Open AccessPredicting overall survival of patients with hepatocellular carcinoma using a three‐category method based on DNA methylation and machine learning
Author(s) -
Dong RuiZhao,
Yang Xuan,
Zhang XinYu,
Gao PingTing,
Ke AiWu,
Sun Huichuan,
Zhou Jian,
Fan Jia,
Cai Jiabin,
Shi GuoMing
Publication year - 2019
Publication title -
journal of cellular and molecular medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.44
H-Index - 130
eISSN - 1582-4934
pISSN - 1582-1838
DOI - 10.1111/jcmm.14231
Subject(s) - hepatocellular carcinoma , dna methylation , proportional hazards model , methylation , carcinoma , regression , regression analysis , oncology , medicine , biology , computational biology , artificial intelligence , dna , computer science , machine learning , statistics , mathematics , genetics , gene , gene expression
Hepatocellular carcinoma (HCC) is closely associated with abnormal DNA methylation. In this study, we analyzed 450K methylation chip data from 377 HCC samples and 50 adjacent normal samples in the TCGA database. We screened 47,099 differentially methylated sites using Cox regression as well as SVM‐RFE and FW‐SVM algorithms, and constructed a model using three risk categories to predict the overall survival based on 134 methylation sites. The model showed a 10‐fold cross‐validation score of 0.95 and satisfactory predictive power, and correctly classified 26 of 33 samples in testing set obtained by stratified sampling from high, intermediate and low risk groups.