A novel inflammatory response-related signature predicts the prognosis of skin cutaneous melanoma and the effect of anti-tumor drugs

Abstract

Skin cutaneous melanoma (SKCM) is a skin cancer that is highly metastatic and aggressive, with a dismal prognosis. This is the first study to use inflammatory response-related genes to build a model and evaluate their predictive significance in SKCM. This study used public databases to download SKCM patients’ mRNA expression profiles and clinical data to create multigene prognostic markers in the UCSC cohort. We compared overall survival (OS) between high- and low-risk groups using the Kaplan-Meier curve and determined independent predictors using Cox analysis. We also used enrichment analysis to assess immune cell infiltration fraction and immune pathway-related activity using KEGG enrichment analysis. Finally, we detected prognostic genes’ mRNA and protein expression in SKCM and normal skin tissues using qRT-PCR and IHC (data from Human Protein Atlas). Finally, we developed a 5-gene predictive model that showed that patients in the high-risk group had a considerably shorter OS than those in the low-risk group. The analysis of the receiver operating characteristic (ROC) curve proved the model’s predictive ability. Furthermore, we conducted a drug sensitivity analysis and discovered that the expression levels of prognostic genes were substantially linked with cancer cell sensitivity to antitumor medicines. The findings show that the model we developed, which consists of five inflammatory response-related genes, can be used to forecast the prognosis and immunological state of cutaneous melanoma, giving personalized medicine and precision medicine a new goal and direction.