Development and Validation of a Novel Metabolic Signature-Based Prognostic Model for Uveal Melanoma

Abstract

Purpose: Uveal melanoma (UM) is the most common primary malignant tumor with poor prognosis. The role of metabolism-related genes in the prognosis of UM remains unrevealed. This study aimed to establish and validate a prognostic prediction model for UM based on metabolism-related genes.

Methods: Gene expression profiles and clinicopathological information were downloaded from The Cancer Genome Atlas, and the Gene Expression Omnibus database. Univariable Cox regression, least absolute shrinkage and selection operator Cox regression, and stepwise regression were performed to establish the model. Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve analysis, and calibration and discrimination analyses were used to evaluate the prognostic model.

Results: Three metabolism-related genes, carbonic anhydrase 12, acyl-CoA synthetase long-chain family member 3, and synaptojanin 2, and three clinicopathological parameters (i.e., age, gender, and metastasis staging) were identified to establish the model. The risk score was found to be an independent prognostic factor for UM survival. High-risk patients demonstrated significantly poorer prognosis than low-risk patients. ROC analysis suggested the promising prognostic efficiency of the model. The calibration curve manifested satisfactory agreement between the predicted and observed risk. A nomogram and online survival calculator were developed to predict the survival probability.

Conclusions: The novel metabolism-based prognostic model could accurately predict the prognosis of UM patients, which facilitates the prediction of the survival probability by both ophthalmologists and patients with the online dynamic nomogram.

Translational relevance: The dynamic nomogram links gene expression profiles to clinical prognosis of UM and is useful to evaluate the survival probability.

Figure 1.

Brief flow chart of this study. NRI, net reclassification index; IDI, integrated discrimination improvement; C-index, concordance index.

Figure 2.

Construction of the prognostic model. (A) Forest plot showing the hazard ratio of 23 metabolism-related gene signatures associated with overall survival time of the TCGA-UVM cohort through univariable Cox regression. (B) Selection of tuning parameter (lambda) in the LASSO model with 100-fold cross-validation. (C) LASSO coefficient profiles of 23 metabolism-related genes in the TCGA-UVM cohort. Each coefficient profile plot was generated versus the log (lambda) sequence. (D) ROC analysis of the preliminary model (black curve) and the optimized model (red curve) with 500 iterations of bootstrap resampling. (E, F) The risk score distribution of the patients from the TCGA-UVM cohort (E) and GSE22138 cohort (F) were plotted in ascending order and colored as high-risk (red) and low-risk (green).

Figure 3.

Prognostic value of the risk score and three metabolism-related gene signatures. In the TCGA-UVM cohort, both univariate (A) and multivariate (B) Cox regression analyses indicate that the risk score was a powerful independent predictor associated with overall survival (P < 0.001, HR = 2.041, 95% CI = 1.515-2.751; P < 0.001, HR = 3.967, 95% CI = 1.761-8.937, respectively). In the GSE22138 cohort, both univariate (C) and multivariate (D) Cox regression analyses indicate that the risk score was a powerful independent predictor associated with overall survival (P < 0.001, HR = 6.584, 95% CI = 2.719-15.946; P < 0.001, HR = 14.013, 95% CI = 2.874-68.332, respectively). Spearman correlation show weak correlations between the three metabolism-related gene signatures in the TCGA-UVM cohort (E) and GSE22138 cohort (F). A heatmap shows that the three metabolism-related gene signatures were upregulated in the high-risk group in the TCGA-UVM cohort (G) and GSE22138 cohort (H).

Figure 4.

Prognostic evaluation performance of the prediction model. Kaplan-Meier curve of the training cohort (A) and validation cohort (B). (C) Bar plot shows the mortality proportions in the high- and low-risk groups of the training cohort. (D) Dot plot displays patient status distributions and death event occurrences in the high- and low-risk groups of the training cohort. (E) Bar plot shows the mortality proportions in the high- and low-risk groups of the validation cohort. (F) Dot plot displays patient status distributions and death event occurrences in the high- and low-risk groups of the validation cohort. ROC analysis reveals the predictive efficiency of the optimized model in one-, three-, and five-year survival probability in the training cohort (G) and validation cohort (H). (I) Calibration curve of the optimized model showed satisfactory agreement between the predicted risk (black curve) and observed risk (red diagonal).

Figure 5.

A nomogram and online calculator were developed for the prediction model. (A) The nomogram was established by using the parameters of age, gender, M stage, and the expression of CA12, ACSL3, and SYNJ2. (B) QR code of our online calculator website to predict the survival probability. (C) The user interface of the online calculator, including the input section on the left and output section on the right. The forest plot on the right section displays multiple prediction results.

Figure 6.

The significantly enriched KEGG pathways in the high-risk group of the TCGA-UVM cohort and GSE22138 cohort determined by GSEA. Common upregulated KEGG-Proteasome pathway and KEGG-ABC transporters pathway in the high-risk group of the TCGA-UVM cohort (A) and GSE22138 cohort (B). (C) Six upregulated metabolism-related KEGG pathways in the TCGA-UVM cohort. (D) One upregulated metabolism-related KEGG pathway in the GSE22138 cohort. ABC, ATP-binding cassette.