{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T04:55:46Z","timestamp":1790916946188,"version":"4.1.0"},"reference-count":47,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T00:00:00Z","timestamp":1790899200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2026.1900370","type":"journal-article","created":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T04:36:12Z","timestamp":1790915772000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Transcription factor-driven metabolic stratification reveals glycolytic and lipogenic subtypes with distinct prognostic and functional features in pancreatic cancer"],"prefix":"10.3389","volume":"16","author":[{"given":"Jie","family":"Ke","sequence":"first","affiliation":[{"name":"Department of Central Laboratory, Shenzhen Hospital, Beijing University of Chinese Medicine","place":["Shenzhen, Guangdong, China"]},{"name":"Biotherapy Center, Shenzhen Third People\u2019s 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