Zheng Dan, Pu Xuan, Deng XuHui, Liu Cui, Li SiJun
Department of Otolaryngology, Head and Neck Surgery, Affiliated Hospital of North Sichuan Medical College, Nanchong, China.
Department of Clinical Medicine, North Sichuan Medical College, Nanchong, China.
Front Immunol. 2025 May 22;16:1512502. doi: 10.3389/fimmu.2025.1512502. eCollection 2025.
Laryngeal cancer (LCA) is the second most common type of head and neck malignancy, characterized by high recurrence rates and poor overall survival (OS). However, progress in curing LCA through molecular-targeted diagnostics and therapies is slow and limited. The occurrence and progression of cancer are closely associated with metabolic reprogramming. Therefore, this study aimed to identify metabolism-related LCA subtypes through a comprehensive analysis of transcriptomic, mutational, methylation, and single-cell RNA sequencing, in hopes of finding factors which influences the prognosis of LCA.
First, to identify metabolism-related LCA subtypes, data from 114 patients with LCA from The Cancer Genome Atlas (TCGA) dataset were collected for an unsupervised clustering analysis, which focused on the expression characteristics of survival-related metabolic genes. Subsequently, prognostic and diagnostic models have been developed using machine learning techniques. Specifically, the prognostic model utilized the least absolute shrinkage and selection operator (LASSO) Cox regression, whereas the diagnostic model was built using the Random Forest (RF) algorithm. Furthermore, to ensure the reproducibility, the results of the subtypes and models were validated using three independent bulk RNA datasets and a scRNA-seq dataset.
Two robust subtypes were identified and independently validated. Each subtype has a distinct prognostic outcomes and molecular features. Specifically, the subtype exhibited better prognosis, enriched metabolic pathways, and higher mutation frequencies. Notably, significant damaging mutations in the methyltransferases were observed in this subtype. In contrast, the subtype was associated with poorer prognosis, higher immune infiltration, and elevated methylation levels. Moreover, in tumors, higher levels of T cell/APC co-inhibition and inhibitory checkpoints were observed. In addition, the diagnostic model demonstrated strong performance, achieving an area under the curve (AUC) values of 1.000 in the training group and 0.947 in the validation group. The prognostic model effectively predicted patient outcomes, with the RiskScore emerging as an independent prognostic factor.
This study offers new perspectives for patient stratification and presents opportunities for therapeutic development in LCA. Furthermore, we explored the potentials of several key tumor markers for both diagnosis and prognosis prediction.
喉癌（LCA）是头颈部第二常见的恶性肿瘤，其特点是复发率高和总生存期（OS）较差。然而，通过分子靶向诊断和治疗治愈LCA的进展缓慢且有限。癌症的发生和进展与代谢重编程密切相关。因此，本研究旨在通过对转录组、突变、甲基化和单细胞RNA测序进行综合分析，确定与代谢相关的LCA亚型，以期找到影响LCA预后的因素。
首先，为了确定与代谢相关的LCA亚型，收集了来自癌症基因组图谱（TCGA）数据集的114例LCA患者的数据，进行无监督聚类分析，重点关注生存相关代谢基因的表达特征。随后，使用机器学习技术开发了预后和诊断模型。具体而言，预后模型采用最小绝对收缩和选择算子（LASSO）Cox回归，而诊断模型则使用随机森林（RF）算法构建。此外，为确保结果的可重复性，使用三个独立的批量RNA数据集和一个scRNA-seq数据集对亚型和模型的结果进行了验证。
确定了两种稳健的亚型并进行了独立验证。每个亚型都有不同的预后结果和分子特征。具体而言，该亚型表现出更好的预后、丰富的代谢途径和更高的突变频率。值得注意的是，在该亚型中观察到甲基转移酶存在显著的有害突变。相比之下，该亚型与较差的预后、更高的免疫浸润和更高的甲基化水平相关。此外，在肿瘤中，观察到更高水平的T细胞/抗原呈递细胞（APC）共抑制和抑制性检查点。此外，诊断模型表现出强大的性能，在训练组中的曲线下面积（AUC）值为1.000，在验证组中为0.947。预后模型有效地预测了患者的预后，风险评分成为一个独立的预后因素。
本研究为患者分层提供了新的视角，并为LCA的治疗发展提供了机会。此外，我们探索了几种关键肿瘤标志物在诊断和预后预测方面的潜力。
