Machine learning-based new classification for immune infiltration of gliomas.

Journal: PloS one
Published Date: Oct 25, 2024

Abstract

BACKGROUND: Glioma is a highly heterogeneous and poorly immunogenic malignant tumor, with limited efficacy of immunotherapy. The characteristics of the immunosuppressive tumor microenvironment (TME) are one of the important factors hindering the effectiveness of immunotherapy. Therefore, this study aims to reveal the immune microenvironment (IME) characteristics of glioma and predict different immune subtypes using machine learning methods, providing guidance for immune therapy in glioma.

Authors

  • Feng Yuan
    School of Information Engineering, Shandong Management University, Jinan 250357, China.
  • Yingshuai Wang
    Department of Computer, School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China.
  • Lei Yuan
    Department of Pharmacy, Baodi People's Hospital, Tianjin, China.
  • Lei Ye
    ZJU-Bioer Technology Research & Development Center, Hangzhou Bioer Technology, Hangzhou, 310053, China.
  • Yangchun Hu
    Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
  • Hongwei Cheng
    Department of Neurosurgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
  • Yan Li
    Interdisciplinary Research Center for Biology and Chemistry, Liaoning Normal University, Dalian, China.

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