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

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.

Single cell RNA sequencing reveals differentiation related genes with drawing implications in predicting prognosis and immunotherapy response in gliomas

“…In the training cohort, the AUC of our model for predicting 1-year and 2-year overall survival were higher than other models (F i g. 5 I). In addition, the accuracy of our model was also better than other glioma related models in the validation cohort 4 , 6 , 7 , 26 (Fig. 5 J).…”

Identification of a Novel Glycosyltransferase Prognostic Signature in Hepatocellular Carcinoma Based on LASSO Algorithm

“…B3GAT3 participates in the biosynthesis of the glycosaminoglycan (GAG) linker region of proteoglycan (PG) ( Barré, et al, 2006 ). Given its important role in tumour metabolism, which was used repeatedly as a candidate gene for constructing prognostic models ( Zhao, et al, 2021 ; Zhao, et al, 2020 ; Bingxiang, et al, 2021 ), its function of promoting the process of tumour EMT in HCC was confirmed by experimental verification ( Zhang, et al, 2019 ). Based on the analysis above, the cancer-promoting effects of dysregulated expression are in accordance with our prediction results.…”

Integrated genomic, transcriptomic and metabolomic analysis reveals MDH2 mutation-induced metabolic disorder in recurrent focal segmental glomerulosclerosis

“…A prognostic model based on nine signature glycolytic genes, including MDH2, can accurately predict the prognosis of brain glioma patients. Among them, the mutation rate of HDAC4 and MDH2 genes was the highest at 8% ( 37 ). MDH2 was also overexpressed in endometrial carcinoma tissues and was related to the grade of the cancer ( 38 ).…”