Developing a prognostic model of glutamine metabolism-related genes associated with clinical features and immune status in melanoma
IntroductionMelanoma 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...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1485006/full |
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| author | Hongyan Hu Jing Yang Jin Miao Chen Li Cao Wang Fengming Ran Jie Zou Yi Zhang Liufang Zhao Wentao Zhao Conghui Ai |
| author_facet | Hongyan Hu Jing Yang Jin Miao Chen Li Cao Wang Fengming Ran Jie Zou Yi Zhang Liufang Zhao Wentao Zhao Conghui Ai |
| author_sort | Hongyan Hu |
| collection | DOAJ |
| description | IntroductionMelanoma 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.MethodsPatient 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.Resultseight 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.DiscussionOverall, it would provide valuable information about the GRGs of prognosis and immune status in melanoma. |
| format | Article |
| id | doaj-art-04ba06458fb84b9c9878d7eb0fcb13fc |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-04ba06458fb84b9c9878d7eb0fcb13fc2025-08-20T05:32:49ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-08-011510.3389/fonc.2025.14850061485006Developing a prognostic model of glutamine metabolism-related genes associated with clinical features and immune status in melanomaHongyan Hu0Jing Yang1Jin Miao2Chen Li3Cao Wang4Fengming Ran5Jie Zou6Yi Zhang7Liufang Zhao8Wentao Zhao9Conghui Ai10Department of Pathology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, ChinaDepartment of Oncology, First People’s Hospital of Kunming, Kunming, ChinaDepartment of Pathology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, ChinaScientific Research Department, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, ChinaDepartment of Orthopedics, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, ChinaDepartment of Pathology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, ChinaDepartment of Pathology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, ChinaDepartment of Gynecology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, ChinaDepartment of Head and Neck Cancer, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, ChinaDepartment of Gastrointestinal Oncology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, ChinaDepartment of Radiology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, ChinaIntroductionMelanoma 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.MethodsPatient 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.Resultseight 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.DiscussionOverall, it would provide valuable information about the GRGs of prognosis and immune status in melanoma.https://www.frontiersin.org/articles/10.3389/fonc.2025.1485006/fullglutamine metabolismmelanomaprognosisimmune microenvironmentbioinformatics |
| spellingShingle | Hongyan Hu Jing Yang Jin Miao Chen Li Cao Wang Fengming Ran Jie Zou Yi Zhang Liufang Zhao Wentao Zhao Conghui Ai Developing a prognostic model of glutamine metabolism-related genes associated with clinical features and immune status in melanoma Frontiers in Oncology glutamine metabolism melanoma prognosis immune microenvironment bioinformatics |
| title | Developing a prognostic model of glutamine metabolism-related genes associated with clinical features and immune status in melanoma |
| title_full | Developing a prognostic model of glutamine metabolism-related genes associated with clinical features and immune status in melanoma |
| title_fullStr | Developing a prognostic model of glutamine metabolism-related genes associated with clinical features and immune status in melanoma |
| title_full_unstemmed | Developing a prognostic model of glutamine metabolism-related genes associated with clinical features and immune status in melanoma |
| title_short | Developing a prognostic model of glutamine metabolism-related genes associated with clinical features and immune status in melanoma |
| title_sort | developing a prognostic model of glutamine metabolism related genes associated with clinical features and immune status in melanoma |
| topic | glutamine metabolism melanoma prognosis immune microenvironment bioinformatics |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2025.1485006/full |
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