Introduction Melanoma exhibited a poor prognosis due to its aggression and heterogeneity. The effect of glutamate metabolism promoting tumor progression on cutaneous melanoma remains unknown. Herein, glutamine metabolism-related genes (GRGs) were identified followed by constructing a prognostic model for melanoma via bioinformatics analysis. Methods Patient data were collected from ,Gene Expression Omnibus (GEO) and The Cancer Genome Atlas—Skin Cutaneous Melanoma (TCGA-SKCM). In addition, GRGs were extracted from the MSigDB database, and the R package "Seurat" was used for scRNA-seq data processing. Results eight key genes (CHMP4A, IFFO1, ANKRD10, ZDHHC11, CLPB, ANKMY1, TCAP and POLG2) were identified to construct a risk model. Based on univariate and multivariate Cox regression analyses, clinical characteristics including Clark stage and ulcer status were identified as independent prognostic factors, and a nomogram was successfully constructed. Survival analysis demonstrated that the overall survival rates of the high-risk group were lower than those of the low-risk group. The gene set enrichment analysis (GSEA) results showed that only ANKRD10, ANKMY1 and TCAP were enriched in the “glycolysis gluconeogenesis” pathway. The high-risk and low-risk groups displayed significant differences in immune cell infiltration and immune checkpoint expression. Analysis on drug sensitivity revealed that the high-risk group was highly sensitive to rapamycin. Additionally, it was verified that IFFO1, ANKRD10 and POLG2 were markedly upregulated and CHMP4A was also markedly downregulated in A375 cells by RT-PCR, which was consistent with the partial results of biological analysis. Discussion Overall, it would provide valuable information about the GRGs of prognosis and immune status in melanoma.
Hu et al. (2025) studied this question.