ArticleJournal of ovarian research2023
A signature based on glycosyltransferase genes provides a promising tool for the prediction of prognosis and immunotherapy responsiveness in ovarian cancer.
Article in Journal of ovarian research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed, 17 citations in OpenAlex.
- Structure, function, and implications of fucosyltransferases in health and disease.Nature communications · 2025Review
- Building simplified cancer subtyping and prediction models with glycan gene signatures.Cell reports methods · 2025Article
- Glycan diversity in ovarian cancer: Unraveling the immune interplay and therapeutic prospects.Seminars in immunopathology · 2024Review
- Comprehensive machine learning-based integration develops a novel prognostic model for glioblastoma.Molecular therapy. Oncology · 2024Article
- Glycosylation: mechanisms, biological functions and clinical implications.Signal transduction and targeted therapy · 2024Review
- Exploring Potential Epigenetic Biomarkers for Colorectal Cancer Metastasis.International journal of molecular sciences · 2024Review
- Application of a risk score model based on glycosylation-related genes in the prognosis and treatment of patients with low-grade glioma.Frontiers in immunology · 2024Article
- Comprehensive prognostic and immune analysis of a glycosylation related risk model in pancreatic cancer.BMC cancer · 2023Article
- Circadian Genes MBOAT2/CDA/LPCAT2/B4GALT5 in the Metabolic Pathway Serve as New Biomarkers of PACA Prognosis and Immune Infiltration.Life (Basel, Switzerland) · 2023Article
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Authors and funding
6 authors at 2 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundOvarian cancer (OC) is the most fatal gynaecological malignancy and has a poor prognosis. Glycosylation, the biosynthetic process that depends on specific glycosyltransferases (GTs), has recently attracted increasing importance due to the vital role it plays in cancer. In this study, we aimed to determine whether OC patients could be stratified by glycosyltransferase gene profiles to better predict the prognosis and efficiency of immune checkpoint blockade therapies (ICBs).
methodsWe retrieved transcriptome data across 420 OC and 88 normal tissue samples using The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, respectively. An external validation dataset containing 185 OC samples was downloaded from the Gene Expression Omnibus (GEO) database. Knockdown and pathway prediction of B4GALT5 were conducted to investigate the function and mechanism of B4GALT5 in OC proliferation, migration and invasion.
resultsA total of 50 differentially expressed GT genes were identified between OC and normal ovarian tissues. Two clusters were stratified by operating consensus clustering, but no significant prognostic value was observed. By applying the least absolute shrinkage and selection operator (LASSO) Cox regression method, a 6-gene signature was built that classified OC patients in the TCGA cohort into a low- or high-risk group. Patients with high scores had a worse prognosis than those with low scores. This risk signature was further validated in an external GEO dataset. Furthermore, the risk score was an independent risk predictor, and a nomogram was created to improve the accuracy of prognostic classification. Notably, the low-risk OC patients exhibited a higher degree of antitumor immune cell infiltration and a superior response to ICBs. B4GALT5, one of six hub genes, was identified as a regulator of proliferation, migration and invasion in OC.
conclusionTaken together, we established a reliable GT-gene-based signature to predict prognosis, immune status and identify OC patients who would benefit from ICBs. GT genes might be a promising biomarker for OC progression and a potential therapeutic target for OC.
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