Transcription factor-driven metabolic stratification reveals glycolytic and lipogenic subtypes with distinct prognostic and functional features in pancreatic cancer
- 1Department of Central Laboratory, Shenzhen Hospital, Beijing University of Chinese Medicine , , ,
- 2Department of Human Cell Biology and Genetics, SUSTech Homeostatic Medicine Institute, School of Medicine, Southern University of Science and Technology , , ,
- 3Biotherapy Center, Shenzhen Third People’s Hospital , , ,
Abstract
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.
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BibTeX
@article{ke2026transcription,
title = {{Transcription factor-driven metabolic stratification reveals glycolytic and lipogenic subtypes with distinct prognostic and functional features in pancreatic cancer}},
author = {Jie Ke and Shuai Liu and Junming Xu and Fenfang Wu},
journal = {Frontiers in oncology},
year = {2026},
month = oct,
doi = {10.3389/fonc.2026.1900370},
url = {https://europepmc.org/article/PMC/PMC13672013},
note = {CC BY},
}
RIS
TY - JOUR
TI - Transcription factor-driven metabolic stratification reveals glycolytic and lipogenic subtypes with distinct prognostic and functional features in pancreatic cancer
AU - Jie Ke
AU - Shuai Liu
AU - Junming Xu
AU - Fenfang Wu
PY - 2026
DA - 2026/10/02
JO - Frontiers in oncology
DO - 10.3389/fonc.2026.1900370
UR - https://europepmc.org/article/PMC/PMC13672013
AB - 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.
SN - 2234-943X
ER -