Prediction model for malignant pulmonary nodules based on cfMeDIP-seq and machine learning.

Journal: Cancer science
PMID: 34251068

Abstract

Cell-free methylated DNA immunoprecipitation and high-throughput sequencing (cfMeDIP-seq) is a new bisulfite-free technique, which can detect the whole-genome methylation of blood cell-free DNA (cfDNA). Using this technique, we identified differentially methylated regions (DMR) of cfDNA between lung tumors and normal controls. Based on the top 300 DMR, we built a random forest prediction model, which was able to distinguish malignant lung tumors from normal controls with high sensitivity and specificity of 91.0% and 93.3% (AUROC curve of 0.963). In summary, we reported a non-invasive prediction model that had good ability to distinguish malignant pulmonary nodules.

Authors

  • Jian Qi
    Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
  • Bo Hong
    Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, 100084, China. hongbo@tsinghua.edu.cn.
  • Rui Tao
    Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Ruifang Sun
    Department of Tumor Biobank, Shanxi Cancer Hospital, Taiyuan, China.
  • Huanhu Zhang
    Department of Tumor Biobank, Shanxi Cancer Hospital, Taiyuan, China.
  • Xiaopeng Zhang
  • Jie Ji
    Network and Information Center, Shantou University, Shantou, Guangdong, China.
  • Shujie Wang
    Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
  • Yanzhe Liu
    Casgenome Medicine (Hefei) Ltd, Hefei, China.
  • Qingmei Deng
    Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.
  • Hongzhi Wang
  • Dahai Zhao
    Department of Respiratory and Critical Care Medicine, The Second Affiliated Hospital of Anhui Medical University, Hefei, China.
  • Jinfu Nie
    Anhui Province Key Laboratory of Medical Physics and Technology, Institute of Health and Medical Technology, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.

Keywords

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