比例危险模型
DNA甲基化
肝细胞癌
单变量
基因
肿瘤科
甲基化
Lasso(编程语言)
内科学
多元统计
生物
医学
计算生物学
生物信息学
基因表达
遗传学
计算机科学
机器学习
万维网
作者
Junyu Long,Peipei Chen,Jianzhen Lin,Yi Bai,Yang Xu,Jin Bian,Yu Hung Lin,Dongxu Wang,Xiaobo Yang,Yongchang Zheng,Xinting Sang,Hong Zhao
出处
期刊:Theranostics
[Ivyspring International Publisher]
日期:2019-01-01
卷期号:9 (24): 7251-7267
被引量:81
摘要
In this study, we performed a comprehensively analysis of gene expression and DNA methylation data to establish diagnostic, prognostic, and recurrence models for hepatocellular carcinoma (HCC). Methods: We collected gene expression and DNA methylation datasets for over 1,200 clinical samples. Integrated analyses of RNA-sequencing and DNA methylation data were performed to identify DNA methylation-driven genes. These genes were utilized in univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses to build a prognostic model. Recurrence and diagnostic models for HCC were also constructed using the same genes. Results: A total of 123 DNA methylation-driven genes were identified. Two of these genes (SPP1 and LCAT) were chosen to construct the prognostic model. The high-risk group showed a markedly unfavorable prognosis compared to the low-risk group in both training (HR = 2.81; P < 0.001) and validation (HR = 3.06; P < 0.001) datasets. Multivariate Cox regression analysis indicated the prognostic model to be an independent predictor of prognosis (P < 0.05). Also, the recurrence model successfully distinguished the HCC recurrence rate between the high-risk and low-risk groups in both training (HR = 2.22; P < 0.001) and validation (HR = 2; P < 0.01) datasets. The two diagnostic models provided high accuracy for distinguishing HCC from normal samples and dysplastic nodules in the training and validation datasets, respectively. Conclusions: We identified and validated prognostic, recurrence, and diagnostic models that were constructed using two DNA methylation-driven genes in HCC. The results obtained by integrating multidimensional genomic data offer novel research directions for HCC biomarkers and new possibilities for individualized treatment of patients with HCC.
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