Predicting breast cancer prognosis based on a novel pathomics model through CHEK1 expression analysis using machine learning algorithms.

Journal: PloS one
PMID: 40344565

Abstract

BACKGROUND: Checkpoint kinase 1 (CHEK1) is often overexpressed in solid tumors. Nonetheless, the prognostic significance of CHEK1 in breast cancer (BrC) remains unclear. This study used pathomics leverages machine learning to predict BrC prognosis based on CHEK1 gene expression..

Authors

  • Chen Chen
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
  • Dan Gao
    Breast and Thyroid Center, The First People's Hospital of Zunyi (The Third Affiliated Hospital of Zunyi Medical University), Zunyi, Guizhou, China.
  • Huan Yue
    Clinical Laboratory, The First People's Hospital of Zunyi (The Third Affiliated Hospital of Zunyi Medical University), Zunyi, Guizhou, China.
  • Huijing Wang
    Breast and Thyroid Center, The First People's Hospital of Zunyi (The Third Affiliated Hospital of Zunyi Medical University), Zunyi, Guizhou, China.
  • Rui Qu
    Breast and Thyroid Center, The First People's Hospital of Zunyi (The Third Affiliated Hospital of Zunyi Medical University), Zunyi, Guizhou, China.
  • Xiaochi Hu
    Breast and Thyroid Center, The First People's Hospital of Zunyi (The Third Affiliated Hospital of Zunyi Medical University), Zunyi, Guizhou, China.
  • Libo Luo
    Breast and Thyroid Center, The First People's Hospital of Zunyi (The Third Affiliated Hospital of Zunyi Medical University), Zunyi, Guizhou, China.

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