ArticleBMC medical genomics2022
Integrative analysis identifies key genes related to metastasis and a robust gene-based prognostic signature in uveal melanoma.
Article in BMC medical genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed, 6 citations in OpenAlex.
- Ferroptosis-related ceRNA axis regulates the apoptosis and proliferation of uveal melanoma cells through MAPRE2.Functional & integrative genomics · 2026Article
- Development and Validation of an Extracellular Matrix Gene Expression Signature for Prognostic Prediction in Patients with Uveal Melanoma.International journal of molecular sciences · 2025Article
- 1,4-dihydroxy quininib activates ferroptosis pathways in metastatic uveal melanoma and reveals a novel prognostic biomarker signature.Cell death discovery · 2024Article
- Machine Learning Methods for Gene Selection in Uveal Melanoma.International journal of molecular sciences · 2024Article
- Interdependence of Molecular Lesions That Drive Uveal Melanoma Metastasis.International journal of molecular sciences · 2023Article
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeUveal melanoma (UM) is an aggressive intraocular malignancy, leading to systemic metastasis in half of the patients. However, the mechanism of the high metastatic rate remains unclear. This study aimed to identify key genes related to metastasis and construct a gene-based signature for better prognosis prediction of UM patients.
methodsWeighted gene co-expression network analysis (WGCNA) was used to identify the co-expression of genes primarily associated with metastasis of UM. Univariate, Lasso-penalized and multivariate Cox regression analyses were performed to establish a prognostic signature for UM patients.
resultsThe tan and greenyellow modules were significantly associated with the metastasis of UM patients. Significant genes related to the overall survival (OS) in these two modules were then identified. Additionally, an OS-predicting signature was established. The UM patients were divided into a low- or high-risk group. The Kaplan-Meier curve indicated that high-risk patients had poorer OS than low-risk patients. The receiver operating curve (ROC) was used to validate the stability and accuracy of the final five-gene signature. Based on the signature and clinical traits of UM patients, a nomogram was established to serve in clinical practice.
conclusionsWe identified key genes involved in the metastasis of UM. A robust five-gene-based prognostic signature was constructed and validated. In addition, the gene signature-based nomogram was created that can optimize the prognosis prediction and identify possible factors causing the poor prognosis of high-risk UM patients.
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