{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:23:25Z","timestamp":1787027005874,"version":"build-2736575974"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T00:00:00Z","timestamp":1762819200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study systematically investigates radiotherapy-induced metabolic remodeling across the TME, encompassing tumor cells, immune cells, and tumor-draining lymph nodes (TDLNs), and establishes a prognostic signature based on radioresistance-related metabolic genes (RRMGs) to optimize therapeutic stratification and radiosensitizer discovery.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Bulk transcriptomic datasets of NSCLC tumor cells and tumor-draining TDLNs were systematically integrated, along with single-cell RNA-seq data from tumor tissues, to reconstruct metabolic flux maps using the single-cell Flux Estimation Analysis (scFEA) algorithm. WGCNA and Cox regression modeling of TCGA radiotherapy cohort were used to identify core RRMGs. A prognostic nomogram was developed using risk scores derived from these genes, while CIBERSORT and TIDE algorithms were used to evaluated TIME features and immunotherapy responses. Candidate radiosensitizing agents were predicted via the oncoPredict platform and validated by molecular docking, qRT-PCR and western blotting in radioresistant NSCLC cells.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Radiotherapy induced profound metabolic heterogeneity across the NSCLC TIME: Tumor cells and draining TDLNs exhibited suppressed tricarboxylic acid (TCA) cycle activity and N-glycan biosynthesis, while immune cells displayed upregulated serine metabolism alongside divergent shifts in lymphoid subsets. Seven RRMGs were identified as key prognostic determinants, including\n                      <jats:italic>PGD, IDH2, G6PD, ALDH3A1, UPP1, XYLT2, AACS<\/jats:italic>\n                      . The RRMG-based risk model robustly predicted poor overall survival (\n                      <jats:italic>HR<\/jats:italic>\n                      = 4.726, 95%\n                      <jats:italic>CI<\/jats:italic>\n                      : 2.154-10.371;\n                      <jats:italic>P<\/jats:italic>\n                      &amp;lt;0.001), with high predictive accuracy (AUC for 1-, 3-, and 5-year: 0.752, 0.778, and 0.879). High-risk patients demonstrated an immunosuppressive TIME marked by elevated tumor-promoting immune cell infiltration and TIDE scores. The model\u2019s generalizability was verified in an independent radioimmunotherapy cohort (AUC: 0.618). Experimental validation revealed significant upregulation of high-risk RRMGs in radioresistant NSCLC cells. Ouabain and two novel compounds (BRD-K28456706, BRD-K42260513) were nominated as promising radiosensitizers.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Radiotherapy-induced metabolic reprogramming in TIME drives resistance of NSCLC. The RRMG signature predicts radioimmunotherapy outcomes for patient stratification. Identifying ouabain and novel compounds highlights targeting metabolic vulnerabilities as a translatable strategy to overcome resistance.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fonc.2025.1693277","type":"journal-article","created":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T05:10:52Z","timestamp":1762837852000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["A metabolic-radioimmune signature predicts therapy response and immune reprogramming in non-small cell lung cancer"],"prefix":"10.3389","volume":"15","author":[{"given":"Zihong","family":"Zhu","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, the Second Affiliated Hospital of Harbin Medical University","place":["Harbin, Heilongjiang, China"]},{"name":"Department of Radiation Oncology and Shandong Provincial Key Laboratory of Precision Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical 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Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences","place":["Jinan, Shandong, China"]},{"name":"School of Clinical Medicine, Shandong Second Medical University","place":["Weifang, Shandong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ran","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Shandong Provincial Key Laboratory of Precision Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences","place":["Jinan, Shandong, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dawei","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology and Shandong Provincial Key Laboratory of Precision Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical 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