Machine learning-driven prognostic model based on sphingolipid-related gene signature in pancreatic cancer: development and validation.

Oncology/Hematology Urology Pediatrics Hospital-Based Medicine Nursing
Journal: Translational cancer research
Published Date: May 26, 2025

Abstract

BACKGROUND: Pancreatic cancer, a highly malignant tumor with poor prognosis, lacks effective early diagnosis and treatment strategies. Sphingolipids have emerged as key players in tumorigenesis, with certain sphingolipid-related genes linked to patient survival. This study aims to identify prognostic glycosphingolipid (GSL)-related genes and construct a predictive model to improve survival prediction and guide personalized treatment. By providing potential biomarkers, our findings may enhance clinical decision-making and offer new insights into pancreatic cancer diagnosis and therapy.

Authors

  • Qi Zou
  • Hailin Jiang
    Center for Pancreatic Cancer Research and Department of Immunology, South China University of Technology School of Medicine, Guangzhou, China.
  • Qihui Sun
    Center for Pancreatic Cancer Research and Department of Immunology, South China University of Technology School of Medicine, Guangzhou, China.
  • Qian Peng
  • Jie He
    Clinical Medical College of Chengdu Medical College, Chengdu, Sichuan, China.
  • Keping Xie
    Center for Pancreatic Cancer Research, The South China University of Technology School of Medicine, Guangzhou, Guangdong, China.
  • Fang Wei
    School of Dentistry, University of California Los Angeles, Los Angeles, CA, USA.

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