A 10-gene prognostic methylation signature for stage I-III cervical cancer
A 10-gene prognostic methylation signature for stage I-III cervical cancer
复制标题
I–III 期宫颈癌的 10 个基因预后甲基化特征
DOI：
10.1007/s00404-020-05524-3
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发表时间：
2020-05-01
影响因子：
2.6
通讯作者：
Guan, Rui
中科院分区：
文献类型：
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作者：
Cai, Shengyun;Yu, Xiaomin;Guan, Rui
目的宫颈癌（Cervical Cancer，CC）患者预后差.本研究的目的是寻找一个DNA甲基化的签名，预测CC患者的生存。方法选择病理分期为I ~ III期的CC患者，根据TCGA中相应的放射治疗和总生存期（OS）信息。对放疗前后患者进行差异表达和甲基化分析。采用递归特征消除算法选取特征基因，构建支持向量机分类器。使用LASSO Cox-Proportional Hazards模型鉴定预测预后的DNA甲基化生物标志物以构建预后评分模型。在训练集和验证集上测试分类器和预测模型。采用列线图结合危险评分和预后临床因素。结果共获得497个差异表达基因（DEG）和865个差异甲基化基因（DMG）。从DEG和DMG的292个共有基因中筛选出15个特征基因构建放射治疗分类模型。鉴定了包括10个基因的DNA甲基化标记，并用于建立预后评分模型。10个基因甲基化标记可以有效地将患者分为OS时间显著不同的两个风险组。在验证集上成功证实了甲基化特征的预测能力。应用由风险评分、放疗和复发组成的诺模图，校准图显示预测OS和实际OS之间具有良好的一致性。DEG涉及12个KEGG通路，其中大部分与各种癌症的转移和增殖相关，例如癌症中的通路、基底细胞癌、癌症中的转录失调和ECM-受体相互作用。结论我们发现了一个10个基因甲基化标记，可用于病理I ~ III期CC患者的危险分层，其中10个甲基化标记可能成为CC治疗的新靶点。
Purpose Cervical cancer (CC) patients usually have poor prognosis. The present study aims to find a DNA methylation signature for predicting survival of CC patients. Methods 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. Results 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. Conclusion 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.