ArticleLife (Basel, Switzerland)2020
Novel Methylation Patterns Predict Outcome in Uveal Melanoma.
Article in Life (Basel, Switzerland), 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, 11 citations in OpenAlex.
- Azacytidine treatment affects the methylation pattern of genomic and cell-free DNA in uveal melanoma cell lines.BMC cancer · 2024Article
- Article
- GZ17-6.02 kills PDX isolates of uveal melanoma.Oncotarget · 2024Article
- Machine Learning Methods for Gene Selection in Uveal Melanoma.International journal of molecular sciences · 2024Article
- Deep learning classification of uveal melanoma based on histopathological images and identification of a novel indicator for prognosis of patients.Biological procedures online · 2023Article
- Multiple epigenetic modification profiles reveal the tumor immune microenvironment and clinical outcomes of uveal melanoma.Frontiers in genetics · 2023Article
- A Novel Four Genes of Prognostic Signature for Uveal Melanoma.Journal of oncology · 2022Article
- BAP1 methylation: a prognostic marker of uveal melanoma metastasis.NPJ precision oncology · 2021Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Uveal melanoma (UM) is the most common intraocular tumor in adults. Despite effective local treatments, 50% of patients develop metastasis. Better ways to determine prognosis are needed as well as new therapeutic targets. Epigenetic changes are important events driving cancer progression; however, few studies exist on methylation changes in UM. Our aim was to identify methylation events associated with UM prognosis. Matched clinical, genetic, and methylation data for 80 UM cases were obtained from The Cancer Genome Atlas (TCGA). Top differentially methylated loci were sorted through hierarchical clustering based on methylation patterns, and these patterns were compared to tumor characteristics, genomic aberrations, and patient outcome. Hierarchical clustering revealed two distinct groups. These classifications effectively separated high and low-risk cases, with significant differences between groups in patient survival (
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Registered trials
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