Machine learning-based integration develops a stress response stated T cell (Tstr)-related score for predicting outcomes in clear cell renal cell carcinoma.

Journal: International immunopharmacology
PMID: 38599101

Abstract

BACKGROUND: Establishment of a reliable prognostic model and identification of novel biomarkers are urgently needed to develop precise therapy strategies for clear cell renal cell carcinoma (ccRCC). Stress response stated T cells (Tstr) are a new T-cell subtype, which are related to poor disease stage and immunotherapy response in various cancers.

Authors

  • Shuai Yang
    School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei, Anhui, China.
  • Zhaodong Han
    Department of Urology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong 510180, China.
  • Zeheng Tan
    Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong 510280, China.
  • Zhenjie Wu
    Department of Urology, Changzheng Hospital, Naval Military Medical University, Shanghai, PR China.
  • Jianheng Ye
    Department of Urology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong 510180, China.
  • Shanghua Cai
    Guangdong Provincial Key Laboratory of Urology, Department of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong 510120, China; Guangzhou National Laboratory, No. 9 XingDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou, Guangdong 510005, China; State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macau 999078, China.
  • Yuanfa Feng
    Guangdong Provincial Key Laboratory of Urology, Department of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong 510120, China.
  • Huichan He
    Guangdong Provincial Key Laboratory of Urology, Department of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong 510120, China.
  • Biyan Wen
    School of Medicine, South China University of Technology, Guangzhou, Guangdong 510006, China.
  • Xuejin Zhu
    Department of Urology, Affiliated Cancer Hospital and Institute of Guangzhou Medical University, Guangzhou, Guangdong 510095, China.
  • Yongkang Ye
    Department of Urology, The Tenth Affiliated Hospital of Southern Medical University (Dongguan people's hospital), Dongguan, Guangdong 523059, China.
  • Huiting Huang
    Department of Urology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong 510280, China.
  • Sheng Wang
    Intensive Care Medical Center, Tongji Hospital, School of Medicine, Tongji University, Shanghai, 200065, People's Republic of China.
  • Weide Zhong
    Department of Urology, Guangzhou First People's Hospital, South China University of Technology, Guangzhou, Guangdong 510180, China; Guangdong Provincial Key Laboratory of Urology, Department of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong 510120, China; Guangzhou National Laboratory, No. 9 XingDaoHuanBei Road, Guangzhou International Bio Island, Guangzhou, Guangdong 510005, China; State Key Laboratory of Quality Research in Chinese Medicine, Macau University of Science and Technology, Taipa, Macau 999078, China. Electronic address: eyweidezhong@scut.edu.cn.
  • Yulin Deng
    Guangdong Provincial Key Laboratory of Urology, Department of Urology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong 510120, China. Electronic address: urodengyl@126.com.

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