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

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.

Methods

This study utilized 150 pancreatic cancer samples from The Cancer Genome Atlas-Pancreatic Adenocarcinoma (TCGA-PAAD) and 69 from GSE62452 [Gene Expression Omnibus (GEO)] for training and

SUBMITTER: Zou Q 

PROVIDER: S-EPMC12170279 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature