A lipid metabolism related gene signature predicts postoperative recurrence in pancreatic cancer through multicenter cohort validation

Abstract

Abstract Postoperative recurrence of pancreatic adenocarcinoma (PAAD) remains a major challenge. This study aims to establish and validate a lipid metabolism-related prognostic model to predict recurrence in PAAD patients. The TCGA-PAAD database was used to establish a training cohort, which was validated using the ICGC database and multiple center cohorts. A prognostic model based on LASSO Cox regression and a nomogram was developed and further validated. Among 196 lipid metabolism-related genes, four were selected for the prognostic model. Patients were stratified into high- and low-risk groups based on the risk score. Univariate and multivariate Cox regression analyses showed that tumor site, T stage, N stage, M stage, and risk score were significantly associated with progression-free interval (PFI). High-risk patients had worse PFI, overall survival (OS), and disease-specific survival (DSS) (all P < 0.05). Time-dependent ROC and decision curve analyses confirmed the superior diagnostic capacity of the nomogram. GSEA revealed enrichment in G2M checkpoint, glycolysis, estrogen response, and hypoxia pathways for the high-risk group. Additionally, high-risk scores correlated with poor immune infiltration, gene mutations, and tumor mutational burden (TMB). Single-cell analysis suggested that risk genes interact with various cell types to promote PAAD progression. A novel lipid metabolism-related prognostic model was developed and validated to predict recurrence and survival in PAAD patients, with strong accuracy and stability.