ArticleAnnals of translational medicine2022
Identification and verification of a glycolysis-related gene signature for gastric cancer.
Article in Annals of translational medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 4 citations in OpenAlex.
- Glycolysis‑driven immunosuppression in gastric cancer: Metabolic crosstalk between tumor cells and the immune microenvironment (Review).International journal of oncology · 2026Review
- Integrated transcriptomic network analysis reveals shared immunogenomic regulators between glioma and type 2 diabetes, highlighting VCAN as a prominent marker.Scientific reports · 2026Article
- Prognostic modeling and immune infiltration analysis in hepatocellular carcinoma using glutathione metabolism-associated genes.European journal of medical research · 2025Article
- Integrated Multi-omics Data Analysis andJournal of Cancer · 2025Article
- Fructose-1, 6-Bisphosphate Aldolase B Suppresses Glycolysis and Tumor Progression of Gastric Cancer.Digestive diseases and sciences · 2024Article
- Single-Cell Sequencing Combined with Transcriptome Sequencing Constructs a Predictive Model of Key Genes in Multiple Sclerosis and Explores Molecular Mechanisms Related to Cellular Communication.Journal of inflammation research · 2024Article
- Identification of a novel reactive oxygen species (ROS)-related genes model combined with RT-qPCR experiments for prognosis and immunotherapy in gastric cancer.Frontiers in genetics · 2023Article
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Glycolysis is a central metabolic pathway for tumor cells. However, the relationship between glycolysis and the prognosis of gastric cancer (GC) patients is not well established. In this study, we sought to construct a glycolysis-related gene signature for GC. Methods: The messenger ribonucleic acid (mRNA) expression profiles were analyzed using data from The Cancer Genome Atlas (TCGA) database. Glycolysis-related gene sets and pathways were obtained from the Molecular Signatures Database (MSigDB). Subsequently, a prognosis prediction model of the glycolysis-related genes was constructed using Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. An external validation was conducted using data from the Gene Expression Omnibus (GEO) database. Risk scores were also calculated based on the signature. Finally, the correlations between the risk score and overall survival (OS), mutation, immune cell infiltration, immune score, and stromal score were examined in 22 types of infiltrating immune cells. Results: Fifty-five glycolysis-related genes were identified from TCGA database and MSigDB. Using the LASSO and Cox models, 4 novel genes (i.e., Conclusions: The established 4 glycolysis-related gene signature may serve as a reliable tool for the prognosis of GC patients and provide a potential glycolysis therapeutic target for GC.
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