ArticleAnalytical cellular pathology (Amsterdam)2021
A Novel 8-Gene Prognostic Signature for Survival Prediction of Uveal Melanoma.
Article in Analytical cellular pathology (Amsterdam), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- Clinical Applications of Artificial Intelligence in Uveal Melanoma.Anticancer research · 2025Review
- SLC25A38 as a novel biomarker for metastasis and clinical outcome in uveal melanoma.Cell death & disease · 2022Article
- Low Level of PALMD Contributes to the Metastasis of Uveal Melanoma.Frontiers in oncology · 2022Article
- Prognostic Biomarkers in Uveal Melanoma: The Status Quo, Recent Advances and Future Directions.Cancers · 2021Review
- Cancer Therapeutic Potential and Prognostic Value of the SLC25 Mitochondrial Carrier Family: A Review.Cancer control : journal of the Moffitt Cancer CenterReview
Corrections and comments
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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
backgroundUveal melanoma (UM) has favorable local tumor control, but once metastasis develops, the prognosis is rather poor. Thus, it is urgent to develop metastasis predicting markers.
objectiveOur study investigated a novel gene expression-based signature in predicting metastasis for patients with UM.
methodsIn the discovery phase, 63 patients with UM from GEO data set GSE22138 were analyzed using the Weighted Correlation Network Analysis (WGCNA) to identify metastasis-related hub genes. The Least Absolute Shrinkage and Selection Operator (Lasso) Cox regression was used to select candidate genes and build a gene expression signature. In the validation phase, the signature was validated in The Cancer Genome Atlas database.
resultsForty-one genes were identified as hub genes of metastasis by WGCNA. After the Lasso Cox regression analysis, eight genes including RPL10A, EIF1B, TIPARP, RPL15, SLC25A38, PHLDA1, TFDP2, and MEGF10 were highlighted as candidate predictors. The gene expression signature for UM (UMPS) could independently predict MFS by univariate and multivariate Cox regression analysis. Incorporating UMPS increased the AUC of the traditional clinical model. In the validation cohort, UMPS performed well in predicting the MFS of UM patients.
conclusionsUMPS, an eight-gene-based signature, is useful in predicting prognosis for patients with UM.
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