Zhang Xijun, Zheng Yongjun, Li Gaoshan, Yu Changying, Ji Ting, Miao Shenghu
Department of Laboratory of Jiayuguan City First People's Hospital, Jiayuguan, China.
The 984th Hospital of the People's Liberation Army, Shangzhuang Township, Beijing, China.
Transl Cancer Res. 2020 Nov;9(11):7299-7309. doi: 10.21037/tcr-20-3204.
Osteosarcoma (OS) is a common malignant bone tumor in children and adolescents. DNA methylation plays a crucial role in the prognosis prediction of cancer. Identification of novel DNA methylation sites biomarkers could be beneficial for the prognosis of OS patients. In this study, we aim to find an efficient methylated site model for predicting survival in OS.
DNA methylation data were downloaded from the Cancer Genome Atlas database (TCGA) and the GEO database. Cox proportional hazard regression and random survival forest algorithm (RSFVH) were applied to identify DNA methylated site signature in the samples randomly assigned to the training subset and the other samples as the test subset. By randomizing 71 clinical samples into two individual groups and a series of statistical analyses between the two groups, a DNA methylation signature is verified.
This signature comprises four methylation sites (cg04533248, cg12401425, cg13997435, and cg15075357) associated with the patient training group from the univariate Cox proportional hazards regression analysis, RSFVH, and multivariate Cox regression analysis. Kaplan-Meier survival curves showed the OS patients in the high-risk group have a poor 5-year overall survival compared with the low-risk group, and this finding was identified in the test data set. A ROC analysis was performed in the current research. The results revealed that this signature was an independent predictor of patient survival by investigating the AUC of the four methylation sites signature in the training data set (AUC =0.861) and test data set, respectively (AUC =0.920). The nomogram described in the current study placed a great guiding value for predicting 1-, 2-, 3-year survival of the OS by combining age, gender, grade, and TNM stage as covariates with the RS of patients' methylation related signatures.
Our study proved that this signature might be a powerful prognostic tool for survival rate evaluation and guide tailored therapy for OS patients.
骨肉瘤（OS）是儿童和青少年常见的恶性骨肿瘤。DNA甲基化在癌症预后预测中起着关键作用。鉴定新的DNA甲基化位点生物标志物可能有助于骨肉瘤患者的预后。在本研究中，我们旨在寻找一种有效的甲基化位点模型来预测骨肉瘤患者的生存情况。
从癌症基因组图谱数据库（TCGA）和基因表达综合数据库（GEO）下载DNA甲基化数据。应用Cox比例风险回归和随机生存森林算法（RSFVH），在随机分配到训练子集的样本中识别DNA甲基化位点特征，并将其他样本作为测试子集。通过将71个临床样本随机分为两组，并对两组进行一系列统计分析，验证了DNA甲基化特征。
该特征包含四个甲基化位点（cg04533248、cg12401425、cg13997435和cg15075357），这些位点通过单变量Cox比例风险回归分析、RSFVH和多变量Cox回归分析与患者训练组相关。Kaplan-Meier生存曲线显示，与低风险组相比，高风险组的骨肉瘤患者5年总生存率较差，这一发现也在测试数据集中得到证实。本研究进行了ROC分析。结果显示，通过分别研究训练数据集（AUC = 0.861）和测试数据集（AUC = 0.920）中四个甲基化位点特征的AUC，该特征是患者生存的独立预测因子。本研究中描述的列线图通过将年龄、性别、分级和TNM分期作为协变量与患者甲基化相关特征的风险评分相结合，对预测骨肉瘤患者1年、2年、3年生存率具有重要指导价值。
我们的研究证明，该特征可能是评估生存率的有力预后工具，并可为骨肉瘤患者指导个体化治疗。
