ArticleFrontiers in surgery2023
Development of prognostic indicator based on NAD+ metabolism related genes in glioma.
Article in Frontiers in surgery, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- The role of NADFrontiers in oncology · 2026Review
- Causal inference between immune cells and glioblastoma: a bidirectional Mendelian randomization study.Journal of Cancer · 2025Article
- Bioinformatics-based analysis of nicotinamide adenine dinucleotide metabolism-related genes to predict immune status and prognosis for head and neck squamous cell carcinoma patients.Frontiers in immunology · 2025Article
- Genetic polymorphisms of bone marrow stromal cell antigen-1 (BST-1/CD157): implications for immune/inflammatory dysfunction in neuropsychiatric disorders.Frontiers in immunology · 2023Review
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13 authors.
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Abstract
Background: Studies have shown that Nicotinamide adenine dinucleotide (NAD+) metabolism can promote the occurrence and development of glioma. However, the specific effects and mechanisms of NAD+ metabolism in glioma are unclear and there were no systematic researches about NAD+ metabolism related genes to predict the survival of patients with glioma. Methods: The research was performed based on expression data of glioma cases in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Firstly, TCGA-glioma cases were classified into different subtypes based on 49 NAD+ metabolism-related genes (NMRGs) by consensus clustering. NAD+ metabolism-related differentially expressed genes (NMR-DEGs) were gotten by intersecting the 49 NMRGs and differentially expressed genes (DEGs) between normal and glioma samples. Then a risk model was built by Cox analysis and the least shrinkage and selection operator (LASSO) regression analysis. The validity of the model was verified by survival curves and receiver operating characteristic (ROC) curves. In addition, independent prognostic analysis of the risk model was performed by Cox analysis. Then, we also identified different immune cells, HLA family genes and immune checkpoints between high and low risk groups. Finally, the functions of model genes at single-cell level were also explored. Results: Consensus clustering classified glioma patients into two subtypes, and the overall survival (OS) of the two subtypes differed. A total of 11 NAD+ metabolism-related differentially expressed genes (NMR-DEGs) were screened by overlapping 5,995 differentially expressed genes (DEGs) and 49 NAD+ metabolism-related genes (NMRGs). Next, four model genes, PARP9, BST1, NMNAT2, and CD38, were obtained by Cox regression and least absolute shrinkage and selection operator (Lasso) regression analyses and to construct a risk model. The OS of high-risk group was lower. And the area under curves (AUCs) of Receiver operating characteristic (ROC) curves were >0.7 at 1, 3, and 5 years. Cox analysis showed that age, grade G3, grade G4, IDH status, ATRX status, BCR status, and risk Scores were reliable independent prognostic factors. In addition, three different immune cells, Mast cells activated, NK cells activated and B cells naive, 24 different HLA family genes, such as HLA-DPA1 and HLA-H, and 8 different immune checkpoints, such as ICOS, LAG3, and CD274, were found between the high and low risk groups. The model genes were significantly relevant with proliferation, cell differentiation, and apoptosis. Conclusion: The four genes, PARP9, BST1, NMNAT2, and CD38, might be important molecular biomarkers and therapeutic targets for glioma patients.
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