Machine Learning-based Framework Develops a Tumor Thrombus Coagulation Signature in Multicenter Cohorts for Renal Cancer.

Journal: International journal of biological sciences
PMID: 38993563

Abstract

Renal cell carcinoma (RCC) is frequently accompanied by tumor thrombus in the venous system with an extremely dismal prognosis. The current Tumor Node Metastasis (TNM) stage and Mayo clinical classification do not appropriately identify preference-sensitive treatment. Therefore, there is an urgent need to develop a better ideal model for precision medicine. In this study, we developed a coagulation tumor thrombus signature for RCC with 10 machine-learning algorithms (101 combinations) based on a novel computational framework using multiple independent cohorts. The established tumor thrombus coagulation-related risk stratification (TTCRRS) signature comprises 10 prognostic coagulation-related genes (CRGs). This signature could predict survival outcomes in public and in-house protein cohorts and showed high performance compared to 129 published signatures. Additionally, the TTCRRS signature was significantly related to some immune landscapes, immunotherapy response, and chemotherapy. Furthermore, we also screened out hub genes, transcription factors, and small compounds based on the TTCRRS signature. Meanwhile, CYP51A1 can regulate the proliferation and migration properties of RCC. The TTCRRS signature can complement the traditional anatomic TNM staging system and Mayo clinical stratification and provide clinicians with more therapeutic options.

Authors

  • Tao Feng
    School of Pharmacy, Anhui University of Chinese Medicine, Anhui Key Laboratory of Modern Chinese Materia Medica Hefei 230012 People's Republic of China tfeng@mail.scuec.edu.cn wanggk@ahtcm.edu.cn.
  • Yue Wang
    Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
  • Wei Zhang
    The First Affiliated Hospital of Nanchang University, Nanchang, China.
  • Tingting Cai
    XBL-China, WuXi AppTec, Nanjing, 210038, China. Electronic address: cttcome@126.com.
  • Xi Tian
    Department of Urology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
  • Jiaqi Su
    Department of Urology, State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200433, China.
  • Zihao Zhang
    Institute for Hospital Management, Tsinghua University, Beijing, China.
  • Shengfeng Zheng
    Qingdao Institute, School of Life Medicine, Department of Urology, Fudan University Shanghai Cancer Center, Fudan University, Qingdao, 266500, China.
  • Shiqi Ye
    Department of Urology, State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200433, China.
  • Bo Dai
    School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China.
  • Ziliang Wang
    4School of Psychology, Beijing Normal University, Beijing, PR China.
  • Yiping Zhu
    Department of Urology, State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200433, China.
  • Hailiang Zhang
    Department of Urology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.
  • Kun Chang
    Department of Urology, State Key Laboratory of Genetic Engineering, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200433, China.
  • Dingwei Ye
    Department of Urology, Fudan University Shanghai Cancer Center; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China.

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