Comprehensive Analysis to Reveal Nitrogen Metabolism-Associated Genes as a Prognostic Index in Head and Neck Squamous Cell Cancer
簡単な紹介:
- 著者: Yiming Shen, Wenfang Sun and Chunfu Dai
- ジャーナル: Current Medicinal Chemistry
- Doi: https://www.doi.org/10.2174/0109298673427009251022060122
- 発行日: 2026/1/9
Abstract
Background
Head and neck squamous cell carcinoma (HNSCC) has a poor prognosis and a high fatality rate. To predict the prognosis of HNSCC, this study developed a prognostic model based on nitrogen metabolism (NM)-related genes.
Methods
This study utilized transcriptomic data and clinical information from HNSCC obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify differentially expressed NM-related genes. Subsequently, an NM-related prognostic risk model was established by integrating univariate Cox regression, LASSO regression, and multivariate Cox regression. Its predictive value was validated using Kaplan-Meier and ROC curves. Further analysis using GSVA and CIBERSORT examined the relationship between the risk model and the tumor microenvironment immune status, while also evaluating chemotherapy drug sensitivity across different risk groups. Finally, protein-protein interaction (PPI) networks and key gene screening were employed, and the functional validation of the core genes was conducted through in vitro experiments.
Results
We identified 10 key NM-related genes (GLS, ASNS, EXT2, HPRT1, SLC7A5, SMS, B3GNT8, GATM, NAGK, and SULT1B1) to construct a prognostic risk model. The GSVA analysis revealed that the low-risk group was enriched in immune-related pathways, while the high-risk group favored metabolic pathways. Additionally, the low-risk group exhibited higher levels of immune cell infiltration. We discovered that gefitinib, belinostat, erlotinib, and phenformin were more effective against cancer cells with lower risk scores. The PPI network screening identified key hub genes, including LORICRIN. Experimental validation demonstrated that LORICRIN overexpression significantly suppressed migration and invasion in HNSCC cells, suggesting its potential tumor-suppressive role in carcinogenesis and progression.
Discussion
This study emphasizes the links between NM signatures, immune regulation, and signaling pathways, underscoring their potential in the HNSCC mechanism research.
Conclusion
Our study established a NM-related gene signature closely linked to immune microenvironment and drug sensitivity, highlighting potential biomarkers and therapeutic targets for prognosis and personalized therapy in HNSCC.
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