Li Sha-Zhou, Sun Hai-Ying, Tian Yuan, Zhou Liu-Qing, Zhou Tao
Department of Otorhinolaryngology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Department of Geriatrics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
Front Immunol. 2024 Dec 19;15:1469895. doi: 10.3389/fimmu.2024.1469895. eCollection 2024.
Head and neck squamous cell carcinoma (HNSCC), a highly heterogeneous malignancy is often associated with unfavorable prognosis. Due to its unique anatomical position and the absence of effective early inspection methods, surgical intervention alone is frequently inadequate for achieving complete remission. Therefore, the identification of reliable biomarker is crucial to enhance the accuracy of screening and treatment strategies for HNSCC.
To develop and identify a machine learning-derived prognostic model (MLDPM) for HNSCC, ten machine learning algorithms, namely CoxBoost, elastic network (Enet), generalized boosted regression modeling (GBM), Lasso, Ridge, partial least squares regression for Cox (plsRcox), random survival forest (RSF), stepwise Cox, supervised principal components (SuperPC), and survival support vector machine (survival-SVM), along with 81 algorithm combinations were utilized. Time-dependent receiver operating characteristics (ROC) curves and Kaplan-Meier analysis can effectively assess the model's predictive performance. Validation was performed through a nomogram, calibration curves, univariate and multivariate Cox analysis. Further analyses included immunological profiling and gene set enrichment analyses (GSEA). Additionally, the prediction of 50% inhibitory concentration (IC50) of potential drugs between groups was determined.
From analyses in the HNSCC tissues and normal tissues, we found 536 differentially expressed genes (DEGs). Subsequent univariate-cox regression analysis narrowed this list to 18 genes. A robust risk model, outperforming other clinical signatures, was then constructed using machine learning techniques. The MLDPM indicated that high-risk scores showed a greater propensity for immune escape and reduced survival rates. Dasatinib and 7 medicine showed the superior sensitivity to the high-risk NHSCC, which had potential to the clinical.
The construction of MLDPM effectively eliminated artificial bias by utilizing 101 algorithm combinations. This model demonstrated high accuracy in predicting HNSCC outcomes and has the potential to identify novel therapeutic targets for HNSCC patients, thus offering significant advancements in personalized treatment strategies.
头颈部鳞状细胞癌（HNSCC）是一种高度异质性的恶性肿瘤，通常预后不良。由于其独特的解剖位置以及缺乏有效的早期检测方法，仅手术干预往往不足以实现完全缓解。因此，识别可靠的生物标志物对于提高HNSCC筛查和治疗策略的准确性至关重要。
为了开发和识别一种用于HNSCC的机器学习衍生预后模型（MLDPM），使用了十种机器学习算法，即CoxBoost、弹性网络（Enet）、广义增强回归建模（GBM）、套索回归（Lasso）、岭回归（Ridge）、Cox偏最小二乘回归（plsRcox）、随机生存森林（RSF）、逐步Cox回归、监督主成分分析（SuperPC）和生存支持向量机（survival-SVM），以及81种算法组合。时间依赖受试者工作特征（ROC）曲线和Kaplan-Meier分析可以有效评估模型的预测性能。通过列线图、校准曲线、单变量和多变量Cox分析进行验证。进一步的分析包括免疫图谱分析和基因集富集分析（GSEA）。此外，还确定了组间潜在药物50%抑制浓度（IC50）的预测。
通过对HNSCC组织和正常组织的分析，我们发现了536个差异表达基因（DEG）。随后的单变量Cox回归分析将这个列表缩小到18个基因。然后使用机器学习技术构建了一个强大的风险模型，其性能优于其他临床特征。MLDPM表明，高风险评分显示出更大的免疫逃逸倾向和更低的生存率。达沙替尼和7种药物对高风险HNSCC表现出更高的敏感性，具有临床应用潜力。
MLDPM的构建通过利用101种算法组合有效地消除了人为偏差。该模型在预测HNSCC预后方面表现出高准确性，并且有潜力为HNSCC患者识别新的治疗靶点，从而在个性化治疗策略方面取得重大进展。
