Abstract
BACKGROUND: Pancreatic cancer represents a significant global health burden. Although dysregulated lipid metabolism and its associated inflammation drive tumorigenesis, their molecular interplay remains incompletely understood. This bioinformatics study investigates lipid metabolism-related genes (LMRGs) for prognostic prediction and treatment guidance in pancreatic cancer. METHODS: LMRGs were obtained from the Gene Set Enrichment Analysis (GSEA) database, while messenger ribonucleic acid (mRNA) expression profiles and clinical information were downloaded from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and International Cancer Genome Consortium (ICGC) databases. Cox regression analysis and the least absolute shrinkage and selection operator (LASSO) regression analysis were employed to screen prognosis-related genes, followed by the construction of a risk prediction model. Patients were stratified into high- and low-risk groups for prognosis and immune infiltration comparison. Potential therapeutic drugs for pancreatic cancer were predicted using the DSigDB database based on the identified LMRGs. RESULTS: We successfully established and validated a prognostic prediction model for pancreatic cancer patients based on six LMRGs (AGT, AHR, PLA2G6, PTGS2, TNFRSF21, and VDR). The 1-, 3-, and 5-year area under the receiver operating characteristic (ROC) curve values were 0.623, 0.698, and 0.720, respectively. Immune infiltration analysis showed that after prognostic risk stratification using the six-gene signature, the high-risk group had higher proportions of M0 macrophages and neutrophils. Furthermore, the expression of eight immune checkpoint-related genes was significantly increased in the high-risk group. DSigDB database analysis revealed four possible therapeutic drugs for pancreatic cancer: prolinedithiocarbamate, isoliquiritigenin, aspirin, and resveratrol. CONCLUSIONS: The risk score based on the six LMRGs provides prognostic insights for pancreatic cancer. High-risk pancreatic cancer populations are potentially associated with an immunosuppressive microenvironment. Candidate drugs screened based on LMRGs offer new possibilities for personalized treatment of pancreatic cancer.