ArticleJournal of Cancer2019
Identification of Prognostic Signatures for Predicting the Overall Survival of Uveal Melanoma Patients.
Article in Journal of Cancer, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Integrated bioinformatics and deep learning (MLP) approach reveals a novel five miRNA prognostic signature in uveal melanoma.Scientific reports · 2025Article
- Prognostic evaluation and experimental validation of cuproptosis-related hub genes identified through weighted gene co-expression network analysis in uveal melanoma.Cancer cell international · 2025Article
- Identification of multiomics map and key biomarkers in uveal melanoma with chromosome 3 loss.Annals of medicine and surgery (2012) · 2024Article
- Immunogenic profiling of metastatic uveal melanoma discerns a potential signature related to prognosis.Journal of cancer research and clinical oncology · 2024Article
- A transfer learning approach on MRI-based radiomics signature for overall survival prediction of low-grade and high-grade gliomas.Medical & biological engineering & computing · 2023Article
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- Integrated single-cell and transcriptome sequencing analyses develops a metastasis-based risk score system for prognosis and immunotherapy response in uveal melanoma.Frontiers in pharmacology · 2023Article
- Tumor subtypes and signature model construction based on chromatin regulators for better prediction of prognosis in uveal melanoma.Pathology oncology research : POR · 2023Article
- Lactate Rewrites the Metabolic Reprogramming of Uveal Melanoma Cells and Induces Quiescence Phenotype.International journal of molecular sciences · 2022Article
- Development and Validation of a Novel Metabolic Signature-Based Prognostic Model for Uveal Melanoma.Translational vision science & technology · 2022Article
- Extraction and Prioritization of a Gene-Cancer-By-Survival Network Involved in Homeostasis of Intracellular Calcium Concentrations Using TCGA PANCAN Data.Bioelectricity · 2022Article
- The Role of Metalloproteinases and Their Tissue Inhibitors on Ocular Diseases: Focusing on Potential Mechanisms.International journal of molecular sciences · 2022Review
- Integrative analysis identifies key genes related to metastasis and a robust gene-based prognostic signature in uveal melanoma.BMC medical genomics · 2022Article
- Machine Learning-Based Integration Develops a Pyroptosis-Related lncRNA Model to Enhance the Predicted Value of Low-Grade Glioma Patients.Journal of oncology · 2022Article
- Prognostic Biomarkers in Uveal Melanoma: The Status Quo, Recent Advances and Future Directions.Cancers · 2021Review
- Risk Score Generated from CT-Based Radiomics Signatures for Overall Survival Prediction in Non-Small Cell Lung Cancer.Cancers · 2021Article
- Prognostic significance of PD-1/PD-L1 expression in uveal melanoma: correlation with tumor-infiltrating lymphocytes and clinicopathological parameters.Cancer immunology, immunotherapy : CII · 2021Article
- A Glycolysis-Based Long Non-coding RNA Signature Accurately Predicts Prognosis in Renal Carcinoma Patients.Frontiers in genetics · 2021Article
- A Novel 8-Gene Prognostic Signature for Survival Prediction of Uveal Melanoma.Analytical cellular pathology (Amsterdam) · 2021Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Uveal melanoma (UM) is an aggressive cancer which has a high percentage of metastasis and with a poor prognosis. Identifying the potential prognostic markers of uveal melanoma may provide information for early detection of metastasis and treatment. In this work, we analyzed 80 uveal melanoma samples from The Cancer Genome Atlas (TCGA). We developed an 18-gene signature which can significantly predict the prognosis of UM patients. Firstly, we performed a univariate Cox regression analysis to identify significantly prognostic genes in uveal melanoma (P<0.01). Then the glmnet Cox analysis was used to generate a powerful prognostic gene model. Further, we established a risk score formula for every patient based on the 18-gene prognostic model with multivariate Cox regression. We stratified patients into high- and low-risk subtypes with median risk score and found that patients in high-risk group had worse prognosis than patients in low-risk group. Multivariate Cox regression analysis demonstrated that 18-gene model risk score was independent of clinical prognostic factors. We identified four genes whose mutations were closely to UM patients' prognosis or risk score. We also explored the relationship between copy number variation and risk score and found that high risk group showed more chromosome aberrations than low risk group. Gene Set Enrichment Analysis (GSEA) analysis showed that the different biological pathways and functions between low and high risk group. In summary, our findings constructed an 18-gene signature for estimating overall survival (OS) of UM. Patients were categorized into two subtypes based on the risk score and we found that high risk group showed more chromosome aberrations than low risk group.
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