Liu Shixian, Liu Weiwei, Ding Zhao, Yang Xue, Jiang Yuan, Wu Yu, Liu Yehai, Wu Jing
Department of Otolaryngology-Head & Neck Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, China.
Anhui Medical University, Hefei, China.
Front Mol Biosci. 2022 Oct 20;9:912620. doi: 10.3389/fmolb.2022.912620. eCollection 2022.
Head and neck squamous cell carcinoma (HNSCC) is a common heterogeneous cancer with complex carcinogenic factors. However, the current TNM staging criteria to judge its severity to formulate treatment plans and evaluate the prognosis are particularly weak. Therefore, a robust diagnostic model capable of accurately diagnosing and predicting HNSCC should be established. Gene expression and clinical data were retrieved from The Cancer Genome Atlas and Gene Expression Omnibus databases. Key prognostic genes associated with HNSCC were screened with the weighted gene co-expression network analysis and least absolute shrinkage and selection operator (LASSO) Cox regression model analysis. We used the timeROC and survival R packages to conduct time-dependent receiver operating characteristic curve analyses and calculated the area under the curve at different time points of model prediction. Patients in the training and validation groups were divided into high- and low-risk subgroups, and Kaplan-Meier (K-M) survival curves were plotted for all subgroups. Subsequently, LASSO and support vector machine algorithms were used to screen genes to construct diagnostic model. Furthermore, we used the Wilcoxon signed-rank test to compare the half-maximal inhibitory concentrations of common chemotherapy drugs among patients in different risk groups. Finally, the expression levels of eight genes were measured using quantitative real-time polymerase chain reaction and immunohistochemistry. Ten genes (, , , , , , , , , and ) with prognostic potential were identified, and a risk score was derived accordingly. Patients were divided into high- and low-risk groups based on the median risk score. The K-M survival curves confirmed that patients with high scores had significantly worse overall survival. Receiver operating characteristic curves proved that the prognostic signature had good sensitivity and specificity for predicting the prognosis of patients with HNSCC. Univariate and multivariate Cox regression analyses confirmed that the gene signature was an independent prognostic risk factor for HNSCC. Diagnostic model was built by identifying eight genes (, , , , , , , and ). The high-risk group showed higher sensitivity to various common chemotherapeutic drugs. expression was higher in normal tissues than in HNSCC tissues. Our study identified the important role of tumor-driver genes in HNSCC and their potential clinical diagnostic and prognostic values to facilitate individualized management of patients with HNSCC.
头颈部鳞状细胞癌（HNSCC）是一种常见的异质性癌症，致癌因素复杂。然而，目前用于判断其严重程度以制定治疗方案和评估预后的TNM分期标准存在明显不足。因此，应建立一种能够准确诊断和预测HNSCC的强大诊断模型。从癌症基因组图谱（The Cancer Genome Atlas）和基因表达综合数据库（Gene Expression Omnibus）中检索基因表达和临床数据。通过加权基因共表达网络分析和最小绝对收缩和选择算子（LASSO）Cox回归模型分析筛选与HNSCC相关的关键预后基因。我们使用timeROC和生存R包进行时间依赖性受试者工作特征曲线分析，并计算模型预测不同时间点的曲线下面积。将训练组和验证组的患者分为高风险和低风险亚组，并为所有亚组绘制Kaplan-Meier（K-M）生存曲线。随后，使用LASSO和支持向量机算法筛选基因以构建诊断模型。此外，我们使用Wilcoxon符号秩检验比较不同风险组患者中常用化疗药物的半数最大抑制浓度。最后，使用定量实时聚合酶链反应和免疫组织化学测量八个基因的表达水平。鉴定出十个具有预后潜力的基因（、、、、、、、、和），并据此得出风险评分。根据中位风险评分将患者分为高风险和低风险组。K-M生存曲线证实，高分患者的总生存期明显更差。受试者工作特征曲线证明，该预后特征对预测HNSCC患者的预后具有良好的敏感性和特异性。单因素和多因素Cox回归分析证实，基因特征是HNSCC的独立预后危险因素。通过鉴定八个基因（、、、、、、、和）建立诊断模型。高风险组对各种常用化疗药物表现出更高的敏感性。在正常组织中的表达高于HNSCC组织。我们的研究确定了肿瘤驱动基因在HNSCC中的重要作用及其潜在的临床诊断和预后价值，以促进HNSCC患者的个体化管理。
