Open Access

ARTICLE

Characterization of prognosis and immune infiltration by a novel glutamine metabolism-related model in cutaneous melanoma

by MENGQIN ZHU^1,2,3,4,#, TIANYI XU^5,#, HAN ZHANG^3,4, XIN FAN^3,4, YULAN WANG⁶, JIAJIA ZHANG^3,4, FEI YU^1,2,3,4,*

1 Shanghai Clinical College, Anhui Medical University, Shanghai, 200040, China
2 The Fifth Clinical Medical College, Anhui Medical University, Hefei, 230032, China
3 Department of Nuclear Medicine, Shanghai Tenth People’s Hospital, Tongji University School of Medicine, Shanghai, 200040, China
4 Institute of Nuclear Medicine, Tongji University School of Medicine, Shanghai, 200040, China
5 National Genomics Data Center and CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, China
6 Department of Biomedical Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210000, China

* Corresponding Author: FEI YU. Email:
# These authors contributed equally to this work and should be considered as co-first authors

BIOCELL 2023, 47(9), 1931-1945. https://doi.org/10.32604/biocell.2023.028968

Received 19 January 2023; Accepted 24 May 2023; Issue published 28 September 2023

Abstract

Glutamine metabolism (GM) plays an important role in tumor growth and proliferation. Skin cutaneous melanoma (SKCM) is a glutamine-dependent cancer. However, the molecular characteristics and action mechanism of GM on SKCM remain unclear. Therefore, we aimed to explore the effects of GM-related genes on survival, clinicopathological characteristics, and the tumor microenvironment in SKCM. In this study, 682 SKCM samples were obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Consensus clustering was used to classify SKCM samples into distinct subtypes based on 41 GM-related genes. Differences in survival, immune infiltration, clinical characteristics, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways as well as differentially expressed genes (DEGs) between subgroups were evaluated. A prognostic model was constructed according to prognostic DEGs. Differential analyses in survival, immune infiltration, tumor microenvironment (TME), tumor mutation burden (TMB), stemness, and drug sensitivity between risk groups were conducted. We identified two distinct GM-related subtypes on SKCM and found that GM-related gene alterations were associated with survival probability, clinical features, biological function, and immune infiltration. Then a risk model based on six DEGs (IL18, SEMA6A, PAEP, TNFRSF17, AIM2, and CXCL10) was constructed and validated for predicting overall survival in SKCM patients. The results showed that the risk score was negatively correlated with CD8⁺ T cells, activated CD4⁺ memory T cells, M1 macrophages, and γ δ T cells. The group with a low-risk score was accompanied by a better survival rate with higher TME scores and lower stemness index. Moreover, the group with high- and low-risk score had a significant difference with the sensitivity of 75 drugs (p < 0.001). Overall, distinct subtypes in SKCM patients based on GM-related genes were identified and the risk model was constructed, which might contribute to prognosis prediction, guide clinical therapy, and develop novel therapeutic strategies.

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Supplementary Material

Supplementary Material File

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