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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 closely related to metabolic alteration. However, few accepted prognostic models are currently available, especially models based on metabolic genes. Methods: The transcriptome data were obtained for all of the 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 contracted, and a Lasso regression model was constructed. Survival was assessed by univariate or multivariate Cox proportional hazards regression and Kaplan-Meier analysis, and an independent external validation was also conducted to examine the model. Results: There were 341 metabolic genes showed significant differences between normal brain and GBM tissues in both the training and validation cohorts, among which 56 genes were dramatically correlated to 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 independent external validation (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.

SUBMITTER: Lei C

PROVIDER: S-EPMC8176239 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundGrowing evidence has revealed the crucial roles of stromal cells in the microenvironment of various malignant tumors. However, efficient prognostic signatures based on stromal characteristics in colon cancer have not been well-established yet. The present study aimed to construct a stromal score-based multigene prognostic prediction model for colon cancer.MethodsStromal scores were calculated based on the expression profiles of a colon cancer cohort from TCGA database applying the ESTIMATE algorithm. Linear models were used to identify differentially expressed genes between low-score and high-score groups by limma R package. Univariate, LASSO, and multivariate Cox regression models were used successively to select the prognostic gene signature. Two independent datasets from GEO were used as external validation cohorts.ResultsLow stromal score was demonstrated to be a favorable factor to the overall survival of colon cancer patients in TCGA cohort (p = 0.0046). Three hundred and seven stromal score-related differentially expressed genes were identified. Through univariate, LASSO, and multivariate Cox regression analyses, a gene signature consisting of LEP, NOG, and SYT3 was recognized to build a prognostic prediction model. Based on the predictive values estimated by the established integrated model, patients were divided into two groups with significantly different overall survival outcomes (p < 0.0001). Time-dependent Receiver operating characteristic curve analyses suggested the satisfactory predictive efficacy for the 5-year overall survival of the model (AUC value = 0.733). A nomogram with great predictive performance combining the multigene prediction model and clinicopathological factors was developed. The established model was validated in an external cohort (AUC value = 0.728). In another independent cohort, the model was verified to be of significant prognostic value for different subgroups, which was demonstrated to be especially accurate for young patients (AUC value = 0.763).ConclusionThe well-established model based on stromal score-related gene signature might serve as a promising tool for the prognostic prediction of colon cancer.

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Prognostic Prediction Model for Glioblastoma: A Metabolic Gene Signature and Independent External Validation.

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