Prognostic prediction model for glioblastoma: a metabolic gene signature and independent external validation

Abstract

Background. Glioblastoma (GBM) is the most common primary malignant intracranial tumor and is closely related to metabolic alterations. However, few accepted prognostic models are currently available, especially models based on metabolic genes. Methods . Transcriptome data were obtained for all patients diagnosed with GBM from the Gene Expression Omnibus (GEO) (training cohort, n=369) and The Cancer Genome Atlas (TCGA) (validation cohort, n=152) with the following variables: age at diagnosis, sex, follow-up and overall survival (OS). Metabolic genes according to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were filtered, and a Lasso regression model was constructed. Survival was assessed by univariate or multivariate Cox proportional hazards regression and Kaplan-Meier analysis, and we also conducted an independent external validation to examine the model. Results. There were 341 metabolic genes that showed significant differences between normal brain tissues and GBM tissues in both the training and validation cohorts, among which 56 genes were significantly correlated with the OS of patients. Lasso regression revealed that the metabolic prognostic model was composed of 18 genes, including COX10 , COMT , and GPX2 , with protective effects, as well as OCRL and RRM2 , with unfavorable effects. Patients classified as high-risk by the risk score from this model had markedly shorter OS than low-risk patients ( P <0.0001), and this significant result was also observed in the independent external validation cohort ( P <0.001). Conclusions . The prognosis of GBM was dramatically related to metabolic pathways, and our metabolic prognostic model had high accuracy and application value in predicting the OS of GBM patients. Background. Glioblastoma (GBM) is the most common primary malignant intracranial tumor and is closely related to metabolic alterations. However, few accepted prognostic models are currently available, especially models based on metabolic genes. Methods . Transcriptome data were obtained for all patients diagnosed with GBM from the Gene Expression Omnibus (GEO) (training cohort, n=369) and The Cancer Genome Atlas (TCGA) (validation cohort, n=152) with the following variables: age at diagnosis, sex, follow-up and overall survival (OS). Metabolic genes according to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were filtered, and a Lasso regression model was constructed. Survival was assessed by univariate or multivariate Cox proportional hazards regression and Kaplan-Meier analysis, and we also conducted an independent external validation to examine the model. Results. There were 341 metabolic genes that showed significant differences between normal brain tissues and GBM tissues in both the training and validation cohorts, among which 56 genes were significantly correlated with the OS of patients. Lasso regression revealed that the metabolic prognostic model was composed of 18 genes, including COX10 , COMT , and GPX2 , with protective effects, as well as OCRL and RRM2 , with unfavorable effects. Patients classified as high-risk by the risk score from this model had markedly shorter OS than low-risk patients ( P <0.0001), and this significant result was also observed in the independent external validation cohort ( P <0.001).Conclusions . The prognosis of GBM was dramatically related to metabolic pathways, and our metabolic prognostic model had high accuracy and application value in predicting the OS of GBM patients.

Identification and characterization of sex‐dependent gene expression profile in glioblastoma

“…The study of Lei et al . showed 341 metabolic genes that exhibited significant differences between normal brain and GBM tissues, and they constructed a prognostic prediction model for GBM according to these metabolic genes 20 . Johansen and colleagues further found that GBM displayed distinct sex‐based differential methylation patterns based on molecular subtype 21 …”