基于甲基化组学数据建立并验证肝细胞癌预后评价模型研究

doi:10.19586/j.2095-2341.2022.0208

生物技术进展 ›› 2023, Vol. 13 ›› Issue (2): 282-290.DOI: 10.19586/j.2095-2341.2022.0208

• 研究论文 • 上一篇

基于甲基化组学数据建立并验证肝细胞癌预后评价模型研究

李冠群¹(), 梁超杰², 夏峰¹()

^1.首都医科大学附属北京同仁医院普外科，北京 100730
^2.山西医科大学第一医院肝胆胰外科，太原 030001

收稿日期:2022-12-08 接受日期:2023-02-07 出版日期:2023-03-25 发布日期:2023-04-07
通讯作者: 夏峰
作者简介:李冠群 E-mail: qunmm55@163.com；
基金资助:
山西省应用基础研究计划项目(201901D211480)

Study on Establishment and Validation of Prognostic Evaluation Model for Hepatocellular Carcinoma Based on Methylomics Database

Guanqun LI¹(), Chaojie LANG², Feng XIA¹()

^1.General Surgery Department，Beijing Tongren Hospital，Capital Medical University，Beijing 100730，China
^2.Department of Hepatobiliary Pancreatic Surgery，the First Hospital of Shanxi Medical University，Taiyuan 030001，China

Received:2022-12-08 Accepted:2023-02-07 Online:2023-03-25 Published:2023-04-07
Contact: Feng XIA

摘要/Abstract

摘要：

利用甲基化CG位点建立并验证肝细胞癌（hepatocellular carcinoma，HCC）预后相关模型是当前的研究热点。利用R语言平台分析肝癌基因组图谱数据库（TCGA-LIHC）和基因表达综合数据库（GSE136380）甲基化组学数据，采用limma包分析TCGA-LIHC和GSE136380数据差异甲基化CG位点，采用wgcna包对GSE136380进行加权基因共表达网络分析，确定三者交集为关键甲基化CG位点集，共获得83个甲基化CG位点。利用LASSO-COX风险比例回归分析筛选出6个甲基化CG位点，包括cg15744128、cg24282479、cg17922226、cg13090969、cg23505966和cg08708079，构建并验证HCC外科切除术后的预后模型。拟合模型在训练集中1、3和5年的AUC分别是0.756（95%CI:0.644~0.876）、0.749（95%CI:0.638~0.854）和0.797（95%CI:0.661~0.920）；测试集中1、3和5年的AUC分别为0.848（95%CI:0.680~0.990）、0.833（95%CI:0.697~0.974）和0.685（95%CI:0.553~0.903）。Kaplan-Meier曲线展示出模型能精准鉴别出HCC切除术后的预后差的高危患者（logrank检验P<0.001）和易复发患者（logrank检验P<0.001），研究结果有助于进一步制定治疗方案使患者受益。

关键词: 肝细胞癌, 公共数据库, 预后评价, 甲基化, 列线图

Abstract:

It is a current research hotspot to establish and validate the prognosis related model of hepatocellular carcinoma （HCC） using methylation CG sites. On the R language platform， methylomics data of TCGA-LIHC and GSE136380 were analyzed. Limma package was used to analyze the differential methylation CG sites of TCGA-LIHC and GSE136380， and wgcna package was used to analyze the weighted gene co expression network of GSE136380. The intersection of the three was determined as the key methylation CG site set， and then 83 methylation CG sites were obtained. LASSO-COX risk proportion regression analysis was used to screen six methylation CG sites， including cg15744128， cg24282479， cg17922226， cg13090969， cg23505966 and cg08708079， to construct and validate the prognosis model of HCC after surgical resection. The AUC of the fitting model in training concentration for 1， 3 and 5 years is 0.756 （95% CI：0.644~0.876）， 0.749 （95% CI：0.638~0.854） and 0.797 （95% CI：0.661~0.920） respectively. The AUC of the test set for 1， 3 and 5 years were 0.848 （95% CI： 0.680~0.990）， 0.833 （95% CI：0.697~0.974） and 0.685 （95% CI： 0.553~0.903）， respectively. Kaplan Meier curve showed that it could accurately identify high-risk patients with poor prognosis （logrank test P<0.001） and patients with easy recurrence （logrank test P<0.001） after HCC resection， which is helpful to further develop the treatment plan and benefit the patient.

Key words: hepatocellular carcinoma, public database, prognostic evaluation, methylation, nomogram

中图分类号:

R735.7

李冠群, 梁超杰, 夏峰. 基于甲基化组学数据建立并验证肝细胞癌预后评价模型研究[J]. 生物技术进展, 2023, 13(2): 282-290.

Guanqun LI, Chaojie LANG, Feng XIA. Study on Establishment and Validation of Prognostic Evaluation Model for Hepatocellular Carcinoma Based on Methylomics Database[J]. Current Biotechnology, 2023, 13(2): 282-290.

图/表 7

图1 本研究流程图

Fig. 1 Flow chart of this study

图2 候选甲基化CG位点筛选A：TCGA-LIHC差异性甲基化CG位点火山图。Bonferroni校正P<0.05，|deltaβ|>0.35为差异性CG位点，其中红色为高甲基化CG位点，蓝色为低甲基化CG位点；B：表示GSE136380差异性甲基化CG位点火山图。Bonferroni校正P<0.05，|deltaβ|>0.15，其中红色为高甲基化CG位点，蓝色为低甲基化CG位点；C：基因层次聚类对共表达网络模块的聚类树状图及模块划分，主要模块为灰色、青色和蓝色；D：模块—肝癌的关联性分析，3种模块对HCC的相关性分析，每个单元格都包含相应的相关性和P值；E：加权共表达网络分析。维恩图表示3个数据集重合83个CG位点为候选位点

Fig. 2 Screening of candidate methylation CG sites

图3 LASSO筛选出备选甲基化CG位点A：83个候选CG位点五倍交叉验证的代表性重复的LASSO系数。第1条垂直虚线对应于交叉验证中最小化二项式偏差的λ值，即λmin（0.05899029）；第2条虚线对应1个标准误范围的λ值，即λ1se（0.1030872），用于选择标志物CG位点；B：LASSO系数路径的剖面图，83个CG位点的系数显著不同于零。

Fig. 3 Candidate methylation CG sites screened by LASSO

图4 预后风险模型及其6个CG位点的AUC曲线A：训练集；B：测试集；C：全部病人；黑色曲线为预后风险模型，彩色曲线代表6个CG位点

Fig. 4 AUC curve of prognostic risk model and its 6 CG sites

图5 预后风险模型列线图及其预测效能A：肝癌患者外科手术后预后甲基化CG位点模型的列线图；B：预后风险模型在训练集中1、3、5年的时间依赖ROC曲线及其相应的AUC值；C：校正曲线验证预后风险模型在训练集中的1、3、5年预测效能；D：预后风险模型在测试集中的1、3、5年时间依赖ROC曲线及其相应年的AUC值；E：校正曲线验证预后风险模型在测试集中的1、3、5年的预测效能

Fig. 5 Nomogram of prognostic risk model and its predictive effectiveness

图6 预后风险模型的DCA曲线A~C: 训练集中DCA曲线；D~E: 测试集中DCA曲线

Fig. 6 DCA curve of prognostic risk model

图7 Kaplan-Meier曲线展示预后风险模型区分总体生存率和无复发生存率A：训练集的总生存率；B：测试集的总生存率；C：训练集的无复发生存率；D：测试集的无复发生存率

Fig. 7 Kaplan-Meier curve showed the overall survival rate and recurrence-free survival differentiated by the prognostic risk model

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基于甲基化组学数据建立并验证肝细胞癌预后评价模型研究

Study on Establishment and Validation of Prognostic Evaluation Model for Hepatocellular Carcinoma Based on Methylomics Database

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