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

Abstract

Cutaneous melanoma (CM) 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 CM. This study used public databases to download CM 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. Furthermore, we detected prognostic genes’ mRNA and protein expression in CM and normal skin tissues using qRT-PCR and immunohistochemistry. 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. We also 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 CM, giving personalized and precision medicine a new goal and direction.

Integrating molecular pathway with genome-wide association data for causality identification in breast cancer

“…To execute the PCA analysis on the expression matrix pre and post correction, we employed the R packages ggplot2 and ggpubr, utilizing the bioPCA function. The outcomes were then visually represented using scatter plots [ 19 ].…”