A machine learning-based screening model for the early detection of prostate cancer developed using serum microRNA data from a mixed cohort of 8,741 participants.

Journal: Discover oncology
Published Date: Jul 15, 2025

Abstract

OBJECTIVE: Early detection of prostate cancer (PCa) can improve the prognosis of patients. Currently, the role of the prostate specific antigen test for PCa screening remains debatable. We aimed to develop an efficient and clinically applicable method for the screening of PCa by the noninvasive screening of several serum microRNA (miRNA) levels.

Authors

  • Cong Lai
    Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
  • Zhensheng Hu
  • Zhuohang Li
    Department of Computer Science, Vanderbilt University, Nashville, TN 37212, United States.
  • Zhikai Wu
    Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang West Road, Guangzhou, 510000, Guangdong, China.
  • Kuiqing Li
    Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
  • Lin Li
    Department of Medicine III, LMU University Hospital, LMU Munich, Munich, Germany.
  • Hongze Liu
    Zhongshan School of Medicine, Sun Yat-sen University, 74 Zhongshan 2nd Road, Yuexiu District, Guangzhou, 510080, Guangdong, China.
  • Juanyi Shi
    Department of Urology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, No. 107 Yanjiang West Road, Guangzhou, 510000, Guangdong, China.
  • Yi Zhou
    Eye Center of Xiangya Hospital, Central South University, Changsha, Hunan, China.
  • Kewei Xu
    Department of Urology, Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
  • Cheng Liu
    Key Lab of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China; School of Earth and Space Sciences, University of Science and Technology of China, Hefei 230026, China; Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Anhui Province Key Laboratory of Polar Environment and Global Change, University of Science and Technology of China, Hefei 230026, China. Electronic address: chliu81@ustc.edu.cn.

Keywords

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