ArticleBioMed research international2021
Identifying Novel Cell Glycolysis-Related Gene Signature Predictive of Overall Survival in Gastric Cancer.
Article in BioMed research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 8 citations in OpenAlex.
- GALNT7-induced O-glycosylation of NUP50 activates fatty acid β-oxidation to promote lung adenocarcinoma metastasis.The Journal of biological chemistry · 2026Article
- Integrated transcriptomic network analysis reveals shared immunogenomic regulators between glioma and type 2 diabetes, highlighting VCAN as a prominent marker.Scientific reports · 2026Article
- Article
- Identification and verification of a glycolysis-related gene signature for gastric cancer.Annals of translational medicine · 2022Article
- NF-BioMed research international · 2022Article
- Transcriptomic and Metabolomic Profiling inFrontiers in cell and developmental biology · 2021Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGastric cancer (GC) is believed to be one of the most common digestive tract malignant tumors. The prognosis of GC remains poor due to its high malignancy, high incidence of metastasis and relapse, and lack of effective treatment. The constant progress in bioinformatics and molecular biology techniques has given rise to the discovery of biomarkers with clinical value to predict the GC patients' prognosis. However, the use of a single gene biomarker can hardly achieve the satisfactory specificity and sensitivity. Therefore, it is urgent to identify novel genetic markers to forecast the prognosis of patients with GC. MATERIALS AND
methodsIn our research, data mining was applied to perform expression profile analysis of mRNAs in the 443 GC patients from The Cancer Genome Atlas (TCGA) cohort. Genes associated with the overall survival (OS) of GC were identified using univariate analysis. The prognostic predictive value of the risk factors was determined using the Kaplan-Meier survival analysis and multivariate analysis. The risk scoring system was built in TCGA dataset and validated in an independent Gene Expression Omnibus (GEO) dataset comprising 300 GC patients. Based on the median of the risk score, GC patients were grouped into high-risk and low-risk groups.
resultsWe identified four genes (
conclusionThe signature consisting of four glycolysis-related genes could be used to forecast the GC patients' prognosis.
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