Evidence map›Paper›PMID 40909968›Full record

ArticleFrontiers in oncology2025

Developing a prognostic model of glutamine metabolism-related genes associated with clinical features and immune status in melanoma.

Hongyan Hu, Jing Yang, Jin Miao, Chen Li, Cao Wang, Fengming Ran, Jie Zou, Yi Zhang, Liufang Zhao, Wentao Zhao and 1 more

Abstract read
In one paragraph

Article in Frontiers in oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Hongyan Hu *Department of Pathology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, China.
Jing Yang *Department of Oncology, First People's Hospital of Kunming, Kunming, China.
Jin Miao *Department of Pathology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, China.
Chen LiScientific Research Department, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, China.
Cao WangDepartment of Orthopedics, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, China.
Fengming RanDepartment of Pathology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, China.
Jie ZouDepartment of Pathology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, China.
Yi ZhangDepartment of Gynecology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, China.
Liufang ZhaoDepartment of Head and Neck Cancer, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, China.
Wentao ZhaoDepartment of Gastrointestinal Oncology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, China.
Conghui AiDepartment of Radiology, Yunnan Cancer Hospital, The Third Affiliated Hospital of Kunming Medical University, Peking University Cancer Hospital Yunnan, Kunming, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

bioinformaticsglutamine metabolismimmune microenvironmentmelanomaprognosis

Identifiers

PMID40909968
PMCPMC12405417

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.