DNA甲基化驱动的转录表达特征作为肝癌预后预测标志物的价值

doi:10.16288/j.yczz.20-139

遗传 ›› 2020, Vol. 42 ›› Issue (8): 775-787.doi: 10.16288/j.yczz.20-139

DNA甲基化驱动的转录表达特征作为肝癌预后预测标志物的价值

骆红波¹, 曹鹏博²(), 周钢桥^1,²()

^1. 贵州大学医学院,贵阳 550025
^2. 军事科学院军事医学研究院辐射医学研究所,蛋白质组学国家重点实验室,国家蛋白质科学中心(北京),北京 100850;

收稿日期:2020-05-18 修回日期:2020-07-10 出版日期:2020-08-20 发布日期:2020-07-10
通讯作者: 曹鹏博,周钢桥 E-mail:birchcpb@163.com;zhougq114@126.com
作者简介:骆红波,硕士研究生,专业方向：肝癌转录组学。E-mail: 965589073@qq.com
基金资助:
国家科技重大专项艾滋病和病毒性肝炎等重大传染病防治专项项目资助编号(2018ZX10732202);国家科技重大专项艾滋病和病毒性肝炎等重大传染病防治专项项目资助编号(2017ZX10203205)

Prognostic and predictive value of a DNA methylation-driven transcriptional signature in hepatocellular carcinoma

Hongbo Luo¹, Pengbo Cao²(), Gangqiao Zhou^1,²()

^1. Guizhou University School of Medicine, Guiyang 550025, China
^2. State Key Lab of Proteomics, National Center for Protein Sciences (Beijing), Institute of Radiation Medicine, Academy of Military Medical Sciences, Academy of Military Sciences, Beijing 100850, China;

Received:2020-05-18 Revised:2020-07-10 Online:2020-08-20 Published:2020-07-10
Contact: Cao Pengbo,Zhou Gangqiao E-mail:birchcpb@163.com;zhougq114@126.com
Supported by:
Supported by the National S&T Major Project Nos(2018ZX10732202);Supported by the National S&T Major Project Nos(2017ZX10203205)

摘要/Abstract

摘要：

肝细胞癌(hepatocellular carcinoma,简称肝癌)是最常见的恶性肿瘤之一。DNA甲基化的异常是恶性肿瘤的特征之一,并被发现在肝癌等肿瘤的发生发展中发挥重要作用。为了能为肝癌患者提供新的临床预后预测标志物,本研究首先采用整合组学分析策略在全基因组范围内鉴定与肝癌患者预后相关的DNA甲基化驱动的差异表达基因;然后,采用LASSO (least absolute shrinkage and selection operator)分析建立了10个最优基因组合的预后预测模型。Cox比例风险回归分析显示,在校正临床特征参数后,此预测模型高风险评分与患者不良预后显著相关,表明该模型具有潜在的独立预后价值。受试者工作特征(receiver operating characteristic, ROC)曲线分析显示该风险评分模型在预测患者短期和长期预后方面优于其他已被报道的肝癌预后预测模型。基因集富集分析(gene set enrichment analysis, GSEA)表明,高风险评分与细胞周期和DNA损伤修复通路相关。以上结果表明,本研究构建了一个基于10个DNA甲基化驱动基因的预后风险评分模型,该模型可作为肝癌患者的潜在预后生物标志物,有助于肝癌患者的生存预后评估和治疗策略的指导。

关键词: 肝癌, 转录组, 表观基因组, 预后模型

Abstract:

Hepatocellular carcinoma (HCC) is one of the most common cancers worldwide. DNA methylation alterations are frequently observed in malignant tumours and play critical roles in the development of cancers, including HCC. To provide novel clinical prognosis biomarkers for HCC patients, we first performed a comprehensive analysis and identified a collection of prognosis-associated genes with DNA methylation-driven expression dysregulation in HCCs. Then, we optimally established a 10-gene prognostic risk score model using the least absolute shrinkage and selection operator (LASSO) analysis. Cox's proportional hazards regression analysis revealed that the high-risk score is significantly associated with poor prognosis after being adjusted by clinical parameters, indicating its potential prognostic value. The receiver operating characteristic curve (ROC) analysis showed that this 10-gene prognostic risk score model outperformed several other publicly available models in predicting both short- and long-term prognosis. Gene set enrichment analysis revealed that the high-risk score is relevantly associated with pathways involved in cell cycle and DNA damage repair. The above results indicate that we have constructed a 10-DNA-methylation-driven-gene prognostic risk score model, which might serve as a potential prognostic biomarker for HCC patients and guide treatment decisions for patients at high risk of tumour progression.

Key words: hepatocellular carcinoma, transcriptome, epigenome, prognostic model

骆红波, 曹鹏博, 周钢桥. DNA甲基化驱动的转录表达特征作为肝癌预后预测标志物的价值[J]. 遗传, 2020, 42(8): 775-787.

Hongbo Luo, Pengbo Cao, Gangqiao Zhou. Prognostic and predictive value of a DNA methylation-driven transcriptional signature in hepatocellular carcinoma[J]. Hereditas(Beijing), 2020, 42(8): 775-787.

图/表 8

表1

图1

表2

图2

建立和验证10个基因的预后评分模型 A：使用LASSO回归分析和10倍交叉验证构建预后预测评分模型。左图为基于最小原则(minimum criteria)采用10倍交叉验证对LASSO模型进行调参,通过LASSO回归交叉验证计算的部分似然偏差(partial likelihood deviance)被绘制为log(λ)的函数。y轴表示部分似然偏差,x轴表示log(λ),沿x轴上方的数字表示预测变量的平均数量,红点表示具有给定λ的每个模型的平均偏差值,穿过红点的竖线表示偏差的上限值和下限值,垂直虚线分别表示最小误差的λ值和最大λ值。右图为51个预后基因的LASSO系数分布,垂直虚线表示采用10倍交叉验证选取的基因数,当基因数为10时,部分似然偏差为最小值,对应最小λ值。B：基于LASSO系数和基因表达在模型训练队列建立预后模型。左图为模型训练队列中肝癌患者的风险评分及生存时间的散点图,中间图为模型训练队列中不同风险评分组(中位数分组)患者的生存曲线图,右图为采用ROC曲线分析评估预测模型对训练队列中患者生存率的预测性能。C：在模型验证队列中验证预后模型。左图为模型验证队列中肝癌患者的风险评分及生存时间的散点图,中间图为模型验证队列中不同风险评分组(中位数分组)患者的生存曲线图,右图为ROC曲线分析评估预测模型对验证队列中患者生存率的预测性能。由于验证队列1和3中患者达到5年生存的数量较少,所以并未对患者5年生存率进行ROC分析。卡方检验(χ2)用于评价组间患者生存分布差异;组间生存差异采用Log-rank方法进行比较;ROC分析用于评估模型预测性能。AUC：曲线下面积(area under curve);CI：置信区间(confidence interval);HR：风险比例(hazard ratios);LASSO：最小绝对值收敛和选择算子(least absolute shrinkage and selection operator);ROC：受试者工作特征曲线(receiver operating characteristic curve)。"

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研究队列	数据集	样本量	数据类型	数据来源
发掘队列	SRP069212	20例配对的癌和癌旁组织	mRNA表达	GEO
	SRP118972	12例癌组织样本和8例癌旁组织	mRNA表达	GEO
	GSE89852	33例配对的癌和癌旁组织	DNA甲基化	GEO
	GSE54503	66例配对的癌和癌旁组织	DNA甲基化	GEO
模型训练队列	TCGA-LIHC	371例癌组织和50例癌旁组织	mRNA表达和DNA甲基化	TCGA
模型训练队列	ICGC-LIRI-JP	203例癌组织	mRNA表达	ICGC
模型验证队列	GSE76427	115例癌组织	基因表达	GEO
模型验证队列	GSE84005	37例癌组织	基因表达	GEO

基因	HR (95% CI)	P值	LASSO系数	基因组变异频率(%)
CDCA8	2.21 (1.54~3.01)	<0.0001	0.1194	0.0
PRC1	1.85 (1.29~2.54)	0.0005	0.08869	0.3
MAPT	1.72 (1.21~2.46)	0.0021	0.2597	1.7
SFN	1.72 (1.21~2.45)	0.0021	0.001652	0.3
STC2	1.73 (1.22~2.43)	0.0021	0.03600	0.8
MYO18B	1.69 (1.19~2.37)	0.0031	0.1932	4.0
PBK	1.67 (1.17~2.35)	0.0036	0.06524	6.0
MAEL	1.55 (1.09~2.17)	0.013	-0.1766	10.0
TTC39A	1.55 (1.09~2.17)	0.013	-0.01347	1.4
LPL	1.55 (1.10~2.18)	0.014	0.003126	7.0

DNA甲基化驱动的转录表达特征作为肝癌预后预测标志物的价值

Prognostic and predictive value of a DNA methylation-driven transcriptional signature in hepatocellular carcinoma

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