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. 2021 Jun 16:2021:6680066.
doi: 10.1155/2021/6680066. eCollection 2021.

A Prognostic Model for Brain Glioma Patients Based on 9 Signature Glycolytic Genes

Xiao Bingxiang  1 , Wu Panxing  1 , Feng Lu  1 , Yan Xiuyou  1 , Ding Chao  1
Affiliations

A Prognostic Model for Brain Glioma Patients Based on 9 Signature Glycolytic Genes

Xiao Bingxiang et al. Biomed Res Int. .

Retraction in

Abstract

Objective: To screen glycolytic genes linked to the glioma prognosis and construct the prognostic model.

Methods: The relevant data of glioma were downloaded from TCGA and GTEx databases. GSEA of glycolysis-related pathways was carried out, and enriched differential genes were extracted. Screening out prognostic-related genes with conspicuous significance and construction of the prognostic model were conducted by multivariate Cox regression analysis and Lasso regression analysis. The model was evaluated, and cBioPortal was used to analyze the mutation of the model gene. The expression of the model gene in tumor and normal colon tissue was analyzed. The model was used to evaluate the prognosis of patients in different groups to verify the applicability of the model.

Results: 339 differentially glycolytic-related genes were enriched in REACTOME_GLYCOLYSIS, GLYCOLYTIC_PROCESS, HALLMARK_GLYCOLYSIS, and other pathways. We obtained 9 key prognostic genes and constructed the prognostic evaluation model. The 3-year AUC values of the ROC curve display model are greater than 0.75, which indicates that the accuracy of the model is good. The relation of age and risk score to prognosis is shown by univariate and multivariate Cox analysis. The expression of SRD5A3, MDH2, and B3GAT3 genes was significantly upregulated in the tumor tissues, while the HDAC4 and G6PC2 genes were downregulated. The mutation rate of MDH2 and HDAC4 genes was the highest. This model could effectively distinguish the risk of poor prognosis of patients in any age stage.

Conclusion: The prognostic assessment models based on glycolysis-related nine-gene signature could accurately predict the prognosis of patients with GBM.

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Conflict of interest statement

There are no conflicts of interest in this study.

Figures

Figure 1
Figure 1
GSEA results for the enrichment plots of five gene sets (BIOCARTA_GLYCOLYSIS_PATHWAY, GLYCOLYTIC, HALLMARK_GLYCOLYSIS, GLYCOLYSIS_GLUCONEOGENESIS, and REACTOME_GLYCOLYSIS) that were significantly differentiated in normal and GBM tissues based on TCGA. GSEA: gene set enrichment analysis; GBM: glioblastoma multiforme; TCGA: The Cancer Genome Atlas.
Figure 2
Figure 2
The nine-mRNA signature related to risk score predicts the overall survival of patients with glioblastoma multiforme. (a) mRNA risk score distribution. (b) Survival status. (c) Survival curves of patients in high-risk and low-risk groups. (d) The receiver operating characteristic curve (ROC) of 1 year and 3 years of the model. (e) Heat map of nine gene expression profile in The Cancer Genome Atlas.
Figure 3
Figure 3
The alteration proportion for the nine selected genes in clinical samples of glioblastoma multiforme in the cBioPortal database.
Figure 4
Figure 4
Different expressions of nine genes in the normal tissues and tumor tissues based on Genotype-Tissue Expression and The Cancer Genome Atlas database. (∗∗∗ represents P < 0.0001).
Figure 5
Figure 5
(a) Univariate analysis of gender, age, and risk score. (b) Multivariate analysis of gender, age, and risk score. (c) Kaplan-Meier survival curves of the female patient group. (d) Kaplan-Meier survival curves of the male patient group. (e) Kaplan-Meier survival curves of the age < 65 patient group. (f) Kaplan-Meier survival curves of the age≧65 patient group.
Figure 6
Figure 6
(a) Immunohistochemistry validated the expression of 9 prognostic-related glycolytic genes in glioma tissues and normal tissues. (b) qPCR experiments validated the expression of 9 prognostic-related glycolytic genes in glioma tissues and normal tissues.
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