Predicting immunotherapy response in melanoma using a novel tumor immunological phenotype-related gene index

Abstract

Introduction: Melanoma is a highly aggressive and recurrent form of skin cancer, posing challenges in prognosis and therapy prediction.

Methods: In this study, we developed a novel TIPRGPI consisting of 20 genes using Univariate Cox regression and the LASSO algorithm. The high and low-risk groups based on TIPRGPI exhibited distinct mutation profiles, hallmark pathways, and immune cell infiltration in the tumor microenvironment.

Results: Notably, significant differences in tumor immunogenicity and TIDE were observed between the risk groups, suggesting a better response to immune checkpoint blockade therapy in the low-TIPRGPI group. Additionally, molecular docking predicted 10 potential drugs that bind to the core target, PTPRC, of the TIPRGPI signature.

Discussion: Our findings highlight the reliability of TIPRGPI as a prognostic signature and its potential application in risk classification, immunotherapy response prediction, and drug candidate identification for melanoma treatment. The "TIP genes" guided strategy presented in this study may have implications beyond melanoma and could be applied to other cancer types.

Keywords: bioinformatics; immunotherapy response; melanoma; molecular docking; prognosis; tumor microenvironment.