ArticleBioMed research international2021
A Prognostic Model for Brain Glioma Patients Based on 9 Signature Glycolytic Genes.
Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 11 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
11 citing papers in PubMed, 12 citations in OpenAlex.
- The construction of a novel prognostic prediction model for glioma based on GWAS-identified prognostic-related risk loci.Open medicine (Warsaw, Poland) · 2024Article
- Retracted: A Prognostic Model for Brain Glioma Patients Based on 9 Signature Glycolytic Genes.BioMed research international · 2024Article
- Article
- Article
- Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model.PloS one · 2023Article
- Construction of a machine learning-based artificial neural network for discriminating PANoptosis related subgroups to predict prognosis in low-grade gliomas.Scientific reports · 2022Article
- Article
- Single cell RNA sequencing reveals differentiation related genes with drawing implications in predicting prognosis and immunotherapy response in gliomas.Scientific reports · 2022Article
- Integrated genomic, transcriptomic and metabolomic analysis reveals MDH2 mutation-induced metabolic disorder in recurrent focal segmental glomerulosclerosis.Frontiers in immunology · 2022Article
- Identification of a Novel Glycosyltransferase Prognostic Signature in Hepatocellular Carcinoma Based on LASSO Algorithm.Frontiers in genetics · 2022Article
- Establishment of an Immune-Related Gene Signature for Risk Stratification for Patients with Glioma.Computational and mathematical methods in medicine · 2021Article
Corrections and comments
- Retraction · 2024-03-20Computer-Aided Content or Computer-Generated Content · Concerns/Issues about Data · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo screen glycolytic genes linked to the glioma prognosis and construct the prognostic model.
methodsThe 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.
results339 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.
conclusionThe prognostic assessment models based on glycolysis-related nine-gene signature could accurately predict the prognosis of patients with GBM.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.