Liu Ke, Yu Jiannan, Huang Xuanxi, Gao Hongyan, Wang Jing
Department of Stomatology, Jinling Clinical Medical College, Nanjing Medical University, Nanjing, China.
Department of Pediatric Dental Prevention, The Afiliated Stomatological Hospial of Nanjing Medical University, Nanjing, China.
Discov Oncol. 2025 Mar 24;16(1):379. doi: 10.1007/s12672-025-02151-9.
Oral squamous cell carcinoma (OSCC) is the predominant type of oral cancer, with over 370,000 new cases and approximately 170,000 deaths annually worldwide. Despite therapeutic advancements, OSCC mortality rates have been increasing, underscoring the need for improved prognostic models and therapeutic targets.
We integrated transcriptomic and clinical survival data from the TCGA-OSCC dataset to identify ferroptosis-related prognostic genes. Using weighted gene co-expression network analysis (WGCNA), we selected genes associated with OSCC prognosis and applied Lasso regression analysis to pinpoint key genes. A prognostic model was constructed and validated through survival analysis and receiver operating characteristic (ROC) curve analysis.
WGCNA identified modules significantly correlated with ferroptosis, yielding 321 genes associated with OSCC prognosis. Univariate Cox analysis identified 13 genes affecting OSCC prognosis. Lasso regression and multivariate Cox regression narrowed down the gene set to a final set of 7 genes, which were used to construct the risk model. The model stratified patients into high- and low-risk groups with significant survival differences (P < 0.001). The model's predictive accuracy was validated, with AUC values ranging from 0.565 to 0.733 for 1-, 3-, and 5-year survival predictions. Immune-related analysis revealed that low-risk patients exhibited higher immune cell infiltration and were more likely to benefit from immunotherapy.
Our study presents a novel prognostic model for OSCC patients based on ferroptosis-related genes, which not only predicts survival but also identifies potential therapeutic targets. The model's predictive accuracy and clinical relevance were validated, offering a new strategy for OSCC treatment.
口腔鳞状细胞癌（OSCC）是口腔癌的主要类型，全球每年有超过37万新发病例和约17万例死亡。尽管治疗取得了进展，但OSCC的死亡率一直在上升，这突出表明需要改进预后模型和治疗靶点。
我们整合了来自TCGA-OSCC数据集的转录组和临床生存数据，以识别铁死亡相关的预后基因。使用加权基因共表达网络分析（WGCNA），我们选择了与OSCC预后相关的基因，并应用Lasso回归分析来确定关键基因。通过生存分析和受试者工作特征（ROC）曲线分析构建并验证了一个预后模型。
WGCNA识别出与铁死亡显著相关的模块，产生了321个与OSCC预后相关的基因。单变量Cox分析确定了13个影响OSCC预后的基因。Lasso回归和多变量Cox回归将基因集缩小到最终的7个基因集，用于构建风险模型。该模型将患者分为高风险和低风险组，生存差异显著（P < 0.001）。该模型的预测准确性得到验证，1年、3年和5年生存预测的AUC值范围为0.565至0.733。免疫相关分析表明，低风险患者表现出更高的免疫细胞浸润，更有可能从免疫治疗中获益。
我们的研究提出了一种基于铁死亡相关基因的OSCC患者新预后模型，该模型不仅可以预测生存，还可以识别潜在的治疗靶点。该模型的预测准确性和临床相关性得到验证，为OSCC治疗提供了一种新策略。
