Identification and Validation of an E2F Targets-related Gene Signature Risk Score Predicting the Prognosis of Patients with Uveal Melanoma

Introduction: Uveal melanoma (UM) is a challenging malignancy, in terms of diagnosis, risk stratification, and treatment associated with high morbidity and mortality rates. It has been demonstrated that E2F-related pathways play a significant role in the tumorigenesis and distant metastasis of UM. In this study, the E2F targets-related genes were utilized to construct and validate a prognostic risk score for patients with UM. Methods: Using the TCGA-UVM cohort (n = 80), 192 E2F target genes were screened using gene set enrichment analysis (GSEA) to identify survival-associated genes. Prognostic genes were filtered using Kaplan–Meier analysis, univariate Cox regression, and LASSO, followed by multivariate Cox regression to construct a risk score model. The model was validated using the GSE22138 cohort (n = 63). Functional annotations of the risk score and its impact on stratifying tumor immune microenvironment components were assessed. Results: A total of 9 genes (CDC25B, NME1, RFC2, PRDX4, NASP, UBE2S, PRKDC, MCM6, and LBR) passed the model construction pipeline. The risk score categorization system showed an independent prognostic power (HR: 2.34, 95% CI: 1.11-4.90, P = .025) and a good predictability of the survival outcome (receiver operating characteristic curve analysis: area under the curve = 0.730, 95% CI: 0.60-0.86, P = .002). When analyzing the most frequently mutated gene cohorts, the risk score was significantly lower in the mutated subgroups of GNAQ, SF3B1, and EIF1AX. In contrast, the risk score was notably higher in the BAP1 mutated sub-group. Copy number analysis of chromosomal arms showed significant correlations between the risk score and 1q, 3q, 3p, 6p, 8q. The high-risk group showed significant infiltration for NK-cells, plasma B cells, gamma delta T-cells, follicular T-cells, M1 and M2 macrophages with lower infiltration of common myeloid progenitor cells. In addition, the high-risk group showed higher immune and microenvironment scores. Conclusion: The developed E2F targets-related gene model offers a robust tool for predicting the prognosis of UM patients. As a potential risk stratification method for UM, this model could have clinical applications pending further evaluation.

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Publication Details

Journal
Ocular Oncology and Pathology
Published
2026-05-14
DOI
https://doi.org/10.1159/000552459
Primary Topic
Ocular Oncology and Treatments
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article
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article

Identification and Validation of an E2F Targets-related Gene Signature Risk Score Predicting the Prognosis of Patients with Uveal Melanoma

Amer Alemam, Abdelwahab Aleshawi, R.K. Jadallah, Asem Alqudah et al.
Ocular Oncology and Pathology
Ocular Oncology and Treatments
article

Identification and Validation of an E2F Targets-related Gene Signature Risk Score Predicting the Prognosis of Patients with Uveal Melanoma

Amer Alemam, Abdelwahab Aleshawi, R.K. Jadallah, Asem Alqudah, Jawad M Al-Karaki, Reem Darweesh, S. Al-Omari, Hashem Abu Serhan, Rami Al-Dwairi, Tamam El-Elimat, Ayman G. Elnahry, Mais B. Tashtoush, Ahmed H. Al Sharie
article en

Abstract

Introduction: Uveal melanoma (UM) is a challenging malignancy, in terms of diagnosis, risk stratification, and treatment associated with high morbidity and mortality rates. It has been demonstrated that E2F-related pathways play a significant role in the tumorigenesis and distant metastasis of UM. In this study, the E2F targets-related genes were utilized to construct and validate a prognostic risk score for patients with UM. Methods: Using the TCGA-UVM cohort (n = 80), 192 E2F target genes were screened using gene set enrichment analysis (GSEA) to identify survival-associated genes. Prognostic genes were filtered using Kaplan–Meier analysis, univariate Cox regression, and LASSO, followed by multivariate Cox regression to construct a risk score model. The model was validated using the GSE22138 cohort (n = 63). Functional annotations of the risk score and its impact on stratifying tumor immune microenvironment components were assessed. Results: A total of 9 genes (CDC25B, NME1, RFC2, PRDX4, NASP, UBE2S, PRKDC, MCM6, and LBR) passed the model construction pipeline. The risk score categorization system showed an independent prognostic power (HR: 2.34, 95% CI: 1.11-4.90, P = .025) and a good predictability of the survival outcome (receiver operating characteristic curve analysis: area under the curve = 0.730, 95% CI: 0.60-0.86, P = .002). When analyzing the most frequently mutated gene cohorts, the risk score was significantly lower in the mutated subgroups of GNAQ, SF3B1, and EIF1AX. In contrast, the risk score was notably higher in the BAP1 mutated sub-group. Copy number analysis of chromosomal arms showed significant correlations between the risk score and 1q, 3q, 3p, 6p, 8q. The high-risk group showed significant infiltration for NK-cells, plasma B cells, gamma delta T-cells, follicular T-cells, M1 and M2 macrophages with lower infiltration of common myeloid progenitor cells. In addition, the high-risk group showed higher immune and microenvironment scores. Conclusion: The developed E2F targets-related gene model offers a robust tool for predicting the prognosis of UM patients. As a potential risk stratification method for UM, this model could have clinical applications pending further evaluation.

Ocular Oncology and Pathology
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