Abstract
Purpose
Uveal melanoma (UM) is the most common primary intraocular malignant tumor in adults with poor prognosis. Pyroptosis is a well-known form of programmed cell death. However, pyroptosis has not been sufficiently discussed in UM. This study aims to explore the expression patterns of pyroptosis-related genes (PRGs) and their relationship with tumor microenvironment (TME) characteristics and prognosis in UM.
Methods
In this study, unsupervised clustering analysis was performed based on the expression of 10 PRGs to identify pyroptotic subtypes. TME characteristics were evaluated by using the ssGSEA, ESTIMATE and CIBERSORT algorithms. In addition, a related scoring model (PRscore) was established to quantify pyroptotic patterns of individual UM patients via principal component analysis. The correlation between PRscore and TME characteristics was assessed by Spearman analysis. Furthermore, univariate and multivariate Cox regression analysis were performed to identify whether the PRscore can be an independent factor for predicting the overall survival (OS) of UM patients.
Results
The results revealed that there were two distinct pyroptotic patterns with different TME characteristics in UM. PRscore was found to be associated with TME characteristics and patient prognosis. In addition, combined with the clinical characteristics, the PRscore was found to be an independent factor for predicting the OS of UM patients. Furthermore, PRscore might be a useful tool for predicting the response to immunotherapy in UM patients.
Conclusions
The status of pyroptosis was associated with TME characteristics in UM. In addition, evaluating the pyroptotic pattern (Prscore) would help us to predict the prognosis and immunotherapy response of individual UM patient. Furthermore, our results may offer novel insights into the development of a promising strategy for treating UM, i.e. the combination of chemodrugs targetting the induction of pyroptosis and immune checkpoint inhibitors (ICIs).
Acknowledgments
We acknowledge TCGA and GEO database for providing their platforms and contributors for uploading their meaningful datasets.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
The datasets analyzed during the current study are available in the GEO database (https://www.ncbi.nlm.nih.gov/geo/) and TCGA database (https://portal.gdc.cancer.gov/).