Abstract
Pancreatic cancer (PC) is a highly aggressive and fatal malignancy, primarily affecting older males. Curcumin, a potential anti-cancer agent, has been shown to regulate key molecules in cancer progression, but its specific mechanisms in PC remain unclear. We conducted a comprehensive database search to identify curcumin-related targets in PC. Gene expression and immune correlations were analyzed using the GEO database, identifying differentially expressed hub genes (DEHGs). A method involving machine learning was employed to identify feature genes and create a nomogram, using external datasets and molecular docking for preliminary validation. Consensus clustering and subgroup comparisons were also performed based on DEHGs expression. We identified 35 DEHGs strongly associated with immune cell infiltration. Five feature genes (VIM, CTNNB1, CASP9, AREG, HIF1A) were used to build a nomogram, with the classification model showing AUC values above 0.9 in both training and validation groups. Molecular docking highlighted potential binding sites of five feature genes for curcumin. Clustering analysis categorized PC samples into four distinct subgroups: C1 and CII, which showed high expression and elevated immune cell infiltration, and C2 and CI, which exhibited the opposite pattern. Significant variations in scores of DEHG were seen between C1 and C2, in addition to between CI and CII. Curcumin may target DEHGs to influence PC, regulating immune and tumor proliferation mechanisms. These outcomes provide potential insights for medical applications and upcoming research.