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Updated: Aug 28, 2026

Utilizing 18F-FDG PET/CT Imaging and Quantitative Histology to Measure Dynamic Changes in the Glucose Metabolism in Mouse Models of Lung Cancer
Published on: July 21, 2018
Metabolism in Tumor: Mechanisms and Therapeutic Perspectives
Jia-Chen Cheng1,2,3,4,5,6, Yi-Xuan Hu1,2,3,4,5,6, Xiang-Chun Huang1,2,3,4,5,6
1Department of Obstetrics and Gynecology Department of Gynecologic Oncology Research Office Guangzhou Medical University Guangzhou China.
Abstract:
Tumor metabolic reprogramming now extends far beyond the Warburg effect, spanning glycolysis, mitochondrial oxidative phosphorylation (OXPHOS), de novo lipogenesis, cholesterol biosynthesis, and fatty acid oxidation. This network is shaped by cell-intrinsic regulatory layers-transcriptional programs, histone lactylation and other posttranslational modifications, and m6A RNA modification-and by metabolic crosstalk with tumor-associated macrophages (TAMs) and cancer-associated fibroblasts (CAFs) that drives immune evasion and drug resistance. How these diverse inputs converge remains undefined: no integrated framework connects regulatory layers to signaling networks such as PI3K-AKT-mTOR, HIF-1α, and cGAS-STING, or explains how their convergence generates metabolic plasticity. This review organizes tumor metabolic reprogramming into functional modules centered on three convergence hubs: the SREBP1/2-FASN-SCD1 lipogenic axis, the HIF-1α-GLUT1-LDHA glycolytic axis, and the LDLR-SCARB1-LXR cholesterol homeostasis system. The discussion traces how multilayered regulation sustains each hub, how metabolic competition between tumor cells and immune effectors creates immunometabolic checkpoints, and how compensatory rewiring undermines single-agent inhibitors. Therapeutic strategies span rational combination regimens that preempt compensatory rewiring to nanomedicine platforms enabling spatially controlled metabolism-immunity reprogramming. By identifying the hubs upon which diverse oncogenic signals converge, this framework reveals context-specific metabolic vulnerabilities and outlines priorities for biomarker-driven patient stratification and hub-targeted combination therapy.
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