Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence.

Journal: Frontiers in immunology
PMID: 39916957

Abstract

BACKGROUND: Diabetic nephropathy (DN) is a complication of systemic microvascular disease in diabetes mellitus. Abnormal glycolysis has emerged as a potential factor for chronic renal dysfunction in DN. The current lack of reliable predictive biomarkers hinders early diagnosis and personalized therapy.

Authors

  • Xiaoyin Wu
    School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.
  • Buyu Guo
    The First Clinical Medical College, Lanzhou University, Lanzhou, China.
  • Xingyu Chang
    Obstetrics and Gynecology Hospital, Fudan University, Shanghai, China.
  • Yuxuan Yang
    The First Clinical Medical College, Lanzhou University, Lanzhou, China.
  • Qianqian Liu
    The First Clinical Medical College, Lanzhou University, Lanzhou, China.
  • Jiahui Liu
    The First Clinical Medical College, Lanzhou University, Lanzhou, China.
  • Yichen Yang
    The First Clinical Medical College, Lanzhou University, Lanzhou, China.
  • Kang Zhang
    Xifeng District People's Hospital, Qingyang, China.
  • Yumei Ma
    Qilihe District People's Hospital, Lanzhou, China.
  • Songbo Fu
    School of Basic Medical Sciences, Lanzhou University, Lanzhou, China.

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