Construction and validation of a novel prognostic signature for uveal melanoma based on five metabolism-related genes

Identification and validation of immunogenic cell death-related score in uveal melanoma to improve prediction of prognosis and response to immunotherapy

BACKGROUND

Immunogenic cell death (ICD) could activate innate and adaptive immune response. In this work, we aimed to develop an ICD-related signature in uveal melanoma (UVM) patients and facilitate assessment of their prognosis and immunotherapy.

METHODS

A set of machine learning methods, including non-negative matrix factorization (NMF) method and least absolute shrinkage and selection operator (LASSO) logistic regression model, and bioinformatics analytic tools were integrated to construct an ICD-related risk score (ICDscore). CIBERSORT and ESTIMATE algorithms were used to evaluate the infiltration of immune cells. The Genomics of Drug Sensitivity in Cancer (GDSC), cellMiner and tumor immune dysfunction and exclusion (TIDE) databases were used for therapy sensitivity analyses. The predictive performance between ICDscore with other mRNA signatures was also compared.

RESULTS

The ICDscore could predict the prognosis of UVM patients in both the training and four validating cohorts. The ICDscore outperformed 19 previously published signatures. Patients with high ICDscore exhibited a substantial increase in immune cell infiltration and expression of immune checkpoint inhibitor-related genes, leading to a higher response rate to immunotherapy. Furthermore, the downregulation of poly (ADP-ribose) polymerase family member 8 (PARP8), a critical gene involved in the development of the ICDscore, resulted in decreased cell proliferation and slower migration of UVM cells.

CONCLUSION

In conclusion, we developed a robust and powerful ICD-related signature for evaluating the prognosis and benefits of immunotherapy that could serve as a promising tool to guide decision-making and surveillance for UVM patients.

A Novel Glycolysis-Related Signature for Predicting the Prognosis and Immune Infiltration of Uveal Melanoma

INTRODUCTION

As the most common aggressive intraocular cancer in adults, uveal melanoma (UVM) threatens the survival and vision of many people. Glycolysis is a novel hallmark of cancer, but the role of glycolysis-related genes in UVM prognosis remains unknown. The purpose of the study was to establish a glycolysis-related gene signature (GRGS) to predict UVM prognosis.

METHODS

Raw data were obtained from TCGA-UVM and GSE22138 datasets. The GRGS was established by univariate, LASSO, and multivariate Cox regression analyses. Kaplan-Meier survival and time-dependent receiver operating characteristic curves were used to evaluate the predictive ability of the GRGS. The relationships of the GRGS with infiltrating immune cell levels and mutations were analyzed with CIBERSORT and maftools.

RESULTS

A novel GRGS (risk score = 0.690861*ISG20 + 0.070991*MET - 0.227520*SDC2 + 0.690223*FBP1 + 0.048008*CLN6 - 0.128520*SDC3) was developed for predicting UVM prognosis. The GRGS had robust predictive stability in UVM. Enrichment annotation suggested that the high-risk group had stronger adaptive immune responses and that the low-risk group had more innate immune cell infiltration. Moreover, BAP1 mutation was related to high risk, and SF3B1 mutation was related to low risk.

CONCLUSIONS

This study developed and validated a novel GRGS to predict UVM prognosis and immune infiltration. The signature revealed an association between glycolysis-related genes and the tumor microenvironment, providing new insights into the role of glycolysis in UVM.

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

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.