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基于 DNA 甲基化和机器学习的三类方法预测肝细胞癌患者的总生存期。

Predicting overall survival of patients with hepatocellular carcinoma using a three-category method based on DNA methylation and machine learning.

机构信息

Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.

Key Laboratory of Carcinogenesis and Cancer Invasion of Ministry of Education, Shanghai, China.

出版信息

J Cell Mol Med. 2019 May;23(5):3369-3374. doi: 10.1111/jcmm.14231. Epub 2019 Feb 19.

Abstract

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.

摘要

肝细胞癌 (HCC) 与异常的 DNA 甲基化密切相关。本研究分析了 TCGA 数据库中 377 例 HCC 样本和 50 例相邻正常样本的 450K 甲基化芯片数据。我们使用 Cox 回归以及 SVM-RFE 和 FW-SVM 算法筛选了 47099 个差异甲基化位点,并使用三个风险类别基于 134 个甲基化位点构建了一个模型来预测总生存期。该模型在 10 倍交叉验证中的评分达到 0.95,具有良好的预测能力,并正确分类了从高、中、低风险组分层采样获得的测试集中的 26 个样本。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c78/6484308/e80fcec33e42/JCMM-23-3369-g001.jpg

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