ArticleJournal of ovarian research2023
A novel DNA methylation-related gene signature for the prediction of overall survival and immune characteristics of ovarian cancer patients.
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 7 papers.
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Who cites it
7 citing papers in PubMed.
- Genome-Wide Assessment of DNA Methylation Fidelity Identifies Representative Ovarian Cancer Cell Line Models.Journal of cellular and molecular medicine · 2026Article
- Aberrant DNA methylation of SOX1 and PAX1 as epigenetic biomarkers and prognostic indicators in oral squamous cell carcinoma: a single-centre retrospective study.BMC oral health · 2026Article
- DNA methylation and transcription factor-driven immune subtypes in ovarian cancer.Discover oncology · 2025Article
- Mechanistic Insights Into the Tumor-Driving and Diagnostic Roles of KCTD Family Genes in Ovarian Cancer: An Integrated In Silico and In Vitro Analysis.Cancer medicine · 2025Article
- Dendritic cell-related gene signature in pancreatic cancer stratifies patient subtypes and implicates a KCTD14-TNF signaling axis.Frontiers in immunology · 2025Article
- Effects of DNA methylation and its application in inflammatory bowel disease (Review).International journal of molecular medicine · 2024Review
- DNA methylation profiles in cancer: functions, therapy, and beyond.Cancer biology & medicine · 2023Article
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3 authors.
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Abstract
backgroundOvarian cancer (OC) is one of the most life-threatening cancers affecting women worldwide. Recent studies have shown that the DNA methylation state can be used in the diagnosis, treatment and prognosis prediction of diseases. Meanwhile, it has been reported that the DNA methylation state can affect the function of immune cells. However, whether DNA methylation-related genes can be used for prognosis and immune response prediction in OC remains unclear.
methodsIn this study, DNA methylation-related genes in OC were identified by an integrated analysis of DNA methylation and transcriptome data. Prognostic values of the DNA methylation-related genes were investigated through least absolute shrinkage and selection operator (LASSO) and Cox progression analyses. Immune characteristics were investigated by CIBERSORT, correlation analysis and weighted gene co-expression network analysis (WGCNA).
resultsTwelve prognostic genes (CA2, CD3G, HABP2, KCTD14, PI3, SERPINB5, SLAMF7, SLC9A2, STC2, TBP, TREML2 and TRIM27) were identified and a risk score signature and a nomogram based on prognostic genes and clinicopathological features were constructed for the survival prediction of OC patients in the training and two validation cohorts. Subsequently, the differences in the immune landscape between the high- and low-risk score groups were systematically investigated.
conclusionsTaken together, our study explored a novel efficient risk score signature and a nomogram for the survival prediction of OC patients. In addition, the differences of the immune characteristics between the two risk groups were clarified preliminarily, which will guide the further exploration of synergistic targets to improve the efficacy of immunotherapy in OC patients.
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