Liu Tianyi, Gao Shan, Jiang Jie, Shi Yan
Department of Pathology, The 2nd Affiliated Hospital of Harbin Medical University, Harbin, China.
Transl Cancer Res. 2025 Aug 31;14(8):4691-4702. doi: 10.21037/tcr-2024-2447. Epub 2025 Aug 21.
Laryngeal cancer (LC) is a common respiratory tract malignancy. Although early-stage LC often responds well to treatment, advanced cases typically have poor outcomes and prognosis, resulting in a low overall survival (OS) rate. This study aimed to explore the correlation between co-stimulatory molecules and immune infiltration in LC and to construct a risk score (RS) model for predicting patient prognosis.
The RNA sequencing (RNA-seq) data of LC samples were downloaded from The Cancer Genome Atlas (TCGA) and used as the training dataset. The GSE27020 dataset served as the validation dataset. Univariate Cox regression analysis was performed to identify immune-related co-stimulatory molecules, based on which the samples were classified into three subtypes. Kaplan-Meier (KM) survival analysis was conducted to predict the survival prognosis in different subtypes. A prognostic RS model was constructed using the co-stimulatory molecules, which were obtained from the least absolute shrinkage and selection operator (LASSO) algorithm and validated using the GSE27020 dataset.
Eighteen immune-co-stimulatory molecules were identified, allowing classification of the samples into three subtypes, among which subtype 2 exhibited the most favorable prognosis. Eight immune cell types were found to be associated with the subtypes, and ten immune checkpoint genes showed differential expression across them. Six optimized co-stimulatory molecules were selected to construct the RS model, which was capable of predicting LC prognosis with an area under the curve (AUC) value of 0.870 for 1-year survival in the TCGA dataset. Validation using GSE27020 yielded an AUC of 0.736.
An RS model incorporating six optimized co-stimulatory molecules was constructed and validated, demonstrating strong predictive power for the prognosis of patients with LC.
喉癌（LC）是一种常见的呼吸道恶性肿瘤。尽管早期喉癌通常对治疗反应良好，但晚期病例的预后通常较差，导致总体生存率（OS）较低。本研究旨在探讨共刺激分子与喉癌免疫浸润之间的相关性，并构建一个预测患者预后的风险评分（RS）模型。
从癌症基因组图谱（TCGA）下载喉癌样本的RNA测序（RNA-seq）数据作为训练数据集。GSE27020数据集用作验证数据集。进行单变量Cox回归分析以识别免疫相关的共刺激分子，并据此将样本分为三个亚型。进行Kaplan-Meier（KM）生存分析以预测不同亚型的生存预后。使用从最小绝对收缩和选择算子（LASSO）算法获得的共刺激分子构建预后RS模型，并使用GSE27020数据集进行验证。
鉴定出18种免疫共刺激分子，可将样本分为三个亚型，其中亚型2的预后最有利。发现八种免疫细胞类型与这些亚型相关，并且十个免疫检查点基因在它们之间表现出差异表达。选择六种优化的共刺激分子构建RS模型，该模型能够预测喉癌的预后，在TCGA数据集中1年生存率的曲线下面积（AUC）值为0.870。使用GSE27020进行验证得到的AUC为0.736。
构建并验证了一个包含六种优化共刺激分子的RS模型，该模型对喉癌患者的预后具有很强的预测能力。
