Prognostic Implications of Novel Ten-Gene Signature in Uveal Melanoma

Abstract

Background: Uveal melanoma (UM) is the most common primary intraocular cancer in adults. Genomic studies have provided insights into molecular subgroups and oncogenic drivers of UM that may lead to novel therapeutic strategies. Methods: Dataset TCGA-UVM, download from TCGA portal, were taken as the training cohort, and dataset GSE22138, obtained from GEO database, was set as the validation cohort. In training cohort, Kaplan-Meier analysis and univariate Cox regression model were applied to preliminary screen prognostic genes. Besides, the LASSO Cox regression model was implemented to build a multi-gene signature, which was then validated in the validation cohorts through Kaplan-Meier, Cox, and ROC analyses. In addition, the correlation between copy number aberrations and risk score was evaluated by Spearman test. GSEA and immune infiltrating analyses were conducted for understanding function annotation and the role of the signature in the tumor microenvironment. Results: A ten-gene signature was built, and it was examined by Kaplan-Meier analysis revealing that significantly overall survival, progression-free survival, and metastasis-free survival difference was seen. The ten-gene signature was further proven to be independent of other clinic-pathologic parameters via the Cox regression analyses. Moreover, the receiver operating characteristic curve (ROC) analysis demonstrated that this signature owned a better predictive power of UM prognosis. The ten-gene signature was significantly correlated with copy numbers of chromosome 3, 8q, 6q, and 6p. Furthermore, GSEA and immune infiltrating analyses showed that the signature had close interactions with immune-related pathways and the tumor environment. Conclusions: Identifying the ten-gene signature (SIRT3, HMCES, SLC44A3, TCTN1, STPG1, POMGNT2, RNF208, ANXA2P2, ULBP1, and CA12) could accurately identify patients’ prognosis and had close interactions with the immunodominant tumor environment, which may provide insight into personalized prognosis prediction and new therapies for UM patients.