Prognostic prediction of laryngeal cancer patients based on nitrogen metabolism-related genes

Background: Nitrogen metabolism plays a crucial role in cancer progression. This study aimed to construct a prognostic model using nitrogen metabolism-related genes (NMRGs) for laryngeal cancer (LC). Methods: NMRGs for LC were identified from public databases and literature. A prognostic model was constructed through regression analysis, and differential and enrichment analyses were performed to explore differentially expressed genes (DEGs) and their functional implications. Immune cell differences between risk groups were assessed, and gene mutations were analyzed using TCGA data. Drug sensitivity predictions for different risk groups were also conducted. Results: A total of 203 NMRGs were identified, leading to eight genes used in a risk-scoring model. Enrichment analysis showed that DEGs in the high-risk group (993 genes) were linked to processes like neuroactive ligand-receptor interaction and calcium signaling. Immune analysis revealed high infiltration of activated NK cells and CD4+ T cells in the low-risk group, while CD8+ T cells and macrophages were prominent in the high-risk group. Drug sensitivity analysis identified KIN001-135, Phenformin, and Gemcitabine as potential treatments. Conclusion: Nitrogen metabolism is closely related to LC prognosis, and the NMRG-based model effectively distinguishes risk groups with distinct immune landscapes and drug sensitivities.

背景：氮代谢在癌症进展中发挥关键作用。本研究旨在利用氮代谢相关基因（Nitrogen Metabolism-Related Genes, NMRGs）构建喉癌（Laryngeal Cancer, LC）的预后模型。方法：从公共数据库及文献中筛选喉癌相关的氮代谢相关基因。通过回归分析构建预后模型，并开展差异表达分析与富集分析，以探究差异表达基因（Differentially Expressed Genes, DEGs）及其功能意义。评估不同风险组间的免疫细胞浸润差异，并利用癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据分析基因突变情况。此外还针对不同风险组开展了药物敏感性预测。结果：本研究共筛选得到203个氮代谢相关基因，最终选取其中8个基因构建风险评分模型。富集分析结果显示，高风险组的993个差异表达基因主要富集于神经活性配体-受体相互作用、钙信号通路等生物学过程。免疫浸润分析显示，低风险组中活化NK细胞与CD4+ T细胞浸润程度更高，而高风险组则以CD8+ T细胞与巨噬细胞浸润为主。药物敏感性分析筛选出KIN001-135、苯乙双胍（Phenformin）与吉西他滨（Gemcitabine）作为潜在治疗药物。结论：氮代谢与喉癌预后密切相关，基于氮代谢相关基因构建的预后模型可有效区分具有不同免疫特征与药物敏感性的风险亚组。