation of GC cells.

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A nomogram to predict outcomes of lung cancer patients after pneumonectomy based on 47 indicators.

Journal of Clinical Oncology ◽

10.1200/jco.2019.37.15_suppl.e20014 ◽

2019 ◽

Vol 37 (15_suppl) ◽

pp. e20014-e20014

Author(s):

Bo Cheng ◽

Cong Wang ◽

Xue Meng

Keyword(s):

Lung Cancer ◽

Cancer Patients ◽

Cox Regression ◽

Clinical Care ◽

External Validation ◽

Categorical Variables ◽

Rank Test ◽

Survival Prediction ◽

Cox Regression Analysis ◽

Lung Cancer Patients

e20014 Background: Nomograms are commonly used tools to estimate prognosis in oncology and medicine.We aimed to establish a nomogram with patients’ characteristics and all available hematological biomarkers for lung cancer patients. Methods: All indexes were cataloged according to clinical significance. Principle component analysis (PCA) was used to reduce the dimensions. Each component was transformed into categorical variables based on recognized cut-off values from receiver operating characteristic (ROC) curve. Kaplan-Meier analysis with log-rank test was used to evaluate the prognostic value of each component. Multivariate analysis was used to determine the promising prognostic biomarkers. Five components were entered into a predictive nomogram. The model was subjected to bootstrap internal validation and to external validation with a separate cohort from Shandong Cancer Hospital. The predictive accuracy and discriminative ability were measured by concordance index (C index) and risk group stratification. Results: Two hundred thirty-six patients were retrospectively analyzed in this study, with 134 in the Discovery Group and 102 in the Validation Group. Forty-seven indexes were sorted into 8 subgroups, and 20 principle components were extracted for further survival analysis. Via cox regression analysis, five components were significant and entered into predictive nomograms. The calibration curves for probability of 3-, and 5-year overall survival (OS) showed optimal agreement between nomogram prediction and actual observation. The new scoring system according to nomogram allowed significant distinction between survival curves within respective tumor-node-metastasis (TNM) subgroups. Conclusions: A nomogram based on the clinical indexes was established for survival prediction of lung cancer patients, which can be used for treatment therapy selection and clinical care option. PCA makes big data analysis feasible.

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Gamma Knife Stereotactic Radiosurgery for Patients with Glioblastoma Multiforme

Neurosurgery ◽

10.1097/00006123-200201000-00009 ◽

2002 ◽

Vol 50 (1) ◽

pp. 41-47 ◽

Cited By ~ 15

Author(s):

Emmanuel C. Nwokedi ◽

Steven J. DiBiase ◽

Salma Jabbour ◽

Joseph Herman ◽

Pradip Amin ◽

...

Keyword(s):

Overall Survival ◽

Regression Analysis ◽

Glioblastoma Multiforme ◽

Stereotactic Radiosurgery ◽

Cox Regression ◽

Karnofsky Performance Status ◽

Performance Status ◽

Cox Regression Analysis ◽

Group 2 ◽

Group 1

ABSTRACT OBJECTIVE Stereotactic radiosurgery (SRS) has become an effective therapeutic modality for the treatment of patients with glioblastoma multiforme (GBM). This retrospective review evaluates the impact of SRS delivered on a gamma knife (GK) unit as an adjuvant therapy in the management of patients with GBM. METHODS Between August 1993 and December 1998, 82 patients with pathologically confirmed GBM received external beam radiotherapy (EBRT) at the University of Maryland Medical Center. Of these 82 patients, 64 with a minimum follow-up duration of at least 1 month are the focus of this analysis. Of the 64 assessable patients, 33 patients were treated with EBRT alone (Group 1), and 31 patients received both EBRT plus a GK-SRS boost (Group 2). GK-SRS was administered to most patients within 6 weeks of the completion of EBRT. The median EBRT dose was 59.7 Gy (range, 28–70.2 Gy), and the median GK-SRS dose to the prescription volume was 17.1 Gy (range, 10–28 Gy). The median age of the study population was 50.4 years, and the median pretreatment Karnofsky performance status was 80. Patient-, tumor-, and treatment-related variables were analyzed by Cox regression analysis, and survival curves were generated by the Kaplan-Meier product limit. RESULTS Median overall survival for the entire cohort was 16 months, and the actuarial survival rate at 1, 2, and 3 years were 67, 40, and 26%, respectively. When comparing age, Karnofsky performance status, extent of resection, and tumor volume, no statistical differences where discovered between Group 1 versus Group 2. When comparing the overall survival of Group 1 versus Group 2, the median survival was 13 months versus 25 months, respectively (P = 0.034). Age, Karnofsky performance status, and the addition of GK-SRS were all found to be significant predictors of overall survival via Cox regression analysis. No acute Grade 3 or Grade 4 toxicity was encountered. CONCLUSION The addition of a GK-SRS boost in conjunction with surgery and EBRT significantly improved the overall survival time in this retrospective series of patients with GBM. A prospective, randomized validation of the benefit of SRS awaits the results of the recently completed Radiation Therapy Oncology Group's trial RTOG 93-05.

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Tumor microenvironment related novel signature predict lung adenocarcinoma survival

PeerJ ◽

10.7717/peerj.10628 ◽

2021 ◽

Vol 9 ◽

pp. e10628

Author(s):

Juan Chen ◽

Rui Zhou

Keyword(s):

Gene Expression ◽

Overall Survival ◽

Regression Analysis ◽

Tumor Microenvironment ◽

Prognostic Model ◽

Cox Regression ◽

Survival Prediction ◽

Clinical Parameters ◽

Ppi Network ◽

Cox Regression Analysis

Background Lung adenocarcinoma (LUAD) is the most common histological type of lung cancers, which is the primary cause of cancer‐related mortality worldwide. Growing evidence has suggested that tumor microenvironment (TME) plays a pivotal role in tumorigenesis and progression. Hence, we investigate the correlation of TME related genes with LUAD prognosis. Method The information of LUAD gene expression data was obtained from The Cancer Genome Atlas (TCGA). According to their immune/stromal scores calculated by the ESTIMATE algorithm, differentially expressed genes (DEGs) were identified. Then, we performed univariate Cox regression analysis on DEGs to obtain genes that are apparently bound up with LUAD survival (SurGenes). Functional annotation and protein-protein interaction (PPI) was also conducted on SurGenes. By validating the SurGenes with data sets of lung cancer from the Gene Expression Omnibus (GEO), 106 TME related SurGenes were generated. Further, intersection analysis was executed between the 106 TME related SurGenes and hub genes from PPI network, PTPRC and CD19 were obtained. Gene Set Enrichment Analysis and CIBERSORT analysis were performed on PTPRC and CD19. Based on the TCGA LUAD dataset, we conducted factor analysis and Step-wise multivariate Cox regression analysis for 106 TME related SurGenes to construct the prognostic model for LUAD survival prediction. The LUAD dataset in GEO (GSE68465) was used as the testing dataset to confirm the prognostic model. Multivariate Cox regression analysis was used between risk score from the prognostic model and clinical parameters. Result A total of 106 TME related genes were collected in our research totally, which were markedly correlated with the overall survival (OS) of LUAD patient. Bioinformatics analysis suggest them mainly concentrated on immune response, cell adhesion, and extracellular matrix. More importantly, among 106 TME related SurGenes, PTPRC and CD19 were highly interconnected nodes among PPI network and correlated with immune activity, exhibiting significant prognostic potential. The prognostic model was a weighted linear combination of the 106 genes, by which the low-OS LUAD samples could be separated from the high-OS samples with success. This model was also able to rebustly predict the situation of survival (training set: p-value < 0.0001, area under the curve (AUC) = 0.649; testing set: p-value = 0.0009, AUC = 0.617). By combining with clinical parameters, the prognostic model was optimized. The AUC achieved 0.716 for 3 year and 0.699 for 5 year. Conclusion A series of TME-related prognostic genes were acquired in this research, which could reflect immune disorders within TME, and PTPRC and CD19 show the potential to be an indicator for LUAD prognosis and tumor microenvironment modulation. The prognostic model constructed base on those prognostic genes presented a high predictive ability, and may have clinical implications in the overall survival prediction of LUAD.

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IL-11RA is a Prognostic Biomarker and Correlated with Immune Infiltration in Lung Adenocarcinoma

10.21203/rs.3.rs-673757/v1 ◽

2021 ◽

Author(s):

Aitao Nai ◽

SHOAIB BASHIR ◽

Ling Jin ◽

Zirui He ◽

Shuwen Zeng ◽

...

Keyword(s):

Overall Survival ◽

Lung Adenocarcinoma ◽

Cox Regression ◽

Progression Free Survival ◽

Clinicopathological Parameters ◽

Free Survival ◽

Cox Regression Analysis ◽

Immune Infiltration ◽

Potential Biomarker

Abstract Background: Interleukin-11 receptor subunit alpha (IL-11RA) contributes to multiple biological processes in various tumors. However, the role of IL-11RA in Lung adenocarcinoma (LUAD) is still undetermined. The study aims to explore the role of IL-11RA in LUAD via an integrated bioinformatics analysis. Methods: TIMER, GEPIA, TCGA and HPA databases analysis were used to detect IL-11RA expression. UALCAN database was used to analysis the correlation between IL-11RA expression and clinicopathological parameters of LUAD. Kaplan-Meier Plotter, TCGA and GEO databases were used to analysis overall survival (OS) and progression-free survival (PFS) of the LUAD patients. Univariate Cox regression analysis was used to assess the prognostic value of IL-11RA in different clinical characteristics. GSEA, and TIMER were used to investigate the relationship between IL-11RA and immune infiltration.Results: The expression of IL-11RA was down-regulated in LUAD tissues. Furthermore, IL-11RA expression was closely associated with clinical stage, lymph node stage and smoking habits. The patients with lower IL-11RA expression had poorer overall survival (OS) and progression-free survival (PFS). Lower IL-11RA expression was significantly associated with its hypermethylation, and the hypermethylation of CpG site at cg14609668 and cg21504624 was obviously correlated with poorer OS. Then, we found that IL-11RA may play an important role in LUAD progression and immune regulations. Notably, High expression of IL-11RA may suppress the progression of LUAD through inhibiting cell proliferation and immune cell infiltration, especially in B cells, CD4+ T cells, and Dendritic Cell. Conclusions: Decreased IL-11RA expression correlates with poor prognosis and immune infiltration in LUAD. Our work highlights IL-11RA might be a potential biomarker for prognosis and provide a new therapeutic target for LUAD patients.

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A Novel Glucose Metabolism-Related Gene Signature for Overall Survival Prediction in Patients with Glioblastoma

BioMed Research International ◽

10.1155/2021/8872977 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Chaocai Zhang ◽

Minjie Wang ◽

Fenghu Ji ◽

Yizhong Peng ◽

Bo Wang ◽

...

Keyword(s):

Overall Survival ◽

High Risk ◽

Glucose Metabolism ◽

Cox Regression ◽

Gene Signature ◽

Low Risk ◽

Gene Set Enrichment Analysis ◽

Survival Prediction ◽

Related Gene ◽

Cox Regression Analysis

Introduction. Glioblastoma (GBM) is one of the most frequent primary intracranial malignancies, with limited treatment options and poor overall survival rates. Alternated glucose metabolism is a key metabolic feature of tumour cells, including GBM cells. However, due to high cellular heterogeneity, accurately predicting the prognosis of GBM patients using a single biomarker is difficult. Therefore, identifying a novel glucose metabolism-related biomarker signature is important and may contribute to accurate prognosis prediction for GBM patients. Methods. In this research, we performed gene set enrichment analysis and profiled four glucose metabolism-related gene sets containing 327 genes related to biological processes. Univariate and multivariate Cox regression analyses were specifically completed to identify genes to build a specific risk signature, and we identified ten mRNAs (B4GALT7, CHST12, G6PC2, GALE, IL13RA1, LDHB, SPAG4, STC1, TGFBI, and TPBG) within the Cox proportional hazards regression model for GBM. Results. Depending on this glucose metabolism-related gene signature, we divided patients into high-risk (with poor outcomes) and low-risk (with satisfactory outcomes) subgroups. The results of the multivariate Cox regression analysis demonstrated that the prognostic potential of this ten-gene signature is independent of clinical variables. Furthermore, we used two other GBM databases (Chinese Glioma Genome Atlas (CGGA) and REMBRANDT) to validate this model. In the functional analysis results, the risk signature was associated with almost every step of cancer progression, such as adhesion, proliferation, angiogenesis, drug resistance, and even an immune-suppressed microenvironment. Moreover, we found that IL31RA expression was significantly different between the high-risk and low-risk subgroups. Conclusion. The 10 glucose metabolism-related gene risk signatures could serve as an independent prognostic factor for GBM patients and might be valuable for the clinical management of GBM patients. The differential gene IL31RA may be a potential treatment target in GBM.

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