ReviewFrontiers in immunology2025
Metabolic reprogramming in the post-metastatic tumor microenvironment: multi-omics insights into determinants of immunotherapy response.
Review in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Guided immunotherapy for residual solid tumor: integrating platelets and CAR T cells to reduce post-surgical recurrence.Biomarker research · 2026Review
- Integrated hypoxia and metabolic gene signature to predict clinical outcomes and therapeutic vulnerabilities in lung adenocarcinoma.Journal of thoracic disease · 2026Article
- AI-driven big data analysis and predictive modeling of infectious disease immunity: from correlates to causal, multiscale understanding.Archives of microbiology · 2026Review
- Amino Acid Metabolic Enzymes in Gastric Cancer: Roles and Mechanisms in Tumorigenesis and Progression.Oncology research · 2026Review
- Editorial: Molecular pathways and signaling molecules in cancer therapy: advances and innovations.Frontiers in immunology · 2026Article
- Extrachromosomal DNA in Solid Tumors-Landscape, Immune Effects, and Resistance to Targeted Therapy.Oncology research · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Metabolic reprogramming has emerged as a central determinant of immune modulation in the post-metastatic tumor immune microenvironment (TIME). Alterations in glycolysis and lactate accumulation, lipid metabolic rewiring, metal-dependent cell death pathways such as ferroptosis and cuproptosis, and the tryptophan-IDO1-kynurenine axis collectively contribute to an immunosuppressive niche that drives tumor progression and therapeutic resistance. These metabolic shifts are not isolated events but are intricately connected with immune-regulatory networks, profoundly influencing the efficacy of immunotherapy. Advances in multi-omics technologies-including metabolomics, proteomics, single-cell sequencing, and spatial omics-have provided unprecedented resolution to decode these complex interactions, enabling the identification of predictive biomarkers, delineation of metabolic-immune signatures, and discovery of therapeutic vulnerabilities. Integrating these multi-layered datasets has paved the way for precision medicine strategies that tailor immunotherapy to patient-specific metabolic and immune contexts. Therapeutically, combining metabolic inhibitors with immune checkpoint blockade, exploiting ferroptosis or cuproptosis to enhance tumor immunogenicity, or modulating amino acid metabolism to reverse immune tolerance are promising strategies to overcome resistance and expand patient benefit. Looking forward, the integration of multi-omics-guided biomarkers, AI-driven analytics, and advanced delivery systems such as nanoparticles and engineered exosomes will accelerate the translation of these insights into clinical practice. Decoding the metabolism-immunity crosstalk through multi-omics not only advances our understanding of metastatic cancer biology but also paves the way for next-generation personalized and adaptive therapies that promise to enhance immunotherapy efficacy, prolong survival, and improve the quality of life for patients with advanced cancers.
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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 Socratic graph.