{"doi":"10.1016/j.bbcan.2025.189425","title":"Elucidating cellular origins and TME dynamic evolution in NSCLC through multi-omics technologies","abstract":"Non-small cell lung cancer (NSCLC) is a leading cause of cancer mortality. Despite progress in targeted therapies and immunotherapy, resistance driven by tumor heterogeneity and dynamic tumor microenvironment (TME) remodeling persists. Multi-omics (single-cell/spatial transcriptomics) reveals lung adenocarcinoma (LUAD) origins in alveolar type 2 (AT2) cells and lineage plasticity via SOX2/WNT/YAP pathways driving aggressive subtypes. The TME, a dynamic ecosystem of immune cells and fibroblasts, evolves through immune-editing phases and cancer-associated fibroblasts (CAF)/tumor-associated macrophage (TAM) crosstalk to foster immunosuppression. Multi-omics identifies key immune subsets (CXCL13<sup>+</sup>CD8<sup>+</sup>T cells, M1/M2 macrophages) and antigen-presenting cancer-associated fibroblasts (apCAFs) as therapeutic targets. Emerging strategies targeting lineage plasticity, TME reprogramming, and microbiome modulation may overcome immune checkpoint blockade (ICB)/tyrosine kinase inhibitor (TKI) resistance. Challenges in spatiotemporal heterogeneity resolution call for artificial intelligence (AI)-driven TME modeling to guide precision interventions. This review highlights multi-omics in bridging NSCLC evolution with clinical translation for personalized therapies.","journal":"Biochimica et Biophysica Acta (BBA) - Reviews on Cancer","year":2025,"id":328,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0661,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-10-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":3069,"name":"Li Zhang","orcid":"0000-0002-3617-2627","position":1,"is_corresponding":false},{"id":3070,"name":"Chao Ning","orcid":"0000-0002-8848-3961","position":2,"is_corresponding":false},{"id":3068,"name":"Ning Ning Chao","orcid":null,"position":0,"is_corresponding":true}],"reference_count":202,"raw_metadata":null,"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}