Wei Zhengyu, Wang Guoli, Hu Yanghao, Zhou Chongchang, Zhang Yuna, Shen Yi, Wang Yaowen
Department of Otorhinolaryngology Head and Neck Surgery, The First Affiliated Hospital of Ningbo University, Ningbo, 315000, Zhejiang, China.
Department of Otorhinolaryngology Head and Neck Surgery, The Affiliated Lihuili Hospital, Ningbo University, Ningbo, Zhejiang, China.
Discov Oncol. 2025 Jun 17;16(1):1134. doi: 10.1007/s12672-025-02962-w.
Exosomes are recognized as essential mediators in the intercellular communication between tumor cells, serving a pivotal function in tumor development. Nevertheless, the patterns of expression and medical relevance of exosome-related genes (ERGs) in nasopharyngeal carcinoma (NPC) remain insufficiently characterized.
Datasets retrieved from the Gene Expression Omnibus database were consolidated into a comprehensive gene dataset, which was then employed to ascertain differentially expressed genes (DEGs) by comparing NPC samples with controls. ERGs were intersected with the DEGs, yielding the detection of exosome-related DEGs. These identified genes underwent functional annotation and pathway enrichment evaluation. The least absolute shrinkage and selection operator regression, support vector machine, and random forest approaches were utilized to develop NPC diagnostic model. Key genes were determined through intersection analysis and subsequently confirmed in an independent cohort. Furthermore, drug screening, molecular docking, and molecular dynamics simulation were executed to generate meaningful insights for developing therapeutic compounds.
Through the application of three machine learning algorithms, five key genes (LTF, IDH1, ITGAV, CCL2, and LGALS3BP) were identified for the construction of a diagnostic model. Validation results demonstrated the strong discriminative and calibration abilities of the model. Furthermore, molecular docking analysis revealed that the interaction between IDH1 and nelfinavir exhibited the lowest Vina score, suggesting a stable binding affinity.
This study identifies five exosome-related key genes, utilizing machine learning approaches to develop a diagnostic model and uncover potential drug targets for NPC. These findings offer novel perspectives for both the diagnosis and therapeutic development of NPC.
外泌体被认为是肿瘤细胞间细胞通讯的重要介质，在肿瘤发展中起关键作用。然而，鼻咽癌（NPC）中外泌体相关基因（ERGs）的表达模式和医学相关性仍未得到充分表征。
从基因表达综合数据库检索的数据集被整合为一个综合基因数据集，然后通过将NPC样本与对照进行比较来确定差异表达基因（DEGs）。ERGs与DEGs进行交集分析，从而检测外泌体相关的DEGs。对这些鉴定出的基因进行功能注释和通路富集评估。利用最小绝对收缩和选择算子回归、支持向量机和随机森林方法构建NPC诊断模型。通过交集分析确定关键基因，并随后在一个独立队列中进行验证。此外，还进行了药物筛选、分子对接和分子动力学模拟，以获得开发治疗化合物的有意义见解。
通过应用三种机器学习算法，鉴定出五个关键基因（乳铁传递蛋白、异柠檬酸脱氢酶1、整合素αV、趋化因子配体2和半乳糖凝集素3结合蛋白）用于构建诊断模型。验证结果表明该模型具有很强的判别能力和校准能力。此外，分子对接分析显示异柠檬酸脱氢酶1与奈非那韦之间的相互作用表现出最低的Vina评分，表明具有稳定的结合亲和力。
本研究鉴定出五个外泌体相关关键基因，利用机器学习方法构建诊断模型并揭示NPC的潜在药物靶点。这些发现为NPC的诊断和治疗发展提供了新的视角。
