Identification of key metabolism-related genes and pathways in spontaneous preterm birth: combining bioinformatic analysis and machine learning.

Journal: Frontiers in endocrinology
PMID: 39229380

Abstract

BACKGROUND: Spontaneous preterm birth (sPTB) is a global disease that is a leading cause of death in neonates and children younger than 5 years of age. However, the etiology of sPTB remains poorly understood. Recent evidence has shown a strong association between metabolic disorders and sPTB. To determine the metabolic alterations in sPTB patients, we used various bioinformatics methods to analyze the abnormal changes in metabolic pathways in the preterm placenta via existing datasets.

Authors

  • Wenqi Lv
    Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China.
  • Han Xie
    Key Laboratory of Magnetic Resonance in Biological Systems, Innovation Academy for Precision Measurement Science and Technology, Wuhan, China.
  • Shengyu Wu
    Department of Obstetrics, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai, China.
  • Jiaqi Dong
    Department of Obstetrics, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai, China.
  • Yuanhui Jia
    Department of Clinical Medicine, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai, sChina.
  • Hao Ying
    Department of Obstetrics, Shanghai First Maternity and Infant Hospital, Tongji University School of Medicine, Shanghai 201204, China.

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