Identification of Survival-Related Metabolic Genes and a Novel Gene Signature Predicting the Overall Survival for Patients with Uveal Melanoma

• Published in: Ophthalmic research 2022-10, Vol.65 (5), p.516-528
• Main Authors: Guo, Xiaoyu, Yu, Xin, Li, Fang, Xia, Qinyun, Ren, He, Chen, Zhen, Xing, Yiqiao
• Format: Article
• Language: English
• Subjects: Cancer; Development and progression; Gene expression; Genes; Genetic aspects; Genomes; Genomics; Medical research; Medicine, Experimental; Melanoma; Patient outcomes; Physiological aspects; Research Article; RNA; RNA sequencing
• ISSN: 0030-3747; 1423-0259
• DOI: 10.1159/000524505

Abstract Introduction: Uveal melanoma (UM) is the most common primary intraocular malignancy among adults. Altered metabolism has been shown to contribute to the development of cancer closely, but the prognostic role of metabolism in UM remains to be explored. This study aimed to construct a metabolic-related signature for UM. Method: We collected the mRNA sequencing data and corresponding clinical information from The Cancer Genome Atlas and Gene Expression Omnibus databases. A univariate Cox regression analysis, the Lasso-penalized Cox regression analysis, and multivariate Cox regression analyses were used to construct a metabolic signature based on TCGA. The time-dependent ROC and Kaplan-Meier survival curves were calculated to validate the prognostic ability of the signature. The immune-related features and mutation profile were characterized by CIBERSORT and maftools between high- and low-risk groups. Result: A novel metabolic-related signature (risk score = −0.246*SLC25A38 − 0.50186*ABCA12 + 0.032*CA12 + 0.086*SYNJ2) was constructed to predict the prognosis of UM patients. In TCGA and GSE22138, the signature had high sensitivity and specificity in predicting the prognosis of UM patients (survival probability; p < 0.0001, p = 0.012) . Gene Ontology pathway enrichment analysis and GSEA were used to discriminate several significantly enriched metabolism-related pathways, including channel activity and passive transmembrane transporter activity, which may reveal the underlying mechanisms. The high-risk group had more immune cell infiltration and greater distribution of BAP1 mutations. Conclusion: Our study developed a robust metabolic-gene signature based on TCGA to predict the prognosis of UM patients. The signature indicates a dysregulated metabolic microenvironment and provides new metabolic biomarkers and therapeutic targets for UM patients.