Integrated machine learning identifies a cellular senescence-related prognostic model to improve outcomes in uterine corpus endometrial carcinoma.

Journal: Frontiers in immunology
PMID: 38994352

Abstract

BACKGROUND: Uterine Corpus Endometrial Carcinoma (UCEC) stands as one of the prevalent malignancies impacting women globally. Given its heterogeneous nature, personalized therapeutic approaches are increasingly significant for optimizing patient outcomes. This study investigated the prognostic potential of cellular senescence genes(CSGs) in UCEC, utilizing machine learning techniques integrated with large-scale genomic data.

Authors

  • Changqiang Wei
    Department of Obstetrics and Gynecology, The First Affiliated Hospital of Guangxi Medical University, Guangxi, China.
  • Shanshan Lin
    Department of Obstetrics and Gynecology, The First Affiliated Hospital of Guangxi Medical University, Guangxi, China.
  • Yanrong Huang
    Department of Obstetrics and Gynecology, The First Affiliated Hospital of Guangxi Medical University, Guangxi, China.
  • Yiyun Wei
    Department of Obstetrics and Gynecology, The First Affiliated Hospital of Guangxi Medical University, Guangxi, China.
  • Jingxin Mao
    Department of Science and Technology Industry, Chongqing Medical and Pharmaceutical College, Chongqing, China.
  • Jiangtao Fan
    Department of Obstetrics and Gynecology, The First Affiliated Hospital of Guangxi Medical University, Guangxi, China.

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