He Yaodong, Li Yun, Tang Jiaqi, Wang Yan, Zhao Zhenyan, Liu Rong, Yang Zihui, Li Huan, Wei Jianhua
State Key Laboratory of Oral and Maxillofacial Reconstruction and Regeneration, National Clinical Research Center for Oral Diseases, Shaanxi Clinical Research Center for Oral Diseases, Department of Oral and Maxillofacial Surgery, School of Stomatology, The Fourth Military Medical University, Xi'an, China.
Front Immunol. 2025 May 22;16:1590331. doi: 10.3389/fimmu.2025.1590331. eCollection 2025.
Head and neck squamous cell carcinoma (HNSCC) is a common cancer associated with elevated mortality rates. Exosomes, diminutive extracellular vesicles, significantly contribute to tumour development, immunological evasion, and treatment resistance. Identifying exosome-associated biomarkers in HNSCC may improve early diagnosis, treatment targeting, and patient classification.
We acquired four publically accessible HNSCC gene expression datasets from the Gene Expression Omnibus (GEO) database and mitigated batch effects utilising the ComBat technique. Differential expression analysis and exosome-related gene screening found a collection of markedly exosome-associated differentially expressed genes (ERDEGs). Subsequently, 10 key exosome-related genes were further screened by combining three machine learning methods, LASSO regression, SVM-RFE and RF, and a clinical prediction model was constructed. Furthermore, we thoroughly investigated the biological roles of these genes in HNSCC and their prospective treatment implications via functional enrichment analysis, immune microenvironment assessment, and molecular docking confirmation.
The study indicated that 10 pivotal exosome-related genes identified by the machine learning method had considerable differential expression in HNSCC. Clinical prediction models developed from these genes have shown high accuracy in prognostic evaluations of HNSCC patients. Analysis of the immunological microenvironment indicated varying immune cell infiltration in HNSCC, and the association with ERDEGs proposed a potential mechanism for immune evasion. Molecular docking validation indicated novel small molecule medicines targeting these genes, establishing a theoretical foundation for pharmacological therapy in HNSCC.
This research identifies new exosome-related indicators for HNSCC through machine learning methodologies. The suggested biomarkers, particularly ANGPTL1, exhibit significant promise for diagnostic and prognostic uses. The investigation of the immunological microenvironment yields insights into immune modulation in HNSCC, presenting novel avenues for therapeutic targeting.
头颈部鳞状细胞癌（HNSCC）是一种常见癌症，死亡率较高。外泌体是微小的细胞外囊泡，对肿瘤发展、免疫逃逸和治疗抗性有显著贡献。鉴定HNSCC中与外泌体相关的生物标志物可能会改善早期诊断、靶向治疗和患者分类。
我们从基因表达综合数据库（GEO）中获取了四个可公开访问的HNSCC基因表达数据集，并使用ComBat技术减轻批次效应。差异表达分析和外泌体相关基因筛选发现了一组明显与外泌体相关的差异表达基因（ERDEGs）。随后，通过结合三种机器学习方法（LASSO回归、支持向量机递归特征消除法和随机森林法）进一步筛选出10个关键的外泌体相关基因，并构建了临床预测模型。此外，我们通过功能富集分析、免疫微环境评估和分子对接确认，深入研究了这些基因在HNSCC中的生物学作用及其潜在的治疗意义。
研究表明，通过机器学习方法鉴定出的10个关键外泌体相关基因在HNSCC中存在显著差异表达。由这些基因开发的临床预测模型在HNSCC患者的预后评估中显示出较高的准确性。免疫微环境分析表明HNSCC中免疫细胞浸润情况各异，且与ERDEGs的关联提出了一种免疫逃逸的潜在机制。分子对接验证表明了针对这些基因的新型小分子药物，为HNSCC的药物治疗奠定了理论基础。
本研究通过机器学习方法为HNSCC确定了新的外泌体相关指标。所建议的生物标志物，特别是血管生成素样蛋白1（ANGPTL1），在诊断和预后应用方面具有显著前景。对免疫微环境的研究深入了解了HNSCC中的免疫调节，为治疗靶向提供了新途径。
