Developing a prognostic model of glutamine metabolism-related genes associated with clinical features and immune status in melanoma
Year: 2025
Type: article
Abstract: 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.
Source: Frontiers in Oncology
Authors: Hongyan Hu, Jing Yang, Jin Miao, Chen Li, Cao Wang, Fengming Ran, Jie Zou, Yi Zhang, Liufang Zhao, Wentao Zhao, Conghui Ai
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $3,801
OpenAlex ID: W4413362959
