مقالات پذیرفته شده کنگره

  • Spatial Metabolic Coupling as a Master Regulator of Tumor Metabolic Plasticity and Immune Checkpoint Response: A Multi-Omics Pan-Cancer Analysis

  • Arian kazemeiny,1,*
    1. Iran University of medical sciences/IAU Tehran Medical Branch


  • Introduction: Cancer metabolism has emerged as a fundamental hallmark of tumor biology, extending beyond the classical Warburg effect to encompass complex metabolic interactions among malignant cells, stromal components, and immune populations within the tumor microenvironment. Recent advances in spatial transcriptomics, metabolomics, and multi-omics technologies have revealed that metabolic reprogramming is spatially heterogeneous and dynamically regulated across distinct tumor niches. However, current studies predominantly investigate individual metabolic pathways or isolated cellular populations, while the spatial coordination of metabolic communication remains incompletely understood. This conceptual gap limits our understanding of how localized metabolic interactions influence tumor plasticity, immune checkpoint regulation, and therapeutic response. In this review, I propose Spatial Metabolic Coupling(SMC) as an integrative conceptual framework that synthesizes current evidence linking intercellular metabolite exchange, mitochondrial function, metabolic plasticity, and immune regulation within spatially organized tumor ecosystems. By integrating recent findings from spatial biology and multi-omics research, I propose that this framework provides a systems-level perspective for interpreting metabolic heterogeneity and may guide future studies aimed at advancing metabolism-based precision oncology.
  • Methods: A comprehensive narrative literature review was conducted to evaluate current evidence regarding metabolic interactions in cancer, with a particular focus on spatial metabolism, tumor metabolic plasticity, immunometabolism, and metabolism-dependent cell death. Electronic databases including PubMed, Scopus, Web of science, Google Scholar, ScienceDirect, SpringerLink, Nature Portfolio, Cell Press, Wiley Online Library, and PMC were systematically searched for relevant publications. The search included articles published from 2010 to June 2026, while landmark studies published before 2010 were incorporated when they provided essential mechanistic insights into cancer metabolism. Search terms included combinations of cancer metabolism, metabolic interactions, tumor microenvironment, spatial metabolism. spatial transcriptomics, metabolomics, multi-omics, immunometabolism, metabolic plasticity, cuproptosis, ferroptosis, and mitochondrial metabolism. Original research articles, systematic reviews, meta-analyses, and high-impact review papers published in peer-reviewed journals were considered. Studies were selected based on their scientific quality, methodological rigor, relevance to tumor metabolic communication, and contribution to understanding spatial metabolic organization within the tumor microenvironment. Data from the selected studies were synthesized qualitatively to identify common mechanisms, emerging concepts, unresolved challenges, and future research directions. Based on this comprehensive synthesis, the concept of Spatial Metabolic Coupling (SMC) was proposed as an integrative framework to explain coordinated metabolic interactions within heterogeneous tumor ecosystems.
  • Results: The reviewed literature consistently demonstrates that tumor metabolism is governed by dynamic interactions among malignant cells, stromal cells, immune populations, and the extracellular matrix rather than by isolated intracellular metabolic pathways. Recent advances in spatial transcriptomics, spatial metabolomics, and multi-omics technologies reveal that metabolic heterogeneity is spatially organized into distinct metabolic niches characterized by differential nutrient availability, mitochondrial activity, oxygen tension, and immune infiltration. Evidence further indicates that intercellular metabolite exchange-including lactate, amino acid, lipids, and mitochondrial metabolites-plays a central role in regulating metabolic plasticity, immune checkpoint activity, therapeutic resistance, and metabolism-dependent cell death, particularly cuproptosis. Collectively, the available evidence suggests that these metabolic processes function as interconnected components of a coordinated spatial network rather than independent biological events. Based on the synthesis of current evidence, this review proposes Spatial Metabolic Coupling (SMC) as an integrative conceptual framework for understanding how localized metabolic communication shapes tumor evolution, immune regulation, and therapeutic response across heterogeneous tumor ecosystems.
  • Conclusion: Cancer metabolism should be viewed as a dynamic and spatially organized network rather than a collection of isolated intracellular pathways. The evidence synthesized in this review highlights that metabolic communication among cancer cells, stromal components, and immune populations plays a pivotal role in regulating tumor plasticity, immune checkpoint response, therapeutic resistance, and metabolism-dependent cell death. Recent advances in spatial biology and multi-omics technologies provide unprecedented opportunities to investigate these interactions within their native tissue context. By integrating current evidence, this review proposes Spatial Metabolic Coupling(SMC) as a conceptual framework for understanding the coordinated metabolic interactions that shape tumor behavior across heterogeneous microenvironments. Although further experimental and clinical validation is required, this perspective may facilitate the identification of novel biomarkers, improve patient stratification, and support the development of metabolism-guided precision oncology. Future studies integrating spatial multi-omics, computational modeling, and functional validation will be essential to translate this concept into clinical applications.
  • Keywords: 1. Spatial metabolic coupling 2. Cuproptosis 3. Pancreatic ductal adenocarcinoma 4. Multi-omics

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