The combination of single-cell and bulk RNA sequencing analysis decodes the melanoma tumor microenvironment and identifies novel T cell-associated signature genes

Abstract

Abstract Background and aim: Skin cutaneous melanoma (SKCM) is a fatal type of skin cancer that arises from the malignant conversion of melanocytes. Different melanoma patients show various immunotherapy responses, which may involve many immunotherapy-related genes in T cells (IRTGs). This study aims to construct a risk score model that can predict the prognosis and immunotherapy response of SKCM patients. Methods: Our study conducted single-cell RNA sequencing (scRNA-seq) analysis on many melanoma patients from several public datasets. Herein, we screened candidategenes in the T cells of melanoma tumors that were highly associated with immunotherapy response. The candidate genes underwent LASSO and COX regression analyses to identify the signature genes. The immune activity was assessed in patients with different scores using ESTIMATE and CIBERSORT algorithms. Finally, the signature gene PEB4B and the immune microenvironment correlation was explored with multiple immunofluorescence (IF) and well-established biological algorithms. Results: Of the initial 33 candidate genes, we successfully identified six distinct signature IRTGs. Additionally, the computation of each patient risk score proved beneficial in evaluating the immune cell infiltration level and functions as an independent prognostic factor for melanoma patient survival. The risk score results revealed promising predictive outcomes in determining the response of melanoma patients to immunotherapy. Conclusion: Our findings demonstrate the potential utility of a risk score dependent on signature genes as a predictive tool for assessing the prognosis and response to immunotherapeutic interventions in melanoma patients.