Deciphering breast cancer prognosis: a novel machine learning-driven model for vascular mimicry signature prediction.

Journal: Frontiers in immunology
PMID: 39165361

Abstract

BACKGROUND: In the ongoing battle against breast cancer, a leading cause of cancer-related mortality among women globally, the urgent need for innovative prognostic markers and therapeutic targets is undeniable. This study pioneers an advanced methodology by integrating machine learning techniques to unveil a vascular mimicry signature, offering predictive insights into breast cancer outcomes. Vascular mimicry refers to the phenomenon where cancer cells mimic blood vessel formation absent of endothelial cells, a trait associated with heightened tumor aggression and diminished response to conventional treatments.

Authors

  • Xue Li
    Department of Clinical Research Center, Dazhou Central Hospital, Dazhou 635000, China.
  • Xukui Li
    Research Laboratory Center, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China.
  • Bin Yang
    School of Control Science and Engineering, Dalian University of Technology, Dalian 116024, PR China. Electronic address: yangbin@dlut.edu.cn.
  • Songyang Sun
    Research Laboratory Center, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China.
  • Shu Wang
    Department of Radiology, Affiliated Hospital 6 of Nantong University, Yancheng Third People's Hospital, Yancheng, Jiangsu, China.
  • Fuxun Yu
    Research Laboratory Center, Guizhou Provincial People's Hospital, Guiyang, Guizhou, China.
  • Tao Wang
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

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