ReviewFrontiers in oncology2026
Decoding lipid metabolic reprogramming in pancreatic ductal adenocarcinoma: from tumor-microenvironment crosstalk to therapeutic resistance.
Review in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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12 authors.
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Abstract
Pancreatic ductal adenocarcinoma (PDAC) is characterized by a nutrient-poor, hypoxic, and highly desmoplastic microenvironment that imposes substantial metabolic stress on malignant cells. Under these unfavorable conditions, lipid metabolism reprogramming provides cancer cells with the flexibility needed to maintain growth and survive therapeutic stress. Recent advances have uncovered new links between lipid metabolic reprogramming, the tumor microenvironment, and therapeutic resistance in PDAC, highlighting the need for an updated synthesis of this rapidly evolving field. In this review, we focus on the major tumor-intrinsic alterations in fatty acid and cholesterol-mevalonate metabolism, including changes in lipid synthesis, uptake, desaturation, storage, and oxidative utilization. These processes support membrane homeostasis, oncogenic signaling, and redox control, thereby linking lipid handling directly to tumor progression and treatment adaptation. We further review how these pathways are reshaped by the tumor microenvironment, where cancer-associated fibroblasts, immune cells, adipocytes, and neural-associated cells influence lipid availability, trafficking, and signaling. These reciprocal interactions can shape stromal plasticity, immune suppression, invasion, metastasis, and treatment response. We also evaluate current strategies targeting lipid metabolism and discuss why their clinical translation remains limited by metabolic compensation, tumor heterogeneity, drug specificity, and systemic toxicity. Overall, the available evidence suggests that lipid metabolism in PDAC is better understood as a dynamic, interconnected adaptive network rather than a set of isolated metabolic pathways. This perspective supports biomarker-guided combination strategies that target context-dependent lipid dependencies and treatment-induced metabolic adaptation, rather than relying on inhibition of a single pathway.
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