{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:17:53Z","timestamp":1787876273447,"version":"build-2784847793"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,8,28]],"date-time":"2026-08-28T00:00:00Z","timestamp":1787875200000},"content-version":"vor","delay-in-days":73,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"name":"\u5e7f\u897f\u81ea\u7136\u79d1\u5b66\u57fa\u91d1","award":["2024JJA140662"],"award-info":[{"award-number":["2024JJA140662"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Discov Onc"],"DOI":"10.1007\/s12672-026-05443-w","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T17:13:53Z","timestamp":1781630033000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Construction of a diagnostic model for nasopharyngeal carcinoma using a consensus machine learning approach and study of immune infiltration characteristics"],"prefix":"10.1007","volume":"17","author":[{"given":"Yaozhuang","family":"Zhou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuanyuan","family":"Gu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunhua","family":"Xie","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yucong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maosheng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liyun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,16]]},"reference":[{"issue":"10192","key":"5443_CR1","doi-asserted-by":"publisher","first-page":"64","DOI":"10.1016\/S0140-6736(19)30956-0","volume":"394","author":"YP Chen","year":"2019","unstructured":"Chen YP, Chan ATC, Le QT, et al. Nasopharyngeal carcinoma. Lancet. 2019;394(10192):64\u201380. https:\/\/doi.org\/10.1016\/S0140-6736(19)30956-0.","journal-title":"Lancet"},{"key":"5443_CR2","doi-asserted-by":"publisher","unstructured":"Kang M. Guidelines for radiotherapy of nasopharyngeal carcinoma in China (2022 Edition). Chin J Cancer Prev Treat. 2022;29(9):611\u201322. https:\/\/doi.org\/10.16073\/j.cnki.cjcpt.2022.09.01","DOI":"10.16073\/j.cnki.cjcpt.2022.09.01"},{"issue":"2","key":"5443_CR3","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1016\/j.anorl.2010.10.003","volume":"128","author":"Y Bensouda","year":"2011","unstructured":"Bensouda Y, Kaikani W, Ahbeddou N, Rahhali R, Jabri M, Mrabti H, et al. Treatment for metastatic nasopharyngeal carcinoma. Eur Ann Otorhinolaryngol Head Neck Dis. 2011;128(2):79\u201385. https:\/\/doi.org\/10.1016\/j.anorl.2010.10.003.","journal-title":"Eur Ann Otorhinolaryngol Head Neck Dis"},{"key":"5443_CR4","doi-asserted-by":"publisher","first-page":"192","DOI":"10.1186\/s12943-022-01659-w","volume":"21","author":"Y Mo","year":"2022","unstructured":"Mo Y, Wang Y, Wang Y, Deng X, Chen S, Liu K, et al. Circular RNA circPVT1 promotes nasopharyngeal carcinoma metastasis via the \u03b2-TrCP\/c-Myc\/SRSF1 positive feedback loop. Mol Cancer. 2022;21:192. https:\/\/doi.org\/10.1186\/s12943-022-01659-w.","journal-title":"Mol Cancer"},{"issue":"9","key":"5443_CR5","doi-asserted-by":"publisher","first-page":"1915","DOI":"10.3390\/v15091915","volume":"15","author":"T Ding","year":"2023","unstructured":"Ding T, Zhang Y, Ren Z, et al. EBV-associated hub genes as potential biomarkers for predicting the prognosis of nasopharyngeal carcinoma. Viruses. 2023;15(9):1915. https:\/\/doi.org\/10.3390\/v15091915.","journal-title":"Viruses"},{"key":"5443_CR6","doi-asserted-by":"publisher","unstructured":"Shan JJ, Zhao J, Liu S, et al. Screening of potential therapeutic targets and small-molecule drugs for cholangiocarcinoma related to ferroptosis mechanism based on bioinformatics. Drug Eval Res. 2023;46(9):1863\u201371. (In Chinese with English abstract) https:\/\/doi.org\/10.7501\/j.issn.1674-6376.2023.09.005.","DOI":"10.7501\/j.issn.1674-6376.2023.09.005"},{"issue":"1","key":"5443_CR7","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12935-025-04133-x","volume":"26","author":"D He","year":"2026","unstructured":"He D, Xiao Z, Li Y, et al. Integrating machine learning and multi-omics analysis to explore Treg-associated programmed cell death features in clear cell renal cell carcinoma. Cancer Cell Int. 2026;26(1):1. https:\/\/doi.org\/10.1186\/s12935-025-04133-x.","journal-title":"Cancer Cell Int"},{"issue":"1","key":"5443_CR8","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1186\/s13062-025-00650-x","volume":"20","author":"H Dai","year":"2025","unstructured":"Dai H, Yu Z, Zhao Y, et al. Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic catastrophe heterogeneity in bladder cancer. Biol Direct. 2025;20(1):56. https:\/\/doi.org\/10.1186\/s13062-025-00650-x.","journal-title":"Biol Direct"},{"issue":"1","key":"5443_CR9","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s41065-025-00409-4","volume":"162","author":"D He","year":"2025","unstructured":"He D, Xiao Z, Yu Z, et al. ADME gene-driven prognostic model for bladder cancer: a breakthrough in predicting survival and personalized treatment. Hereditas. 2025;162(1):12. https:\/\/doi.org\/10.1186\/s41065-025-00409-4.","journal-title":"Hereditas"},{"issue":"1","key":"5443_CR10","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1186\/s13008-025-00172-y","volume":"20","author":"H Dai","year":"2025","unstructured":"Dai H, Wang X, Wu Q, et al. Integrating genetic crosstalk between atherosclerosis and lung adenocarcinoma to advance precision diagnosis and treatment. Cell Div. 2025;20(1):27. https:\/\/doi.org\/10.1186\/s13008-025-00172-y.","journal-title":"Cell Div"},{"issue":"1","key":"5443_CR11","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1186\/s12894-025-01914-4","volume":"25","author":"H Dai","year":"2025","unstructured":"Dai H, Zhao K, Zhao Y, et al. Machine learning model in multi-omics perspective demystifies the prognostic significance of crotonylation heterogeneity in clear cell renal cell carcinoma. BMC Urol. 2025;25(1):229. https:\/\/doi.org\/10.1186\/s12894-025-01914-4.","journal-title":"BMC Urol"},{"key":"5443_CR12","doi-asserted-by":"publisher","unstructured":"Xia W, Liu J, Chen R, et al. Molecular subtypes and prognostic signature rooted in disulfidptosis highlight tumor microenvironment in lung adenocarcinoma. Chin J Cancer Res. 2025;37(5):796\u2013820. https:\/\/doi.org\/10.21147\/j.issn.1000-9604.2025.05.11","DOI":"10.21147\/j.issn.1000-9604.2025.05.11"},{"issue":"1","key":"5443_CR13","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1038\/s41746-025-02172-2","volume":"8","author":"G Zhang","year":"2025","unstructured":"Zhang G, Ma D, Yu L, et al. Machine learning identifies TIME subtypes linking EGFR mutations and immune states in lung adenocarcinoma. NPJ Digit Med. 2025;8(1):45. https:\/\/doi.org\/10.1038\/s41746-025-02172-2.","journal-title":"NPJ Digit Med"},{"issue":"7","key":"5443_CR14","doi-asserted-by":"publisher","first-page":"1888","DOI":"10.1016\/j.cell.2019.05.031","volume":"177","author":"T Stuart","year":"2019","unstructured":"Stuart T, Butler A, Hoffman P, et al. Comprehensive integration of single-cell data. Cell. 2019;177(7):1888-1902.e21. https:\/\/doi.org\/10.1016\/j.cell.2019.05.031.","journal-title":"Cell"},{"issue":"2","key":"5443_CR15","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1038\/s41590-018-0276-y","volume":"20","author":"D Aran","year":"2019","unstructured":"Aran D, Looney AP, Liu L, et al. Reference-based analysis of lung single-cell sequencing reveals a transitional profibrotic macrophage. Nat Immunol. 2019;20(2):163\u201372. https:\/\/doi.org\/10.1038\/s41590-018-0276-y.","journal-title":"Nat Immunol"},{"issue":"D1","key":"5443_CR16","doi-asserted-by":"publisher","first-page":"D870","DOI":"10.1093\/nar\/gkac947","volume":"51","author":"C Hu","year":"2023","unstructured":"Hu C, Li T, Xu Y, Li F, Li X. CellMarker 2.0: an updated database of manually curated cell markers in human\/mouse and web tools based on scRNA-seq data. Nucleic Acids Res. 2023;51(D1):D870\u20136. https:\/\/doi.org\/10.1093\/nar\/gkac947.","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"5443_CR17","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1093\/nar\/28.1.27","volume":"28","author":"M Kanehisa","year":"2000","unstructured":"Kanehisa M, Goto S. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27\u201330. https:\/\/doi.org\/10.1093\/nar\/28.1.27.","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"5443_CR18","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1038\/s41467-021-21246-9","volume":"12","author":"S Jin","year":"2021","unstructured":"Jin S, Guerrero-Juarez CF, Zhang L, et al. Inference and analysis of cell-cell communication using cell chat. Nat Commun. 2021;12(1):1088. https:\/\/doi.org\/10.1038\/s41467-021-21246-9.","journal-title":"Nat Commun"},{"issue":"7","key":"5443_CR19","doi-asserted-by":"publisher","DOI":"10.1093\/nar\/gkv007","volume":"43","author":"ME Ritchie","year":"2015","unstructured":"Ritchie ME, Phipson B, Wu D, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43(7):e47. https:\/\/doi.org\/10.1093\/nar\/gkv007.","journal-title":"Nucleic Acids Res"},{"issue":"1","key":"5443_CR20","doi-asserted-by":"publisher","first-page":"559","DOI":"10.1186\/1471-2105-9-559","volume":"9","author":"P Langfelder","year":"2008","unstructured":"Langfelder P, Horvath S. WGCNA: An R package for weighted correlation network analysis. BMC Bioinform. 2008;9(1):559. https:\/\/doi.org\/10.1186\/1471-2105-9-559.","journal-title":"BMC Bioinform"},{"key":"5443_CR21","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.neunet.2018.12.010","volume":"111","author":"M Fern\u00e1ndez-Delgado","year":"2019","unstructured":"Fern\u00e1ndez-Delgado M, Sirsat MS, Cernadas E, et al. An extensive experimental survey of regression methods. Neural Netw. 2019;111:11\u201334. https:\/\/doi.org\/10.1016\/j.neunet.2018.12.010.","journal-title":"Neural Netw"},{"key":"5443_CR22","doi-asserted-by":"publisher","unstructured":"Huang S, Cai N, Pacheco PP, et al. Applications of support vector machine (SVM) learning in cancer genomics. Cancer Genom Proteomics. 2018;15(1):41\u201351. https:\/\/doi.org\/10.21873\/cgp.20063","DOI":"10.21873\/cgp.20063"},{"issue":"11","key":"5443_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18637\/jss.v036.i11","volume":"36","author":"MB Kursa","year":"2010","unstructured":"Kursa MB, Rudnicki WR. Feature selection with the Boruta package. J Stat Softw. 2010;36(11):1\u201313. https:\/\/doi.org\/10.18637\/jss.v036.i11.","journal-title":"J Stat Softw"},{"key":"5443_CR24","doi-asserted-by":"publisher","first-page":"117954682211336","DOI":"10.1177\/11795468221133611","volume":"16","author":"A Moore","year":"2022","unstructured":"Moore A, Bell M. XGBoost, a novel explainable AI technique, in the prediction of myocardial infarction: A UK Biobank cohort study. Clin Med Insights Cardiol. 2022;16:11795468221133612. https:\/\/doi.org\/10.1177\/11795468221133611.","journal-title":"Clin Med Insights Cardiol"},{"issue":"4","key":"5443_CR25","doi-asserted-by":"publisher","first-page":"1793","DOI":"10.1016\/j.jtcvs.2017.12.107","volume":"155","author":"SY Park","year":"2018","unstructured":"Park SY. Nomogram: an analogue tool to deliver digital knowledge. J Thorac Cardiovasc Surg. 2018;155(4):1793\u20134. https:\/\/doi.org\/10.1016\/j.jtcvs.2017.12.107.","journal-title":"J Thorac Cardiovasc Surg"},{"issue":"1","key":"5443_CR26","doi-asserted-by":"publisher","first-page":"18","DOI":"10.1186\/s41512-019-0064-7","volume":"3","author":"AJ Vickers","year":"2019","unstructured":"Vickers AJ, van Calster B, Steyerberg EW. A simple, step-by-step guide to interpreting decision curve analysis. Diagn Progn Res. 2019;3(1):18. https:\/\/doi.org\/10.1186\/s41512-019-0064-7.","journal-title":"Diagn Progn Res"},{"key":"5443_CR27","unstructured":"Chang W, Cheng J, Allaire JJ, et al. Shiny: web application framework for R. R package version 1.7.4. 2025."},{"issue":"5","key":"5443_CR28","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1038\/nmeth.3337","volume":"12","author":"AM Newman","year":"2015","unstructured":"Newman AM, Liu CL, Green MR, et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods. 2015;12(5):453\u20137. https:\/\/doi.org\/10.1038\/nmeth.3337.","journal-title":"Nat Methods"},{"issue":"1","key":"5443_CR29","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/1471-2105-14-7","volume":"14","author":"S H\u00e4nzelmann","year":"2013","unstructured":"H\u00e4nzelmann S, Castelo R, Guinney J. GSVA: Gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013;14(1):7. https:\/\/doi.org\/10.1186\/1471-2105-14-7.","journal-title":"BMC Bioinformatics"},{"issue":"12","key":"5443_CR30","doi-asserted-by":"publisher","first-page":"1739","DOI":"10.1093\/bioinformatics\/btr260","volume":"27","author":"A Liberzon","year":"2011","unstructured":"Liberzon A, Subramanian A, Pinchback R, et al. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27(12):1739\u201340. https:\/\/doi.org\/10.1093\/bioinformatics\/btr260.","journal-title":"Bioinformatics"},{"issue":"5795","key":"5443_CR31","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.1126\/science.1132939","volume":"313","author":"J Lamb","year":"2006","unstructured":"Lamb J, Crawford ED, Peck D, et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313(5795):1929\u201335. https:\/\/doi.org\/10.1126\/science.1132939.","journal-title":"Science"},{"key":"5443_CR32","doi-asserted-by":"publisher","unstructured":"D'Agostino A, Cuzzocrea D, Lappano R, et al. Parthenolide generates reactive oxygen species and autophagy in MDA-MB231 cells. A soluble parthenolide analogue inhibits tumour growth and metastasis in a xenograft model of breast cancer. Cell Death Dis. 2013;4(10):e890. https:\/\/doi.org\/10.1038\/cddis.2013.415","DOI":"10.1038\/cddis.2013.415"},{"key":"5443_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.rechem.2025.102059","volume":"13","author":"Y Wang","year":"2025","unstructured":"Wang Y, Li J, Zhang H. Sulforaphane in alternative cancer chemotherapy: from carcinogenesis suppression to drug resistance reversal. Results Chem. 2025;13:102059. https:\/\/doi.org\/10.1016\/j.rechem.2025.102059.","journal-title":"Results Chem"},{"issue":"11","key":"5443_CR34","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1038\/s41568-020-0285-7","volume":"20","author":"D Bruni","year":"2020","unstructured":"Bruni D, Angell HK, Galon J. The immune contexture and immunoscore in cancer prognosis and therapeutic efficacy. Nat Rev Cancer. 2020;20(11):662\u201380. https:\/\/doi.org\/10.1038\/s41568-020-0285-7.","journal-title":"Nat Rev Cancer"},{"issue":"1","key":"5443_CR35","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1093\/cei\/uxac029","volume":"209","author":"F Flores-Borja","year":"2022","unstructured":"Flores-Borja F, Blair P. Mechanisms of induction of regulatory B cells in the tumour microenvironment and their contribution to immunosuppression and pro-tumour responses. Clin Exp Immunol. 2022;209(1):33\u201343. https:\/\/doi.org\/10.1093\/cei\/uxac029.","journal-title":"Clin Exp Immunol"},{"issue":"6","key":"5443_CR36","doi-asserted-by":"publisher","first-page":"750","DOI":"10.1038\/cmi.2014.129","volume":"12","author":"BP Miao","year":"2015","unstructured":"Miao BP, Zhang RS, Li M, et al. Nasopharyngeal cancer-derived microRNA-21 promotes immune suppressive B cells. Cell Mol Immunol. 2015;12(6):750\u20136. https:\/\/doi.org\/10.1038\/cmi.2014.129.","journal-title":"Cell Mol Immunol"},{"issue":"9","key":"5443_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.gim.2025.101514","volume":"27","author":"RJLM Lemmers","year":"2025","unstructured":"Lemmers RJLM, de Vries B, Baas F, et al. COL4A1 and COL4A2-related disorders: clinical features, diagnostic guidelines, and management. Genet Med. 2025;27(9):101514. https:\/\/doi.org\/10.1016\/j.gim.2025.101514.","journal-title":"Genet Med"},{"issue":"5930","key":"5443_CR38","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1126\/science.1160809","volume":"324","author":"MG Vander Heiden","year":"2009","unstructured":"Vander Heiden MG, Cantley LC, Thompson CB. Understanding the Warburg effect: the metabolic requirements of cell proliferation. Science. 2009;324(5930):1029\u201333. https:\/\/doi.org\/10.1126\/science.1160809.","journal-title":"Science"},{"issue":"2","key":"5443_CR39","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1038\/nrgastro.2017.119","volume":"15","author":"W Jia","year":"2018","unstructured":"Jia W, Xie G, Jia W. Bile acid-microbiota crosstalk in gastrointestinal inflammation and carcinogenesis. Nat Rev Gastroenterol Hepatol. 2018;15(2):111\u201328. https:\/\/doi.org\/10.1038\/nrgastro.2017.119.","journal-title":"Nat Rev Gastroenterol Hepatol"},{"issue":"4","key":"5443_CR40","doi-asserted-by":"publisher","first-page":"948","DOI":"10.1124\/pr.113.008201","volume":"66","author":"T Li","year":"2014","unstructured":"Li T, Chiang JY. Bile acid signaling in metabolic disease and drug therapy. Pharmacol Rev. 2014;66(4):948\u201384. https:\/\/doi.org\/10.1124\/pr.113.008201.","journal-title":"Pharmacol Rev"},{"issue":"6","key":"5443_CR41","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbcan.2023.189023","volume":"1878","author":"J Li","year":"2023","unstructured":"Li J, Wang H, Chen L. Metabolic reprogramming in nasopharyngeal carcinoma: mechanisms and therapeutic opportunities. Biochim Biophys Acta Rev Cancer. 2023;1878(6):189023. https:\/\/doi.org\/10.1016\/j.bbcan.2023.189023.","journal-title":"Biochim Biophys Acta Rev Cancer"},{"issue":"17","key":"5443_CR42","doi-asserted-by":"publisher","first-page":"3074","DOI":"10.1158\/0008-5472.CAN-21-3748","volume":"82","author":"T Liu","year":"2022","unstructured":"Liu T, Gan H, He S, et al. RNA helicase DDX24 stabilizes LAMB1 to promote hepatocellular carcinoma progression. Cancer Res. 2022;82(17):3074\u201387. https:\/\/doi.org\/10.1158\/0008-5472.CAN-21-3748.","journal-title":"Cancer Res"},{"issue":"23","key":"5443_CR43","doi-asserted-by":"publisher","first-page":"2402509","DOI":"10.1002\/advs.202402509","volume":"11","author":"B Pang","year":"2024","unstructured":"Pang B, Wang Q, Chen H, et al. Proteomic identification of small extracellular vesicle proteins LAMB1 and histone H4 for prostate cancer diagnosis and risk stratification. Adv Sci. 2024;11(23):2402509. https:\/\/doi.org\/10.1002\/advs.202402509.","journal-title":"Adv Sci"},{"issue":"5","key":"5443_CR44","doi-asserted-by":"publisher","first-page":"637","DOI":"10.1093\/neuonc\/not300","volume":"16","author":"Q Chen","year":"2014","unstructured":"Chen Q, Lu G, Cai Y, et al. MiR-124-5p inhibits the growth of high-grade gliomas through posttranscriptional regulation of LAMB1. Neuro-Oncol. 2014;16(5):637\u201351. https:\/\/doi.org\/10.1093\/neuonc\/not300.","journal-title":"Neuro-Oncol"},{"issue":"3","key":"5443_CR45","doi-asserted-by":"publisher","first-page":"174","DOI":"10.1038\/s41568-019-0238-1","volume":"20","author":"E Sahai","year":"2020","unstructured":"Sahai E, Astsaturov I, Cukierman E, et al. A framework for advancing our understanding of cancer-associated fibroblasts. Nat Rev Cancer. 2020;20(3):174\u201386. https:\/\/doi.org\/10.1038\/s41568-019-0238-1.","journal-title":"Nat Rev Cancer"},{"issue":"19","key":"5443_CR46","doi-asserted-by":"publisher","first-page":"28697","DOI":"10.18632\/oncotarget.7376","volume":"7","author":"SY Lim","year":"2016","unstructured":"Lim SY, Yuzhalin AE, Gordon-Weeks AN, et al. Targeting the CCL2-CCR2 signaling axis in cancer metastasis. Oncotarget. 2016;7(19):28697\u2013710. https:\/\/doi.org\/10.18632\/oncotarget.7376.","journal-title":"Oncotarget"},{"issue":"3","key":"5443_CR47","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1016\/j.ccr.2012.01.008","volume":"21","author":"JJ Engelhardt","year":"2012","unstructured":"Engelhardt JJ, Boldajipour B, Beemiller P, et al. Marginating dendritic cells of the tumor microenvironment cross-present tumor antigens and stably engage tumor-specific T cells. Cancer Cell. 2012;21(3):402\u201317. https:\/\/doi.org\/10.1016\/j.ccr.2012.01.008.","journal-title":"Cancer Cell"},{"key":"5443_CR48","doi-asserted-by":"publisher","unstructured":"Zhang P, Liang X, Ye B, et al. Metabolic reprogramming signature predicts immunotherapy efficacy in lung adenocarcinoma: targeting SLC25A1 to overcome immune resistance. Chin J Cancer Res. 2025;37(6):1000\u201319. https:\/\/doi.org\/10.21147\/j.issn.1000-9604.2025.06.11","DOI":"10.21147\/j.issn.1000-9604.2025.06.11"}],"container-title":["Discover Oncology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12672-026-05443-w","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12672-026-05443-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12672-026-05443-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,27]],"date-time":"2026-08-27T23:31:56Z","timestamp":1787873516000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12672-026-05443-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,16]]},"references-count":48,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["5443"],"URL":"https:\/\/doi.org\/10.1007\/s12672-026-05443-w","relation":{},"ISSN":["2730-6011"],"issn-type":[{"value":"2730-6011","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,16]]},"assertion":[{"value":"2 February 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 June 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"16 June 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This study is based on the analysis of publicly available datasets and does not involve any new experiments on human participants or animals by the authors. Therefore, ethical approval is not applicable to this manuscript.","order":1,"name":"Ethics","label":"Ethics approval and consent to participate","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","label":"Consent for publication","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","label":"Competing interests","group":{"name":"EthicsHeading","label":"Declarations"}}],"article-number":"1256"}}