Abstract
BACKGROUND: Pancreatic adenocarcinoma (PDAC) exhibits a complex microenvironment with diverse cell populations influencing patient prognosis. Single-cell RNA sequencing (scRNA-seq) was used to identify prognosis-related cell types, and DNA methylation (DNAm)-based models were developed to predict outcomes based on their cellular characteristics. METHODS: We integrated scRNA-seq, bulk data, and clinical information to identify key cell populations associated with prognosis. The TCGA dataset was used for validation, and cell composition was inferred from DNAm data. Prognostic models were constructed based on cell-type-specific DNAm markers, and genomic features were compared across risk groups. Nomograms were created to assess treatment responses in different risk levels. RESULTS: Epithelial and T cells were major prognostic factors. Genomic analysis showed that epithelial cells in PDAC followed a malignant trajectory. DNAm data from TCGA confirmed the association of higher epithelial and T cell proportions with worse prognosis. Prognostic models based on DNAm markers of these cells effectively predicted patient survival, especially 5-year overall survival (AUC = 0.834). High-risk group with epithelial cell model showed altered pathways (tight junctions, NOTCH, and P53 signaling), while high-risk group with T cell model had changes in glycolysis, hypoxia, and NOTCH signaling, with more KRAS or TP53 mutations. Low-risk groups in the T cell model displayed stronger antitumor immune responses. Treatment predictions and nomograms were developed for clinical use. CONCLUSIONS: scRNA-seq and DNAm data integration enabled the creation of predictive models based on epithelial and T cell-specific methylation patterns, offering robust prognosis prediction for PDAC patients.