Integrating cell-free DNA methylation of SEPT9 and SFRP2 into a machine learning model for early diagnosis of HCC.

Journal: Biomarkers in medicine
Published Date:

Abstract

BACKGROUND: Hepatocellular carcinoma (HCC), a primary contributor to cancer-associated mortality, necessitates enhanced early detection. This study evaluated machine learning models that merge methylated SEPTIN9 (SEPT9) and secreted frizzled-related protein 2 (SFRP2) within circulating cell-free DNA (cfDNA) to detect HCC.

Authors

  • Dong Wang
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Zhihao Dai
    School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China.
  • Minghua Bai
    Department of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
  • Dong Liu
    Department of Gastrointestinal Surgery, The Third Hospital of Hebei Medical University, Shijiazhuang, China.
  • Yanru Feng
    Department of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
  • Quanquan Sun
    Department of Radiation Oncology, Zhejiang Cancer Hospital, Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang, China.
  • Tong Zhang
    Beijing University of Chinese Medicine, Beijing, China.
  • Liang Han
    Department of Computer Science, Missouri University of Science and Technology, Rolla, MO, 65401 United States. Electronic address: lh248@mst.edu.
  • Rui Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • Ji Zhu
    Department of Statistics, University of Michigan, Ann Arbor, Michigan.
  • Weifeng Hong
    Department of Medical Imaging, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China.
  • Weiwei Li
    Research Centre of Engineering and Technology for Computerized Dentistry, Ministry of Health, Peking University School and Hospital of Stomatology, Beijing 100081, PR China. Electronic address: liww@bjmu.edu.cn.

Keywords

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