ReviewMedical molecular morphology2026
Lipid metabolic reprogramming in ovarian cancer: molecular mechanisms and therapeutic implications.
Review in Medical molecular morphology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Authors and funding
5 authors.
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Abstract
Lipids play diverse roles in cellular homeostasis, and dysregulation of lipid metabolism is implicated in a wide spectrum of diseases, including cancer. Ovarian cancer cells exhibit profound metabolic alterations that support autonomous proliferation under conditions of hypoxia and nutrient limitation. Although tumor angiogenesis occurs, insufficient and fragile neovasculature often limits nutrient supply, necessitating metabolic adaptation. Among these adaptations, lipid metabolic reprogramming has emerged as a critical feature that supports tumor growth, survival, invasion, and therapeutic resistance. Alterations in fatty acid synthesis, β-oxidation, lipid uptake, and inflammatory lipid metabolism collectively contribute to the malignant phenotype. This review summarizes current knowledge of lipid metabolic regulation in ovarian cancer, with particular emphasis on key enzymes and their regulatory mechanism and discusses potential implications for therapeutic targeting from a molecular and morphological perspective.
Indexed as
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42633601What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.