Abstract
Uveal melanoma (UM) is a highly metastatic cancer with resistance to immunotherapy. The present study aimed to identify novel feature genes and molecular mechanisms in UM through analysis of single-cell sequencing data. For this purpose, data were downloaded from The Cancer Genome Atlas and National Center for Biotechnology Information Gene Expression Omnibus public databases. The statistical analysis function of the CellPhoneDB software package was used to analyze the ligand-receptor relationships of the feature genes. The Metascape database was used to perform the functional annotation of notable gene sets. The randomForestSRC package and random survival forest algorithm were applied to screen feature genes. The CIBERSORT algorithm was used to analyze the RNA-sequencing data and infer the relative proportions of the 22 immune-infiltrating cell types. In vitro, small interfering RNAs were used to knockdown the expression of target genes in C918 cells. The migration capability and viability of these cells were then assessed by gap closure and Cell Counting Kit-8 assays. In total, 13 single-cell sample subtypes were clustered by t-distributed Stochastic Neighbor Embedding and annotated by the R package, SingleR, into 7 cell categories: Tissue stem cells, epithelial cells, fibroblasts, macrophages, natural killer cells, neurons and endothelial cells. The interactions in NK cells|Endothelial cells, Neurons|Endothelial cells, CD74_APP, and SPP1_PTGER4 were more significant than those in the other subsets. T-Box transcription factor 2, tropomyosin 4, plexin D1 (PLXND1), G protein subunit α I2 (GNAI2) and SEC14-like lipid binding 1 were identified as the feature genes in UM. These marker genes were found to be significantly enriched in pathways such as vasculature development, focal adhesion and cell adhesion molecule binding. Significant correlations were observed between key genes and immune cells as well as immune factors. Relationships were also observed between the expression levels of the key genes and multiple disease-related genes. Knockdown of PLXND1 and GNAI2 expression led to significantly lower viability and gap closure rates of C918 cells. Therefore, the results of the present study uncovered cell communication between endothelial cells and other cell types, identified innovative key genes and provided potential targets of gene therapy in UM.