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

Overview

Journal Biomed Res Int

Publisher Wiley

Specialties Biomedical Engineering
Biotechnology
General Medicine

Date 2021 Jul 5

PMID 34222480

Citations 11

Authors

Xiao Bingxiang

Wu Panxing

Feng Lu

Yan Xiuyou

Ding Chao

Affiliations

Soon will be listed here.

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.

Citing Articles

The construction of a novel prognostic prediction model for glioma based on GWAS-identified prognostic-related risk loci.

Wei J, Li Y, Zhou W, Ma X, Hao J, Wen T Open Med (Wars). 2024; 19(1):20240895.

PMID: 38584840 PMC: 10996933. DOI: 10.1515/med-2024-0895.

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

International B Biomed Res Int. 2024; 2024:9854370.

PMID: 38550201 PMC: 10977256. DOI: 10.1155/2024/9854370.

A pyroptosis gene-based prognostic model for predicting survival in low-grade glioma.

Wang H, Yan L, Liu L, Lu X, Chen Y, Zhang Q PeerJ. 2023; 11:e16412.

PMID: 38025749 PMC: 10652862. DOI: 10.7717/peerj.16412.

Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model.

Nassani R, Bokhari Y, Alrfaei B PLoS One. 2023; 18(11):e0287448.

PMID: 37972206 PMC: 10653472. DOI: 10.1371/journal.pone.0287448.

Nuclear mitochondria-related genes-based molecular classification and prognostic signature reveal immune landscape, somatic mutation, and prognosis for glioma.

Liu C, Zhang N, Xu Z, Wang X, Yang Y, Bu J Heliyon. 2023; 9(9):e19856.

PMID: 37809472 PMC: 10559255. DOI: 10.1016/j.heliyon.2023.e19856.

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