Medical Laboratory, Shenzhen Luohu People's Hospital, Shenzhen, China.
School of Medicine, Anhui University of Science and Technology, Huainan, China.
Cancer Med. 2021 Sep;10(17):5936-5947. doi: 10.1002/cam4.4142. Epub 2021 Jul 27.
Head and neck squamous cell carcinoma (HNSCC) is a common malignancy worldwide with a poor prognosis. DNA methylation is an epigenetic modification that plays a critical role in the etiology and pathogenesis of HNSCC. The current study aimed to develop a predictive methylation signature based on bioinformatics analysis to improve the prognosis and optimize therapeutic outcome in HNSCC. Clinical information and methylation sequencing data of patients with HNSCC were downloaded from The Cancer Genome Atlas database. The R package was used to identify differentially methylated genes (DMGs) between HNSCC and adjacent normal tissues. We identified 22 DMGs associated with 246 differentially methylated sites. Patients with HNSCC were classified into training and test groups. Cox regression analysis was used to build a risk score formula based on the five methylation sites (cg26428455, cg13754259, cg17421709, cg19229344, and cg11668749) in the training group. The Kaplan-Meier survival curves showed that the overall survival (OS) rates were significantly different between the high- and low-risk groups sorted by the signature in the training group (median: 1.38 vs. 1.57 years, log-rank test, p < 0.001). The predictive power was then validated in the test group (median: 1.34 vs. 1.75 years, log-rank test, p < 0.001). The area under the receiver operating characteristic curve (area under the curve) based on the signature for predicting the 5-year survival rates, was 0.7 in the training and 0.73 in test groups, respectively. The results of multivariate Cox regression analysis showed that the riskscore (RS) signature based on the five methylation sites was an independent prognostic tool for OS prediction in patients. In addition, a predictive nomogram model that incorporated the RS signature and patient clinical information was developed. The innovative methylation signature-based model developed in our study represents a robust prognostic tool for guiding clinical therapy and predicting the OS in patients with HNSCC.
头颈部鳞状细胞癌（HNSCC）是一种常见的恶性肿瘤，全球范围内预后较差。DNA 甲基化是一种表观遗传修饰，在 HNSCC 的病因和发病机制中起着关键作用。本研究旨在通过生物信息学分析开发预测性甲基化特征，以改善 HNSCC 的预后并优化治疗效果。从癌症基因组图谱数据库下载 HNSCC 患者的临床信息和甲基化测序数据。使用 R 包识别 HNSCC 与相邻正常组织之间的差异甲基化基因（DMGs）。我们确定了 22 个与 246 个差异甲基化位点相关的 DMG。将 HNSCC 患者分为训练组和测试组。Cox 回归分析用于基于训练组中五个甲基化位点（cg26428455、cg13754259、cg17421709、cg19229344 和 cg11668749）构建风险评分公式。Kaplan-Meier 生存曲线显示，根据签名对训练组中进行排序的高风险和低风险组的总体生存率（OS）有显著差异（中位数：1.38 与 1.57 年，对数秩检验，p<0.001）。然后在测试组中验证了预测能力（中位数：1.34 与 1.75 年，对数秩检验，p<0.001）。基于签名预测 5 年生存率的受试者工作特征曲线下面积（曲线下面积）在训练组中为 0.7，在测试组中为 0.73。多变量 Cox 回归分析结果表明，基于五个甲基化位点的风险评分（RS）特征是预测 OS 的独立预后工具。此外，还开发了一个包含 RS 特征和患者临床信息的预测列线图模型。本研究中开发的基于创新甲基化特征的模型是指导临床治疗和预测 HNSCC 患者 OS 的强大预后工具。
