Multi-omics profiling of intercellular immunometabolic heterogeneity highlights in lung cancer: Crosstalk mechanisms and resistance in the tumor-immune interface
- Crit Rev Oncol Hematol. 2026 Mar:219:105094. doi: 10.1016/j.critrevonc.2025.105094.
- 1. Department of Cardiothoracic Surgery, Jiangyin Clinical College of Xuzhou Medical University, Jiangyin, Jiangsu 214400, China.
- 2. School of Medical Imaging, Xuzhou Medical University, Xuzhou 221006, China.
- 3. Wuxi Key Laboratory of Biomaterials for Clinical Application, Department of Central Laboratory, Jiangyin Clinical College of Xuzhou Medical University, Wuxi 214400, China.
- 4. Department of Cardiothoracic Surgery, The first clinical college of Xuzhou Medical University, Jiangsu, China.
- 5. Department of Neurosurgery, The Second Affiliated Hospital of Kunming Medical University, China.
- 6. Department of Cardiothoracic Surgery, The Affiliated Jiangyin Hospital of Nantong University, Jiangsu, China.
- 7. Department of Cardiothoracic Surgery, Jiangyin Clinical College of Xuzhou Medical University, Jiangyin, Jiangsu 214400, China. Electronic address: [email protected].
Lung cancer's tumor microenvironment (TME) is shaped by metabolic crosstalk between malignant and immune cells, driving immune evasion, heterogeneity, and resistance to immunotherapy. Tumor-derived metabolites such as lactate, Adenosine, and kynurenine impair cytotoxic T cells and dendritic cells while promoting regulatory and suppressive immune subsets, creating a metabolically hostile niche that limits checkpoint inhibitor efficacy. Advances in multiomics including single-cell transcriptomics, proteomics, metabolomics, and spatial profiling have enabled high-resolution mapping of tumor-immune metabolic communication. Available evidence from various studies reveals metabolic subtypes, immune states, and spatial niches linked to resistance, including lactate accumulation, glutamine dependence, and Adenosine signaling. This review uniquely synthesizes findings from latest literature (2009-2025) obtained from electronic database including PubMed, Google Scholar, Scopus and Web of Science, which integrates multi-omics data to define immunometabolic phenotypes (LM-high, CD73^high, KEAP1/NRF2^mutation) and pathways in Lung Cancer and highlights therapeutic strategies such as CD73/Adenosine blockade, Arginase and Glutaminase inhibition, and metabolically engineered immune cells. Collectively, available evidence from various studies positions multi-omics profiling as a critical clinical tool. It enables the classification of Tumors by dominant immunometabolic phenotype, thereby paving the way for biomarker-driven trials that rationally combine metabolic inhibitors with immunotherapy to overcome resistance.