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Significance of Aneuploidy in Predicting Prognosis and Treatment Response of Uveal Melanoma


Xiaoqian Zhang, Ling Jin, Chenchen Zhou, Jinghua Liu and Qin Jiang*   Pages 1 - 16 ( 16 )


Aims: This study aimed to improve personalized treatment strategies and predict survival outcomes for patients with uveal melanoma (UM).

Background: Copy number aberrations (CNAs) have been considered as a main feature of metastatic UM.

Objective: This study was designed to explore the feasibility of using copy number variation (CNV) in UM classification, prognosis stratification and treatment response.

Methods: The CNV data in the TCGA-UVM cohort were used to classify the samples. The differentially expressed genes (DEGs) between subtypes were screened by the “Limma” package. The module and hub genes related to aneuploidy score were identified by performing weighted gene co-expression network analysis (WGCNA) on the DEGs. Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analysis were employed to train the hub genes for developing a prognosis model for UM. Finally, the expression levels of the screened prognostic key genes were verified in UM cells, and the cell migration and invasion abilities were detected using real-time quantitative PCR (qRT-PCR) and transwell assay.

Results: The UM samples were divided into 3 CNV subtypes, which differed significantly in overall survival (OS) and disease-specific survival (DSS). C1 had the shortest OS and DSS and the highest level of immune infiltration. A total of 2036 DEGs were obtained from the three subtypes. Eighty hub genes with the closest correlation with aneuploidy scores were selected by WGCNA. Univariate Cox and LASSO regression-based analyses finally determined eight genes as the key prognostic genes, including HES6, RNASEH2C, NQO1, NUDT14, TTYH3, GJC1, FKBP10, and MRPL24. A prognostic model was developed using the eight genes, demonstrating a strong prediction power. Differences in the response to immunotherapy among patients in different risk groups were significant. We found that high-risk patients were more sensitive to two drugs (Palbociclib_ 1054 and Ribociclib_1632), while low-risk patients were more sensitive to AZD1208_1449, ERK_2440_1713, Mirin_1048, and Selumetinib_1736.

Conclusion: UM in this study was divided into three CNV subtypes, and a model based on eight aneuploidy score-related genes was established to evaluate the prognosis and drug treatment efficacy of UM patients. The current results may have the potential to help the clinical decision-making process for UM management.


Copy number variation,uveal melanoma,risk model,prognosis,drug therapy


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