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Life sciences · Journal article
Signal Transduction and Targeted Therapy · October 8, 2026
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Abstract Metabolic reprogramming is a core feature of tumor cells in adapting to microenvironmental stress and acquiring malignant phenotypes. It not only supports rapid proliferation but also mediates immune escape by reshaping the immune microenvironment. In recent years, the “metabolism-immune axis”, serving as a central hub for the crosstalk between metabolic regulation and immune suppression, has provided a new perspective for understanding tumor immune evasion. This review systematically discusses how metabolic pathways, including glucose, lipid, and amino acid metabolism, along with mitochondrial function and other related processes, shape the immunosuppressive tumor microenvironment by regulating immune cell function, immune checkpoint expression, and immune signaling networks, thereby promoting tumor immune escape. The concept of “metabolic immune checkpoints (MICs)” is innovatively proposed and classified into three categories based on their mechanisms of action: (1) MIC-I: metabolic modification-type immune checkpoints, exemplified by posttranslational modifications of immune checkpoint molecules such as PD-L1 lactylation, acetylation and glycosylation, etc.; (2) MIC-II: metabolic enzyme-dependent immune regulatory nodes, represented by enzymes such as indoleamine 2,3-dioxygenase 1 and others that directly influence immune cell function; and (3) MIC-III: microbial metabolite-coupled immune regulatory targets, including receptors for microbe-derived metabolites such as short-chain fatty acid receptors. By integrating molecular interaction networks of key targets such as lactate dehydrogenase A and ATP citrate lyase, this review explores synergistic therapeutic strategies that combine small-molecule inhibitors targeting MICs with immune checkpoint blockade. This highlights how metabolic intervention can restore immune cell function and reverse exhaustion. Current key challenges in clinical translation include limitations in metabolic dynamic monitoring technologies, lack of patient stratification biomarkers and insufficient specificity in microbiome regulation. Future directions will focus on personalized treatment guided by multiomics, integrating single-cell metabolic flux analysis, deep learning-driven target prediction, and synthetic microbiome engineering, driving tumor immunotherapy into the era of precision metabolic regulation. By integrating the fundamental mechanisms of MICs with translational medical research, this review not only provides a systematic paradigm but also establishes a foundation for the development of new-generation precision combination therapies.