scholarly journals Identification and Validation of an 11-Ferroptosis Related Gene Signature and Its Correlation With Immune Checkpoint Molecules in Glioma

2021 ◽  
Vol 9 ◽  
Author(s):  
Zhuohui Chen ◽  
Tong Wu ◽  
Zhouyi Yan ◽  
Mengqi Zhang
Keyword(s):  
Overall Survival ◽  
Cox Regression ◽  
Gene Signature ◽  
Curve Analysis ◽  
Regression Analyses ◽  
Related Gene ◽  
Roc Curve Analysis ◽  
Small Molecule Drugs ◽  
The Relationship ◽  
Genome Atlas

BackgroundGlioma is the most common primary malignant brain tumor with significant mortality and morbidity. Ferroptosis, a novel form of programmed cell death (PCD), is critically involved in tumorigenesis, progression and metastatic processes.MethodsWe revealed the relationship between ferroptosis-related genes and glioma by analyzing the mRNA expression profiles from The Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA), GSE16011, and the Repository of Molecular Brain Neoplasia Data (REMBRANDT) datasets. The least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to construct a ferroptosis-associated gene signature in the TCGA cohort. Glioma patients from the CGGA, GSE16011, and REMBRANDT cohorts were used to validate the efficacy of the signature. Receiver operating characteristic (ROC) curve analysis was applied to measure the predictive performance of the risk score for overall survival (OS). Univariate and multivariate Cox regression analyses of the 11-gene signature were performed to determine whether the ability of the prognostic signature in predicting OS was independent. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted to identify the potential biological functions and pathways of the signature. Subsequently, we performed single sample gene set enrichment analysis (ssGSEA) to explore the correlation between risk scores and immune status. Finally, seven putative small molecule drugs were predicted by Connectivity Map.ResultsThe 11-gene signature was identified to divide patients into two risk groups. ROC curve analysis indicated the 11-gene signature as a potential diagnostic factor in glioma patients. Multivariate Cox regression analyses showed that the risk score was an independent predictive factor for overall survival. Functional analysis revealed that genes were enriched in iron-related molecular functions and immune-related biological processes. The results of ssGSEA indicated that the 11-gene signature was correlated with the initiation and progression of glioma. The small molecule drugs we selected showed significant potential to be used as putative drugs.Conclusionwe identified a novel ferroptosis-related gene signature for prognostic prediction in glioma patients and revealed the relationship between ferroptosis-related genes and immune checkpoint molecules.

scholarly journals Identification of a Liver Progenitor Cell-Related Genes Signature Predicting Overall Survival for Hepatocellular Carcinoma

2021 ◽  
Vol 20 ◽  
pp. 153303382110414
Author(s):  
Xiaoyong Li ◽  
Jiaqong Lin ◽  
Yuguo pan ◽  
Peng Cui ◽  
Jintang Xia
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Regression Analysis ◽  
Prognostic Model ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Regression Analyses ◽  
Related Gene ◽  
Cox Regression Analysis

Background: Liver progenitor cells (LPCs) play significant roles in the development and progression of hepatocellular carcinoma (HCC). However, no studies on the value of LPC-related genes for evaluating HCC prognosis exist. We developed a gene signature of LPC-related genes for prognostication in HCC. Methods: To identify LPC-related genes, we analyzed mRNA expression arrays from a dataset (GSE57812 & GSE 37071) containing LPCs, mature hepatocytes, and embryonic stem cell samples. HCC RNA-Seq data from The Cancer Genome Atlas (TCGA) were used to explore the differentially expressed genes (DEGs) related to prognosis through DEG analysis and univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed to construct the LPC-related gene prognostic model in the TCGA training dataset. This model was validated in the TCGA testing set and an external dataset (International Cancer Genome Consortium [ICGC] dataset). Finally, we investigated the relationship between this prognostic model with tumor-node-metastasis stage, tumor grade, and vascular invasion of HCC. Results: Overall, 1770 genes were identified as LPC-related genes, of which 92 genes were identified as DEGs in HCC tissues compared with normal tissues. Furthermore, we randomly assigned patients from the TCGA dataset to the training and testing cohorts. Twenty-six DEGs correlated with overall survival (OS) in the univariate Cox regression analysis. Lasso and multivariate Cox regression analyses were performed in the TCGA training set, and a 3-gene signature was constructed to stratify patients into 2 risk groups: high-risk and low-risk. Patients in the high-risk group had significantly lower OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the signature's predictive capacity. Moreover, the risk score was confirmed to be an independent predictor for patients with HCC. Conclusion: We demonstrated that the LPC-related gene signature can be used for prognostication in HCC. Thus, targeting LPCs may serve as a therapeutic alternative for HCC.

scholarly journals Establishment and Validation of an MTORC1 Signaling-Related Gene Signature to Predict Overall Survival in Patients with Hepatocellular Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zheng Yao ◽  
Song Wen ◽  
Jun Luo ◽  
Weiyuan Hao ◽  
Weiren Liang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Decision Tree ◽  
Survival Data ◽  
Cox Regression ◽  
Gene Signature ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Cancer Hallmarks ◽  
Mtorc1 Signaling

Background. Accurate and effective biomarkers for the prognosis of patients with hepatocellular carcinoma (HCC) are poorly identified. A network-based gene signature may serve as a valuable biomarker to improve the accuracy of risk discrimination in patients. Methods. The expression levels of cancer hallmarks were determined by Cox regression analysis. Various bioinformatic methods, such as GSEA, WGCNA, and LASSO, and statistical approaches were applied to generate an MTORC1 signaling-related gene signature (MSRS). Moreover, a decision tree and nomogram were constructed to aid in the quantification of risk levels for each HCC patient. Results. Active MTORC1 signaling was found to be the most vital predictor of overall survival in HCC patients in the training cohort. MSRS was established and proved to hold the capacity to stratify HCC patients with poor outcomes in two validated datasets. Analysis of the patient MSRS levels and patient survival data suggested that the MSRS can be a valuable risk factor in two validated datasets and the integrated cohort. Finally, we constructed a decision tree which allowed to distinguish subclasses of patients at high risk and a nomogram which could accurately predict the survival of individuals. Conclusions. The present study may contribute to the improvement of current prognostic systems for patients with HCC.

Development and validation of an unfolded protein response-related gene signature to predict overall survival in HNSCC.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e18033-e18033
Author(s):  
Jun Chen ◽  
Bei Zhang
Keyword(s):  
Gene Expression ◽  
Overall Survival ◽  
Unfolded Protein Response ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
Related Gene ◽  
Hallmarks Of Cancer ◽  
Unfolded Protein ◽  
Protein Response

e18033 Background: Genomic expression profiles have enabled the classification of head and neck squamous cell carcinoma (HNSCC) into molecular sub-types and provide prognostic information, which have implications for the personalized treatment of HNSCC beyond clinical and pathological features. Methods: Gene-expression profiling was identified in TCGA- HNSCC (n = 492) and validated with the Gene Expression Ominibus (GEO) dataset(n = 270) for which RNA sequencing data and clinical covariates were available. A single-sample gene set enrichment analysis (ssGSEA) algorithm were used to quantified the levels of various hallmarks of cancer. And LASSO Cox regression model was used to screen robust prognostic biomarkers to identify the best set of survival-associated gene signatures in HNSCC. Statistical analyses were performed using R version 3.4.4. Results: We identified unfolded protein response as the primary risk factor for survival（cox coefficient = 17.4 [8.4-26.3], P < 0.001）among various hallmarks of cancer in TCGA- HNSCC. And unfolded protein response ssGESA scores were significantly elevated in patients who died during follow up (P = 0.009). Kaplan-Meier analysis showed that patients with low ssGSEA scores of unfolded protein response exhibited better OS (HR = 0.69, P = 0.008). And we established an unfolded protein response-related gene signature based on lasso cox. We then apply the unfolded protein response -related gene signature to classify patients into the high risk group and the low risk group with the cutoff of 0.18. Adjusted for stage，age，gender, our signature was an independent risk factor for overall survival in TCGA cohorts (HR = 0.39 [0.28-0.53],P = < 0.001). In external independent cohorts, similar results were observed. In the validation cohort GEO65858, the patients with high unfolded protein response score showed longer survival (HR = 0.62 [0.38-1.0], P = 0.049). And adjusted for stage，age，HPV state, the multivariate cox regression analysis showed that unfolded protein response-related gene signature exhibited an independent risk prediction for overall survival in 270 patients with HNSCC (HR = 0.57 [0.35-0.94], P = 0.026). Conclusions: By analyzing the gene-expression data with bioinformation approach, we developed and validated a risk prediction model with unfolded protein response -related expression scores in HNSCC, which have the potential to identify patients who could have better overall survival.

scholarly journals Development of a Predictive Immune-Related Gene Signature Associated With Hepatocellular Carcinoma Patient Prognosis

2020 ◽  
Vol 27 (1) ◽  
pp. 107327482097711
Author(s):  
Jiasheng Lei ◽  
Dengyong Zhang ◽  
Chao Yao ◽  
Sheng Ding ◽  
Zheng Lu
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Cox Regression ◽  
R Package ◽  
Gene Signature ◽  
Differentially Expressed ◽  
Related Gene ◽  
Patient Cohort ◽  
Immune Related Gene ◽  
Related Risk

Background: Hepatocellular carcinoma (HCC) remains the third leader cancer-associated cause of death globally, but the etiological basis for this complex disease remains poorly clarified. The present study was thus conceptualized to define a prognostic immune-related gene (IRG) signature capable of predicting immunotherapy responsiveness and overall survival (OS) in patients with HCC. Methods: Five differentially expressed IRG associated with HCC were established the immune-related risk model through univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. Patients were separated at random into training and testing cohorts, after which the association between the identified IRG signature and OS was evaluated using the “survival” R package. In addition, maftools was leveraged to assess mutational data, with tumor mutation burden (TMB) scores being calculated as follows: (total mutations/total bases) × 106. Immune-related risk term abundance was quantified via “ssGSEA” algorithm using the “gsva” R package. Results: HCC patients were successfully stratified into low-risk and high-risk groups based upon a signature composed of 5 differentially expressed IRGs, with overall survival being significantly different between these 2 groups in training cohort, testing cohort and overall patient cohort ( P = 1.745e-06, P = 1.888e-02, P = 4.281e-07). No association was observed between TMB and this IRG risk score in the overall patient cohort ( P = 0.461). Notably, 19 out of 29 immune-related risk terms differed substantially in the overall patient dataset. These risk terms mainly included checkpoints, human leukocyte antigens, natural killer cells, dendritic cells, and major histocompatibility complex class I. Conclusion: In summary, an immune-related prognostic gene signature was successfully developed and used to predict survival outcomes and immune system status in patients with HCC. This signature has the potential to help guide immunotherapeutic treatment planning for patients affected by this deadly cancer.

scholarly journals The Landscape of Glycosyltransferase Gene Signature for Overall Survival Prediction in Hepatocellular Carcinoma

2020 ◽  
Author(s):  
Qiang Cai ◽  
Shizhe Yu ◽  
Jian Zhao ◽  
Duo Ma ◽  
Long Jiang ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
Risk Score ◽  
Prognostic Value ◽  
Cox Regression ◽  
Risk Groups ◽  
Gene Signature ◽  
Low Risk ◽  
Regression Analyses ◽  
Risk Patients

Abstract Background: Hepatocellular carcinoma (HCC) is heterogeneous disease occurring in the background of chronic liver diseases. The role of glycosyltransferase (GT) genes have recently been the focus of research associating with the development of tumors. However, the prognostic value of GT genes in HCC remains not elucidated. This study aimed to demonstrate the GT genes related to the prognosis of HCC through bioinformatics analysis.Methods: The GT genes signatures were identified from the training set of The Cancer Genome Atlas (TCGA) dataset using univariate and the least absolute shrinkage and selection operator (LASSO) Cox regression analyses. Then, we analyzed the prognostic value of GT genes signatures related to the overall survival (OS) of HCC patients. A prognostic model was constructed, and the risk score of each patient was calculated as formula, which divided HCC patients into high- and low-risk groups. Kaplan-Meier (K-M) and Receiver operating characteristic (ROC) curves were used to assess the OS of HCC patients. The prognostic value of GT genes signatures was further investigated in the validation set of TCGA database. Univariate and multivariate Cox regression analyses were performed to demonstrate the independent factors on OS. Finally, we utilized the gene set enrichment analysis (GSEA) to annotate the function of these genes between the two risk categories. Results: In this study, we identified and validated 4 GT genes as the prognostic signatures. The K-M analysis showed that the survival rate of the high-risk patients was significantly lower than that of the low-risk patients. The risk score calculated with 4 gene signatures could predict OS for 3-, 5-, and 7-year in patients with HCC, revealing the prognostic ability of these gene signature. In addition, Multivariate Cox regression analyses indicated that the risk score was an independent prognostic factor for HCC. Functional analysis further revealed that immune-related pathways were enriched, and immune status in HCC were different between the two risk groups.Conclusion: In conclusion, a novel GT genes signature can be used for prognostic prediction in HCC. Thus, targeting GT genes may be a therapeutic alternative for HCC.

scholarly journals Identification of a Pyroptosis-Related Gene Signature for Predicting Overall Survival and Response to Immunotherapy in Hepatocellular Carcinoma

2021 ◽  
Vol 12 ◽  
Author(s):  
Susu Zheng ◽  
Xiaoying Xie ◽  
Xinkun Guo ◽  
Yanfang Wu ◽  
Guobin Chen ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Overall Survival ◽  
High Risk ◽  
Regression Model ◽  
Risk Score ◽  
Cox Regression ◽  
Risk Group ◽  
Gene Signature ◽  
High Risk Group ◽  
Related Gene

Pyroptosis is a novel kind of cellular necrosis and shown to be involved in cancer progression. However, the diverse expression, prognosis and associations with immune status of pyroptosis-related genes in Hepatocellular carcinoma (HCC) have yet to be analyzed. Herein, the expression profiles and corresponding clinical characteristics of HCC samples were collected from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Then a pyroptosis-related gene signature was built by applying the least absolute shrinkage and selection operator (LASSO) Cox regression model from the TCGA cohort, while the GEO datasets were applied for verification. Twenty-four pyroptosis-related genes were found to be differentially expressed between HCC and normal samples. A five pyroptosis-related gene signature (GSDME, CASP8, SCAF11, NOD2, CASP6) was constructed according to LASSO Cox regression model. Patients in the low-risk group had better survival rates than those in the high-risk group. The risk score was proved to be an independent prognostic factor for overall survival (OS). The risk score correlated with immune infiltrations and immunotherapy responses. GSEA indicated that endocytosis, ubiquitin mediated proteolysis and regulation of autophagy were enriched in the high-risk group, while drug metabolism cytochrome P450 and tryptophan metabolism were enriched in the low-risk group. In conclusion, our pyroptosis-related gene signature can be used for survival prediction and may also predict the response of immunotherapy.

scholarly journals Prognostic Value of an Immune-Related Gene Signature in Oral Squamous Cell Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Chao Zhu ◽  
Liqun Gu ◽  
Mianfeng Yao ◽  
Jiang Li ◽  
Changyun Fang
Keyword(s):  
Overall Survival ◽  
Squamous Cell Carcinoma ◽  
Regression Analysis ◽  
Oral Squamous Cell Carcinoma ◽  
Risk Score ◽  
Cox Regression ◽  
Gene Signature ◽  
Related Gene ◽  
Cox Regression Analysis ◽  
Immune Related Gene

The prognosis and immunotherapy response rates are unfavorable in patients with oral squamous cell carcinoma (OSCC). The tumor microenvironment is associated with tumor prognosis and progression, and the underlying mechanisms remain unclear. We obtained differentially expressed immune-related genes from OSCC mRNA data in The Cancer Genome Atlas (TCGA) database. Overall survival-related risk signature was constructed by univariate Cox regression analysis and LASSO Cox regression analysis. The prognostic performance was validated with receiver operating characteristic (ROC) analysis and Kaplan–Meier survival curves in the TCGA and Gene Expression Omnibus (GEO) datasets. The risk score was confirmed to be an independent prognostic factor and a nomogram was built to quantify the risk of outcome for each patient. Furthermore, a negative correlation was observed between the risk score and the infiltration rate of immune cells, as well as the expression of immunostimulatory and immunosuppressive molecules. Functional enrichment analysis between different risk score subtypes detected multiple immune-related biological processes, metabolic pathways, and cancer-related pathways. Thus, the immune-related gene signature can predict overall survival and contribute to the personalized management of OSCC patients.

Analysis of metabolism-related gene signature for prognosis prediction of clear cell renal cell carcinoma.

2021 ◽  
Vol 39 (15_suppl) ◽  
pp. e16580-e16580
Author(s):  
Dalong Cao ◽  
Yuchen Liao ◽  
Guoqiang Wang ◽  
Shangli Cai ◽  
Guohai Shi
Keyword(s):  
Cox Regression ◽  
Validation Cohort ◽  
Clear Cell ◽  
Gene Signature ◽  
Related Gene ◽  
Prognostic Signature ◽  
Training Set ◽  
Roc Curve Analysis ◽  
Cell Renal Cell Carcinoma ◽  
Risk Patients

e16580 Background: Clear cell renal cell carcinoma (ccRCC) is characterized by a dysregulation of changes in cellular metabolism. However, the prognostic value of metabolism-related genes in ccRCC have not been systematically profiled. In this study, a candidate prognostic gene signature of metabolism in ccRCC was explored. Methods: The clinical and gene expression profiles of ccRCC patients were downloaded from the TCGA, GEO and three clinical trials (CheckMate 009, CheckMate 010, CheckMate 025), and the metabolism-related gene set was downloaded from MSigDB. Differential expression analysis and LASSO Cox regression with binomial deviance minimization criteria were applied to identify and build a metabolism-based signature. The prognostic significance of the signature was further evaluated with the Receiver Operating Characteristic (ROC) curve analysis. Univariate and multivariate Cox regression analysis was performed to evaluate the impact of each variable on OS. Furthermore, the prediction power of the signature has been validated using different ccRCC cohort. Results: In this study, a signature of 8 metabolism-related genes (ANGPT2, ATP6V1B1, CACNA1E, CD163L1, EPN2, HOXD11, PROS1, SHOX2) was constructed as being significantly associated with overall survival (OS) among patients with ccRCC, which differentiated ccRCC patients into high- and low-risk subgroups. The Kaplan-Meier (KM) analysis showed that the survival rate of the low-risk patients was significantly higher than that of the higher-risk patients (hazard ratio (HR) in training set, 0.25 [95% CI, 0.14-0.44; P < 0.001]; testing set, 0.28 [95% CI, 0.10-0.76; P = 0.008]; validation cohort (clinical trials), 0.47 [95% CI, 0.33-0.68; P < 0 .001]; validation cohort (GSE29609), 0.25, [95% CI, 0.08-0.88; P = 0 .01]). ROC curve analysis of the prognostic signature showed that the areas under curve (AUC) for the 1-, 3-, and 5-year OS in all cohort were more than 0.70 (AUC of the signature for 3 year in the training set and validation cohort were 0.816 and 0.708, respectively, and 0.807 and 0.702, respectively, for the 5- year OS). Further more, a nomogram based on the signature was constructed and showed an accurate prediction for prognosis in ccRCC. Conclusions: Taken together, we identified the key metabolism-related genes and constructed a robust prognostic signature for the prognostic predictor of ccRCC patients, which maybe help personalized management of ccRCC patients.

scholarly journals The Systematic Landscape of Nectin Family and Nectin-Like Molecules: Functions and Prognostic Value in Low Grade Glioma

2021 ◽  
Vol 12 ◽  
Author(s):  
Yunhe Han ◽  
Cunyi Zou ◽  
Chen Zhu ◽  
Tianqi Liu ◽  
Shuai Shen ◽  
...  
Keyword(s):  
Prognostic Value ◽  
Molecular Mechanisms ◽  
Cox Regression ◽  
Roc Curves ◽  
Gene Signature ◽  
Low Grade ◽  
Kaplan Meier ◽  
Go Enrichment ◽  
The Relationship ◽  
Genome Atlas

Objective: Nectin and nectin-like molecules (Necls) are molecules that are involved in cell–cell adhesion and other vital cellular processes. This study aimed to determine the expression and prognostic value of nectin and Necls in low grade glioma (LGG).Materials and Methods: Differentially expressed nectin and Necls in LGG samples and the relationship of nectin family and Necls expression with prognosis, clinicopathological parameters, and survival were explored using The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), and Repository of Molecular Brain Neoplasia Data (REMBRANDT) databases. Univariate and multivariate Cox analysis models were performed to construct the prognosis-related gene signature. Kaplan-Meier curves and time-dependent receiver operating characteristic (ROC) curves and multivariate Cox regression analysis, were utilized to evaluate the prognostic capacity of the four-gene signature. Gene ontology (GO)enrichment analysis and Gene Set Enrichment Analyses (GSEA) were performed to further understand the underlying molecular mechanisms. The Tumor Immune Estimation Resource (TIMER) was used to explore the relationship between the four-gene signature and tumor immune infiltration.Results: Several nectin and Necls were differentially expressed in LGG. Kaplan–Meier survival analyses and Univariate Cox regression showed patients with high expression of NECTIN2 and PVR and low expression of CADM2 and NECTIN1 had worse prognosis among TCGA, CGGA, and REMBRANDT database. Then, a novel four-gene signature was built for LGG prognosis prediction. ROC curves, KM survival analyses, and multivariate COX regression indicated the new signature was an independent prognostic indicator for overall survival. Finally, GSEA and GO enrichment analyses revealed that immune-related pathways participate in the molecular mechanisms. The risk score had a strong negative correlation with tumor purity and data of TIMER showed different immune cell proportions (macrophage and myeloid dendritic cell) between high- and low-risk groups. Additionally, signature scores were positively related to multiple immune-related biomarkers (IL 2, IL8 and IFNγ).Conclusion: Our results offer an extensive analysis of nectin and Necls levels and a four-gene model for prognostic prediction in LGG, providing insights for further investigation of CADM2, NECTIN1/2, and PVR as potential clinical and immune targets in LGG.

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

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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