Department of Obstetrics and Gynecology, Changhai Hospital, Second Military Medical University, NO.168, Changhai Road, Shanghai, 200433, People's Republic of China.
Arch Gynecol Obstet. 2020 May;301(5):1275-1287. doi: 10.1007/s00404-020-05524-3. Epub 2020 Apr 9.
Cervical cancer (CC) patients usually have poor prognosis. The present study aims to find a DNA methylation signature for predicting survival of CC patients.
We selected CC patients at pathological stage I-III with corresponding information on radiotherapy and overall survival (OS) from TCGA. Differential expression and methylation analysis was done between patients with and without radiotherapy. We selected feature genes using recursive feature elimination algorithm to build a support vector machine classifier. DNA methylation biomarkers predictive of prognosis were identified using a LASSO Cox-Proportional Hazards model to construct a prognostic scoring model. The classifier and the prognostic model were tested on the training set and the validation set. Nomogram combining risk score and prognostic clinical factors were used.
We obtained 497 differentially expressed genes (DEGs) and 865 differentially methylated genes (DMGs). Fifteen feature genes were selected from the 292 common genes between the DEGs and the DMGs to construct a classification model for radiotherapy. A DNA methylation signature including 10 genes was identified and used to establish a prognostic scoring model. The 10-gene methylation signature could effectively separate patients into two risk groups with markedly different OS time. Predictive capability of the methylation signature was successfully confirmed on the validation set. A nomogram comprised of risk score, radiotherapy, and recurrence was applied, with calibration plots displaying good concordance between predicted and actual OS. The DEGs were involved in 12 KEGG pathways most of which were correlated with metastasis and proliferation of various cancers, such as pathways in cancer, basal cell carcinoma, transcriptional misregulation in cancer and ECM-receptor interaction.
We Identified a 10-gene methylation signature for risk stratification of CC patients at pathological stages I-III, and ten methylation biomarkers might be novel therapeutic targets for CC.
宫颈癌（CC）患者通常预后较差。本研究旨在寻找用于预测 CC 患者生存的 DNA 甲基化特征。
我们从 TCGA 中选择了病理分期 I-III 期的 CC 患者，并选择了具有相应放疗信息和总生存（OS）的患者。对接受和未接受放疗的患者进行差异表达和甲基化分析。我们使用递归特征消除算法选择特征基因，构建支持向量机分类器。使用 LASSO Cox 比例风险模型识别预测预后的 DNA 甲基化生物标志物，构建预后评分模型。在训练集和验证集上测试分类器和预后模型。使用结合风险评分和预后临床因素的列线图。
我们获得了 497 个差异表达基因（DEGs）和 865 个差异甲基化基因（DMGs）。从 DEGs 和 DMGs 之间的 292 个共同基因中选择 15 个特征基因构建用于放疗的分类模型。鉴定出一个包含 10 个基因的 DNA 甲基化特征，用于建立预后评分模型。该 10 基因甲基化特征可有效将患者分为两组，两组的 OS 时间明显不同。在验证集上成功验证了该甲基化特征的预测能力。应用了一个由风险评分、放疗和复发组成的列线图，校准图显示预测 OS 与实际 OS 之间具有良好的一致性。DEGs 参与了 12 个 KEGG 途径，其中大多数与各种癌症的转移和增殖有关，如癌症途径、基底细胞癌、癌症转录失调和 ECM-受体相互作用途径。
我们确定了用于病理分期 I-III 期 CC 患者风险分层的 10 个基因甲基化特征，十个甲基化生物标志物可能是 CC 的新治疗靶点。
