Abstract / Summary
Objective Pancreatic ductal adenocarcinoma (PDAC) exhibits profound molecular and metabolic heterogeneity associated with distinct phenotypic states. However, the contribution of transcription factor (TF)-driven regulatory programs to metabolic subtype stratification remains poorly understood. This study aims to determine whether TF-based classification can identify prognostically relevant glycolytic and lipogenic PDAC subtypes. Methods Bulk transcriptomic data from multiple PDAC cohorts and single-cell RNA sequencing data were integrated to characterize TF-associated metabolic heterogeneity. Metabolic signatures and TFs were quantified using single-sample gene set enrichment analysis (ssGSEA) and AddModule Score (AMS). Differential expression, enrichment analysis, integrative bulk and single-cell transcriptomic analysis, and deep neural network-based metabolic flux modeling were performed to investigate metabolic programs and functional differences between TF-defined subtypes. Additionally, RNA interference, RT-qPCR, lipid droplets staining, lactate assay, and transwell assays were used to evaluate the roles of TP63 in metabolic regulation. Results Glycolytic and lipogenic TF-defined metabolic subtypes were identified across multiple independent PDAC cohorts. These subtypes exhibited distinct metabolic programs, differentiation states, and prognostic outcomes. The glycolytic TF-defined subtype was associated with worse prognosis and differentiation, whereas the lipogenic TF-defined subtype exhibited more favorable clinical features. Integration of public bulk and single-cell transcriptomic analyses, together with deep neural network-based metabolic flux modeling, revealed consistent metabolic heterogeneity at both cellular and functional levels. Notably, TP63 was identified as a potential regulator of glycolytic-lipogenic metabolic reprogramming in PDAC. Conclusions TF-based classification delineates glycolytic and lipogenic subtypes in PDAC, underscoring the critical role of TF-regulated metabolic programs in patient stratification. These findings highlight the prognostic significance of glycolytic and lipogenic metabolic heterogeneity and support the development of metabolism-targeted therapeutic strategies.