Li Chen, Lu Jiajing, Zhu Jialin, Zhou Tao, Shen Qijie, Huang Bikun, Li Qingsong
Department of Stomatology, Taizhou People's Hospital Affiliated to Nanjing Medical University, Taizhou, 225300, Jiangsu, China.
Medical School, Taizhou Polytechnic College, Taizhou, 225300, Jiangsu, China.
Discov Oncol. 2025 Apr 10;16(1):511. doi: 10.1007/s12672-025-02310-y.
Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous and aggressive cancer, posing challenges for prognosis and treatment. The role of PANoptosis in HNSCC remains unclear, despite its potential impact on the tumor immune microenvironment.
We obtained the single-cell RNA sequencing dataset GSE181919 from the GEO database and used the AUCell R package to analyze PANoptosis enrichment and heterogeneity, identifying key PANoptosis-related genes at the single-cell level. A prognostic model was constructed using 101 machine learning algorithms and their combinations, with TCGA-HNSCC as the training set and GSE41613 and GSE65858 as validation sets. Model performance was assessed using Kaplan-Meier survival analysis, ROC curves, and PCA. Glutathione S-transferase omega 1 (GSTO1) was identified as a key model gene, and its expression was validated using PCR in clinical samples.
We integrated single-cell sequencing, bulk transcriptomic sequencing, and machine learning algorithms to construct a StepCox[backward] + RSF prognostic model for HNSCC. This model stratified HNSCC patients into high-risk and low-risk groups, with the high-risk group exhibiting worse prognosis. ROC and PCA analyses confirmed the model's robustness. Additionally, the key gene GSTO1 was identified and further validated to be upregulated in HNSCC and associated with poor prognosis.
The PANoptosis-based prognostic model offers strong predictive value for HNSCC and has potential applications in personalized treatment approaches. GSTO1 stands out as a promising biomarker and therapeutic target. Future research should focus on experimental validation and the development of therapeutic strategies that modulate PANoptosis to improve outcomes for HNSCC patients.
头颈部鳞状细胞癌（HNSCC）是一种高度异质性和侵袭性的癌症，对预后和治疗构成挑战。尽管PAN凋亡对肿瘤免疫微环境有潜在影响，但其在HNSCC中的作用仍不清楚。
我们从GEO数据库中获取了单细胞RNA测序数据集GSE181919，并使用AUCell R包分析PAN凋亡的富集和异质性，在单细胞水平上鉴定关键的PAN凋亡相关基因。以TCGA-HNSCC为训练集，GSE41613和GSE65858为验证集，使用101种机器学习算法及其组合构建预后模型。使用Kaplan-Meier生存分析、ROC曲线和PCA评估模型性能。谷胱甘肽S-转移酶ω1（GSTO1）被鉴定为关键模型基因，并在临床样本中通过PCR验证其表达。
我们整合单细胞测序、批量转录组测序和机器学习算法，构建了HNSCC的StepCox[向后] + RSF预后模型。该模型将HNSCC患者分为高风险和低风险组，高风险组预后较差。ROC和PCA分析证实了模型的稳健性。此外，关键基因GSTO1被鉴定并进一步验证在HNSCC中上调且与预后不良相关。
基于PAN凋亡的预后模型对HNSCC具有强大的预测价值，在个性化治疗方法中具有潜在应用。GSTO1是一个有前景的生物标志物和治疗靶点。未来的研究应集中在实验验证和开发调节PAN凋亡以改善HNSCC患者预后的治疗策略上。
