Integrating Single-cell and Bulk RNA-seq to Construct a Metastasis-related Model for Evaluating Immunotherapy and Chemotherapy in Uveal Melanoma.

Abstract

Metastasis is a major cause of death in UM, highlighting the need to use highly specific and sensitive prognostic markers to identify patients with a risk of developing metastasis. The aim of this study was to improve the current precision treatment for patients with metastatic uveal melanoma (UM). The objective of this work was to investigate the heterogeneity between primary human UM and metastatic UM at the single-cell level and to discover potential molecules regulating UM metastasis. Seurat R toolkit was employed to analyze single-cell sequencing data of UM and to identify differentially expressed genes (DEGs) between primary and metastatic UM. Least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were performed on the DEGs from the bulk RNA-seq cohort to develop a prognostic model. Based on the model, patients were divided into high and low groups. The correlations among the risk score, immune indicators, immune checkpoint blockade (ICB) therapy, and anti-tumor drug therapy were analyzed. Cell types in primary UM and metastatic UM tumors include B/plasma cells, endothelial cells, melanocytes, monocytes/macrophages, photoreceptor cells, and T cells. Among 157 DEGs between the two tumor types, S100A4, PDE4B, CHCHD10, NSG1, and C4orf48 were selected to construct a prognostic model. The model could accurately and independently predict response to ICB treatment and sensitivity to antineoplastic drugs for UM patients as well as their immune infiltration levels, risk of death, and metastasis possibility. This study analyzed the tumor ecosystem of primary and metastatic UM, providing a metastasis-related model that could be used to evaluate the prognosis, risk of metastasis, immunotherapy, and efficacy of antineoplastic drug treatment of UM.