头颈鳞状细胞癌（HNSCC）是一种普遍存在的癌症，由于头部解剖学的限制和缺乏可靠的生物标志物，其存活率很低。 Cuprotosis 代表了一种新型的细胞调节死亡途径，N6-甲基腺苷 (m6A) 是 mRNA 中最常见的内部 RNA 修饰。它们与肿瘤的形成、进展和预后密切相关。本研究旨在使用一组与 m6A 调节因子和铜凋亡基因 (mcrmRNA) 相关的 mRNA 构建 HNSCC 风险模型。对来自癌症基因组图谱 (TCGA) 数据库的 HNSCC 患者的 RNA-seq 和临床数据进行分析，以制定风险模型通过最小绝对收缩和选择算子（LASSO）分析模型。对高风险组和低风险组进行生存分析和受试者工作特征（ROC）分析。此外，该模型还使用基因表达综合 (GEO) 数据库中的 GSE41613 数据集进行了验证。应用GSEA和CIBERSORT研究HNSCC的免疫微环境。利用LASSO分析建立了由32个mcrmRNA组成的风险模型。多变量Cox分析证实患者的风险评分是独立的预后指标。高风险组表现出更高的肿瘤突变负担。此外，CIBERSORT 分析表明两组之间的免疫细胞浸润水平不同。还观察到对常见药物的药物敏感性存在显着差异。富集分析进一步揭示了两组之间代谢途径和 RNA 加工的显着差异。我们的风险模型可以预测 HNSCC 患者的结果，并为个性化治疗方法提供有价值的见解。© 2024。作者。

Head and neck squamous cell carcinoma (HNSCC) is a prevalent cancer with a poor survival rate due to anatomical limitations of the head and a lack of reliable biomarkers. Cuproptosis represents a novel cellular regulated death pathway, and N6-methyladenosine (m6A) is the most common internal RNA modification in mRNA. They are intricately connected to tumor formation, progression, and prognosis. This study aimed to construct a risk model for HNSCC using a set of mRNAs associated with m6A regulators and cuproptosis genes (mcrmRNA).RNA-seq and clinical data of HNSCC patients from The Cancer Genome Atlas (TCGA) database were analyzed to develop a risk model through the least absolute shrinkage and selection operator (LASSO) analysis. Survival analysis and receiver operating characteristic (ROC) analysis were performed for the high- and low-risk groups. Additionally, the model was validated using the GSE41613 dataset from the Gene Expression Omnibus (GEO) database. GSEA and CIBERSORT were applied to investigate the immune microenvironment of HNSCC.A risk model consisting of 32 mcrmRNA was developed using the LASSO analysis. The risk score of patients was confirmed to be an independent prognostic indicator by multivariate Cox analysis. The high-risk group exhibited a higher tumor mutation burden. Additionally, CIBERSORT analysis indicated varying levels of immune cell infiltration between the two groups. Significant disparities in drug sensitivity to common medications were also observed. Enrichment analysis further unveiled significant differences in metabolic pathways and RNA processing between the two groups.Our risk model can predict outcomes for HNSCC patients and offers valuable insights for personalized therapeutic approaches.© 2024. The Author(s).