ArticleFEBS open bio2021
DNA methylation subtypes for ovarian cancer prognosis.
Article in FEBS open bio, 2021. 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.
- An integrated machine learning-based model for joint diagnosis of ovarian cancer with multiple test indicators.Journal of ovarian research · 2024Article
- Personalization of Therapy in High-Grade Serous Tubo-Ovarian Cancer-The Possibility or the Necessity?Journal of personalized medicine · 2023Review
- Tumor Markers and Their Diagnostic Significance in Ovarian Cancer.Life (Basel, Switzerland) · 2023Review
- Computational Analysis of High-Dimensional DNA Methylation Data for Cancer Prognosis.Journal of computational biology : a journal of computational molecular cell biology · 2022Review
- Integrated analysis of the M2 macrophage-related signature associated with prognosis in ovarian cancer.Frontiers in oncology · 2022Article
- Integration of Transcriptome and Epigenome to Identify and Develop Prognostic Markers for Ovarian Cancer.Journal of oncology · 2022Article
- Glycosylation-Related Genes Predict the Prognosis and Immune Fraction of Ovarian Cancer Patients Based on Weighted Gene Coexpression Network Analysis (WGCNA) and Machine Learning.Oxidative medicine and cellular longevity · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ovarian cancer is one of three major malignancies of the female reproductive system. DNA methylation (MET) is closely related to ovarian cancer occurrence and development, and as such, elucidation of effective MET subtype markers may guide individualized treatment and improve ovarian cancer prognosis. To identify potential markers, we downloaded a total of 571 ovarian cancer MET samples from The Cancer Genome Atlas (TCGA), and established a Cox proportional hazards model using the MET spectrum and clinical pathological parameters. A total of 250 prognosis-related MET loci were obtained by Cox regression, and six molecular subtypes were screened by consensus clustering of CpG loci with a significant difference in both univariate and multivariate analyses. There was a remarkable MET difference between most subtypes. Cluster 2 had the highest MET level and demonstrated the best prognosis, while Clusters 4 and 5 had MET levels significantly lower than those of the other subtypes and demonstrated very poor prognosis. All Cluster 5 samples were at a high grade, while the percentage of stage IV samples in Cluster 4 was greater than in the other subtypes. We obtained five CpG loci using a coexpression network: cg27625732, cg00431050, cg22197830, cg03152385, and cg22809047. Our cluster analysis showed that prognosis in patients with hypomethylation was significantly worse than in patients with hypermethylation. These MET molecular subtypes can be used not only to evaluate ovarian cancer prognosis, but also to fully distinguish the tumor stage and histological grade in patients with ovarian cancer.
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