ArticleInternational journal of general medicine2021
A Novel Glycosyltransferase-Related Gene Signature for Overall Survival Prediction in Patients with Ovarian Cancer.
Article in International journal of general medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.
- Mapping and visualization of global research progress on deubiquitinases in ovarian cancer: a bibliometric analysis.Frontiers in pharmacology · 2024Pooled it
- Pathogenic glycosyltransferase genes and potential therapeutic drugs in pressure overload-induced heart failure.ESC heart failure · 2025Article
- BRCAGenie: A machine learning-driven 43-gene polygenic risk score model for precision prediction of breast cancer survival.Journal of translational medicine · 2025Article
- A Prognostic Model Based on Metabolism-Related Genes for Patients with Ovarian Cancer.Doklady. Biochemistry and biophysics · 2023Article
- A novel glycosyltransferase-related lncRNA signature correlates with lung adenocarcinoma prognosis.Frontiers in oncology · 2022Article
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1 author at 1 institution in 1 country.
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Abstract
backgroundOvarian cancer is a highly malignant epithelial tumor. Recently, it has been reported the role of glycosyltransferases (GTs) in various cancers. However, the prognostic value of GTs-related genes in ovarian cancer remained largely unknown.
methodsRNA-sequencing (RNA-seq) data and corresponding clinical characteristics of patients with ovarian cancer were extracted from the public database of the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx). We constructed the least absolute shrinkage and selection operator (LASSO) Cox regression model to explore a multigene signature comprising GTs-related genes in the TCGA and GTEx cohort. Patients with ovarian cancer in International Cancer Genome Consortium (ICGC) database were applied for further validation. We also performed functional analysis on the differentially expressed genes (DEGs) of high-risk and low-risk groups in the TCGA cohort. Additionally, the immune status between the two risk groups was assessed, respectively.
resultsOur results showed that 64 GTs-related genes were differentially expressed between tumor tissues and normal tissues in the TCGA and GTEx cohort. A prognostic model of 15 candidate genes was constructed, which classified patients into high- and low-risk groups. Compared with low-risk patients, high-risk patients had an obvious worse overall survival (OS) (P < 0.0001 in the TCGA and GTEx cohort and P = 0.042 in the ICGC cohort). Multivariate Cox regression analysis revealed that the risk score was an independent factor for OS of ovarian cancer. Functional analysis indicated that these DEGs were also enriched in immune-related pathways, and the immune status was significantly different between the two risk groups in TCGA cohort.
conclusionIn conclusion, a novel GTs-related gene signature may be used for the prognosis of ovarian cancer. Targeting GTs-related gene can act as a therapeutic alternative for ovarian cancer.
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