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。本研究旨在利用喉癌（laryngeal cancer, LC）的氮代谢相关基因（nitrogen metabolism-related genes, NMRGs）构建预后模型。

方法：从公共数据库和文献中筛选出喉癌（LC）的氮代谢相关基因（NMRGs）。通过回归分析构建预后模型，并进行差异分析和富集分析以探究差异表达基因（differentially expressed genes, DEGs）及其功能意义。评估风险组间免疫细胞的差异，并利用TCGA数据分析基因突变情况。此外，还对不同风险组进行了药物敏感性预测。

结果：共鉴定出203个氮代谢相关基因（NMRGs），从中筛选出8个基因用于构建风险评分模型。富集分析显示，高风险组的差异表达基因（DEGs，共993个）与神经活性配体-受体相互作用、钙信号通路等过程相关。免疫分析表明，低风险组中活化自然杀伤细胞（activated NK cells）和CD4+ T细胞浸润水平较高，而高风险组中CD8+ T细胞和巨噬细胞更为显著。药物敏感性分析发现KIN001-135、苯乙双胍（Phenformin）和吉西他滨（Gemcitabine）是潜在治疗药物。

结论：氮代谢与喉癌（LC）预后密切相关，基于NMRGs的模型可有效区分具有不同免疫景观和药物敏感性的风险组。