Uveal melanoma is an aggressive intraocular malignancy that often exhibits low immunogenicity. Metastatic uveal melanoma samples frequently exhibit monosomy 3 or BAP1 deficiency. In this study, we used bioinformatic methods to investigate the immune infiltration of uveal melanoma samples in public datasets. We first performed Gene Set Enrichment/Variation Analyses to detect immunological pathways that are altered in tumors with monosomy 3 or BAP1 deficiency. We then conducted an unsupervised clustering analysis to identify distinct immunologic molecular subtypes of uveal melanoma. We used CIBERSORT and ESTIMATE with RNA-seq data from The Cancer Genome Atlas and the GSE22138 microarray dataset to determine the sample-level immune subpopulations and immune scores of uveal melanoma samples. The Kaplan-Meier method and log-rank test were used to assess the prognostic value of particular immune cells and genes in uveal melanoma samples. Through these approaches, we discovered uveal melanoma-specific immunologic features, which may provide new insights into the tumor microenvironment and enhance the development of immunotherapies in the future.
Keywords: TCGA; bioinformatics; immune cell fractions; immune subtype; uveal melanoma.