ArticleOncology letters2020
Identification and verification of the prognostic value of the glutathione S-transferase Mu genes in gastric cancer.
Article in Oncology letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
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- IGF2BP2 regulates the inflammation of fibroblast-like synoviocytes via GSTM5 in rheumatoid arthritis.Cell death discovery · 2024Article
- 5-Hydroxymethylcytosine in Cell-Free DNA Predicts Immunotherapy Response in Lung Cancer.Cells · 2024Article
- Revealing the mechanism of 755-nm long-pulsed alexandrite laser in inhibiting infantile hemangioma endothelial cells through transcriptome sequencing.Lasers in medical science · 2024Article
- Identification of 10 differentially expressed genes involved in the tumorigenesis of cervical cancerPeerJ · 2024Article
- Aberrant expression of GSTM5 in lung adenocarcinoma is associated with DNA hypermethylation and poor prognosis.BMC cancer · 2022Article
- Development of a Gene Risk Signature for Patients of Pancreatic Cancer.Journal of healthcare engineering · 2022Article
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gastric cancer (GC) is one of the most frequently diagnosed gastrointestinal cancer types in the world. Novel prognostic biomarkers are required to predict the progression of GC. Glutathione S-transferase Mu (GSTM) belongs to a family of phase II enzymes that have been implicated in a number of cancer types. However, the prognostic value of the GSTM genes has not been previously investigated in GC. The Cancer Genome Atlas (TCGA) was used to evaluate mRNA expression levels of GSTMs in GC tissue samples. Overall survival (OS) rates, hazard ratios (HRs) and 95% CIs were calculated using the Cox logistic regression model and Kaplan-Meier (KM) analysis was performed. In addition, the KM plotter online database was used to validate mRNA expression and the prognostic value of GSMT family members in patients with GC. To predict the function of GSTM genes in these patients, several bioinformatics tools, including the Database for Annotation, Visualization and Integrated Discovery, gene multiple association network integration algorithm, Search Tool for the Retrieval of Interacting Genes/Proteins, Gene Set Enrichment Analysis (GSEA), nomogram and genome-wide co-expression analysis were used. In the present study, high expression of GSTM5 was indicated to be strongly associated with lower OS in patients with GC, according to the TCGA and KM plotter online databases (HR=1.47, 95% CI: 1.06-2.04, P=0.021; and HR=1.69, 95% CI: 1.42-2.01, P=1.6×10
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