Wang Xin, Zhang Wen, Liang Kun, Wang Yujuan, Zhang Jin, Wang Jinping, Li An, Yun Yonggang, Liu Hiu, Sun Yanan
Department of Otorhinolaryngology, Head and Neck Surgery, Shaanxi Provincial People's Hospital, The Third Affiliated Hospital of Xi'an Jiaotong University, 256 Youyi Road, Xi'an, 710000, China.
Shaanxi Provincial Key Laboratory of Infection and Immune Diseases, Shaanxi Provincial People's Hospital, The Third Affiliated Hospital of Xi'an Jiaotong University, Xi'an, 710068, China.
BMC Cancer. 2025 Apr 14;25(1):694. doi: 10.1186/s12885-025-14134-8.
Laryngeal cancer (LC) is a malignant tumor that occurs in the larynx. N6-methyladenosine (m6A) RNA methylation, a pivotal and prevalent epigenetic modification in eukaryotic mRNA, intricately intertwines with ferroptosis, and together, they play a crucial role in the development of LC. Accordingly, further research on related molecular mechanisms and pathology of LC is necessary. Weighted gene co-expression network analysis and correlation analysis were used to identify differentially expressed m6A-related ferroptosis genes in LC. The TCGA-HNSC and GSE65858 datasets were obtained from public databases. The TCGA-HNSC dataset consisted of 110 primary tumor oropharynx samples and 12 control oropharynx samples, while the GSE65858 dataset contained forty-eight primary tumor oropharynx samples. Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression were utilized for feature selection and risk model construction in the TCGA-HNSC dataset. The risk model was validated in the GSE65858 dataset. Then, a nomogram was built based on the independent prognostic factor identified using univariate and multivariate Cox regression in the TCGA-HNSC dataset. Mutation analysis, immune-related analysis, and drug sensitivity prediction were applied to analyze the utility of the risk model in the TCGA-HNSC dataset. Additionally, qRT-PCR and western blot were performed to detect the TFRC, RGS4, and FTH1 expression. Three biomarkers were identified to build a risk model using the univariate Cox and LASSO regression algorithms. Receiver operating characteristic (ROC) analysis verified the accuracy of the risk model. Tumor Immune Dysfunction and Exclusion (TIDE) and Estimation of STromal and Immune cells in MAlignant Tumors using the Expression data (ESTIMATE) algorithm showed a positive relationship between risk score and TIDE or ESTIMATE score. Furthermore, drug sensitivity prediction found that 19 chemotherapy drugs were strongly correlated with a risk score. TFRC, RGS4, and FTH1 exhibited high expression levels in 30 laryngeal carcinoma tissues and cell lines. Notably, TFRC and FTH1 expression levels were significantly associated with patient prognosis. In Conclusion, TFRC, RGS4, and FTH1, were identified as m6A-regulated ferroptosis biomarkers in LC, providing insights into LC treatment and prognosis.
喉癌（LC）是一种发生于喉部的恶性肿瘤。N6-甲基腺苷（m6A）RNA甲基化是真核生物mRNA中一种关键且普遍存在的表观遗传修饰，与铁死亡密切相关，二者共同在喉癌的发生发展中发挥关键作用。因此，有必要对喉癌相关分子机制和病理学进行进一步研究。采用加权基因共表达网络分析和相关性分析来鉴定喉癌中差异表达的m6A相关铁死亡基因。从公共数据库中获取TCGA-HNSC和GSE65858数据集。TCGA-HNSC数据集包含110个原发性口咽肿瘤样本和12个对照口咽样本，而GSE65858数据集包含48个原发性口咽肿瘤样本。在TCGA-HNSC数据集中，采用单因素Cox回归和最小绝对收缩和选择算子（LASSO）回归进行特征选择和风险模型构建。该风险模型在GSE65858数据集中进行验证。然后，基于在TCGA-HNSC数据集中通过单因素和多因素Cox回归确定的独立预后因素构建列线图。应用突变分析、免疫相关分析和药物敏感性预测来分析风险模型在TCGA-HNSC数据集中的效用。此外，进行qRT-PCR和蛋白质免疫印迹检测转铁蛋白受体（TFRC）、RGS4和铁蛋白重链1（FTH1）的表达。使用单因素Cox回归和LASSO回归算法鉴定出三个生物标志物以构建风险模型。受试者工作特征（ROC）分析验证了风险模型的准确性。肿瘤免疫功能障碍与排除（TIDE）以及利用表达数据估计恶性肿瘤中的基质和免疫细胞（ESTIMATE）算法显示风险评分与TIDE或ESTIMATE评分呈正相关。此外，药物敏感性预测发现19种化疗药物与风险评分密切相关。TFRC、RGS4和FTH1在30例喉癌组织和细胞系中表达水平较高。值得注意的是，TFRC和FTH1的表达水平与患者预后显著相关。总之，TFRC、RGS4和FTH1被鉴定为喉癌中m6A调节的铁死亡生物标志物，为喉癌的治疗和预后提供了见解。
