{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T05:04:19Z","timestamp":1789448659283,"version":"build-2803163510"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T00:00:00Z","timestamp":1789430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100017700","name":"Henan Province science and technology research project","doi-asserted-by":"crossref","award":["242102310318"],"award-info":[{"award-number":["242102310318"]}],"id":[{"id":"10.13039\/501100017700","id-type":"DOI","asserted-by":"crossref"}]},{"name":"National Postdoctoral Research Program by China Postdoctoral Science Foundation","award":["GZB20230670"],"award-info":[{"award-number":["GZB20230670"]}]},{"name":"the General Program of the China Postdoctoral Science Foundation","award":["2024M763009"],"award-info":[{"award-number":["2024M763009"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Hum Genomics"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Immune checkpoint blockade (ICB) efficacy in clear cell renal cell carcinoma (ccRCC) is limited by tumor microenvironment (TME) heterogeneity. Because traditional bulk-derived models lack spatial resolution, we developed an integrated framework connecting macroscopic survival risks to microscopic TME structures. We applied ten algorithms to establish multi-omics subtypes and evaluated 101 machine-learning combinations across three independent cohorts to generate a Consensus Machine Learning-driven Signature (CMLS). The signature\u2019s spatial and cellular origins were decoded using spatial transcriptomics (ST) and a 140,000-cell scRNA-seq atlas. Expression of key genes was experimentally validated via RT-qPCR in 17 paired ccRCC clinical tissues. We identified two molecular subtypes with distinct clinical and epigenetic profiles. SuperPC optimization yielded a 24-gene CMLS serving as an independent prognostic factor. scRNA-seq and ST deconvolution revealed these signals predominantly originate from cancer-associated fibroblasts (CAFs) and malignant epithelial cells, which collaborate to drive spatial immune exclusion. RT-qPCR confirmed significant overexpression of five core CMLS genes in ccRCC versus adjacent normal tissues. Low CMLS scores correlated with enhanced ICB responsiveness, whereas high-CMLS tumors demonstrated specific vulnerability to dasatinib and dabrafenib. The CMLS translates spatial immune-exclusion dynamics into a quantifiable metric, outperforming tumor mutational burden in predicting ICB benefits, providing a robust tool for patient stratification in ccRCC.<\/jats:p>","DOI":"10.1186\/s40246-026-01024-8","type":"journal-article","created":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T04:51:16Z","timestamp":1789447876000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine learning and multi-omics clustering to map cellular rewiring and immune evasion in ccRCC"],"prefix":"10.1186","volume":"20","author":[{"given":"Zhe","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"YingJian","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuechang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoyang","family":"Xv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Long","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dao","family":"Xin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,9,15]]},"reference":[{"issue":"1","key":"1024_CR1","first-page":"12","volume":"74","author":"RL Siegel","year":"2024","unstructured":"Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin. 2024;74(1):12\u201349.","journal-title":"CA Cancer J Clin"},{"key":"1024_CR2","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15123207","author":"V Schiavoni","year":"2023","unstructured":"Schiavoni V, Campagna R, Pozzi V, Cecati M, Milanese G, Sartini D, et al. Recent advances in the management of clear cell renal cell carcinoma: novel biomarkers and targeted therapies. Cancers (Basel). 2023. https:\/\/doi.org\/10.3390\/cancers15123207.","journal-title":"Cancers (Basel)"},{"issue":"4","key":"1024_CR3","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1056\/NEJMra1601333","volume":"376","author":"TK Choueiri","year":"2017","unstructured":"Choueiri TK, Motzer RJ. Systemic therapy for metastatic renal-cell carcinoma. N Engl J Med. 2017;376(4):354\u201366.","journal-title":"N Engl J Med"},{"issue":"4","key":"1024_CR4","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1038\/s41571-020-00455-z","volume":"18","author":"DA Braun","year":"2021","unstructured":"Braun DA, Bakouny Z, Hirsch L, Flippot R, Van Allen EM, Wu CJ, et al. Beyond conventional immune-checkpoint inhibition - novel immunotherapies for renal cell carcinoma. Nat Rev Clin Oncol. 2021;18(4):199\u2013214.","journal-title":"Nat Rev Clin Oncol"},{"issue":"10","key":"1024_CR5","doi-asserted-by":"publisher","first-page":"1349","DOI":"10.1158\/2159-8290.CD-19-0499","volume":"9","author":"L Vuong","year":"2019","unstructured":"Vuong L, Kotecha RR, Voss MH, Hakimi AA. Tumor microenvironment dynamics in clear-cell renal cell carcinoma. Cancer Discov. 2019;9(10):1349\u201357.","journal-title":"Cancer Discov"},{"issue":"5","key":"1024_CR6","doi-asserted-by":"publisher","first-page":"649","DOI":"10.1016\/j.ccell.2021.02.015","volume":"39","author":"K Bi","year":"2021","unstructured":"Bi K, He MX, Bakouny Z, Kanodia A, Napolitano S, Wu J, et al. Tumor and immune reprogramming during immunotherapy in advanced renal cell carcinoma. Cancer Cell. 2021;39(5):649-661.e645.","journal-title":"Cancer Cell"},{"issue":"6","key":"1024_CR7","doi-asserted-by":"publisher","first-page":"909","DOI":"10.1038\/s41591-020-0839-y","volume":"26","author":"DA Braun","year":"2020","unstructured":"Braun DA, Hou Y, Bakouny Z, Ficial M, Sant\u2019 Angelo M, Forman J, et al. Interplay of somatic alterations and immune infiltration modulates response to PD-1 blockade in advanced clear cell renal cell carcinoma. Nat Med. 2020;26(6):909\u201318.","journal-title":"Nat Med"},{"issue":"6","key":"1024_CR8","doi-asserted-by":"publisher","first-page":"803","DOI":"10.1016\/j.ccell.2020.10.011","volume":"38","author":"RJ Motzer","year":"2020","unstructured":"Motzer RJ, Banchereau R, Hamidi H, Powles T, McDermott D, Atkins MB, et al. Molecular subsets in renal cancer determine outcome to checkpoint and angiogenesis blockade. Cancer Cell. 2020;38(6):803-817.e804.","journal-title":"Cancer Cell"},{"key":"1024_CR9","doi-asserted-by":"publisher","DOI":"10.3389\/fmolb.2021.609865","volume":"8","author":"C Zhan","year":"2021","unstructured":"Zhan C, Wang Z, Xu C, Huang X, Su J, Chen B, et al. Development and validation of a prognostic gene signature in clear cell renal cell carcinoma. Front Mol Biosci. 2021;8:609865.","journal-title":"Front Mol Biosci"},{"issue":"12","key":"1024_CR10","doi-asserted-by":"publisher","first-page":"4310","DOI":"10.1002\/cam4.2983","volume":"9","author":"J Luo","year":"2020","unstructured":"Luo J, Xie Y, Zheng Y, Wang C, Qi F, Hu J, et al. Comprehensive insights on pivotal prognostic signature involved in clear cell renal cell carcinoma microenvironment using the ESTIMATE algorithm. Cancer Med. 2020;9(12):4310\u201323.","journal-title":"Cancer Med"},{"issue":"5","key":"1024_CR11","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1016\/j.hoc.2023.04.011","volume":"37","author":"NH Shirole","year":"2023","unstructured":"Shirole NH, Kaelin WG Jr. Von-Hippel Lindau and hypoxia-inducible factor at the center of renal cell carcinoma biology. Hematol Oncol Clin North Am. 2023;37(5):809\u201325.","journal-title":"Hematol Oncol Clin North Am"},{"issue":"9","key":"1024_CR12","doi-asserted-by":"publisher","first-page":"539","DOI":"10.1038\/s41585-019-0211-5","volume":"16","author":"WM Linehan","year":"2019","unstructured":"Linehan WM, Ricketts CJ. The Cancer Genome Atlas of renal cell carcinoma: findings and clinical implications. Nat Rev Urol. 2019;16(9):539\u201352.","journal-title":"Nat Rev Urol"},{"issue":"8","key":"1024_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s12276-018-0071-8","volume":"50","author":"B Hwang","year":"2018","unstructured":"Hwang B, Lee JH, Bang D. Single-cell RNA sequencing technologies and bioinformatics pipelines. Exp Mol Med. 2018;50(8):1\u201314.","journal-title":"Exp Mol Med"},{"issue":"1","key":"1024_CR14","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1038\/s41592-020-01033-y","volume":"18","author":"V Marx","year":"2021","unstructured":"Marx V. Method of the year: spatially resolved transcriptomics. Nat Methods. 2021;18(1):9\u201314.","journal-title":"Nat Methods"},{"key":"1024_CR15","doi-asserted-by":"publisher","DOI":"10.1038\/s41698-020-0122-1","volume":"4","author":"G Adam","year":"2020","unstructured":"Adam G, Ramp\u00e1\u0161ek L, Safikhani Z, Smirnov P, Haibe-Kains B, Goldenberg A. Machine learning approaches to drug response prediction: challenges and recent progress. NPJ Precis Oncol. 2020;4:19.","journal-title":"NPJ Precis Oncol"},{"key":"1024_CR16","doi-asserted-by":"crossref","unstructured":"Uhl\u00e9n M, Fagerberg L, Hallstr\u00f6m BM, Lindskog C, Oksvold P, Mardinoglu A, Sivertsson \u00c5, Kampf C, Sj\u00f6stedt E, Asplund A, et al: Proteomics. Tissue-based map of the human proteome. Science. 2015;347(6220):1260419.","DOI":"10.1126\/science.1260419"},{"issue":"4","key":"1024_CR17","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1016\/j.ejca.2011.11.036","volume":"48","author":"P Lambin","year":"2012","unstructured":"Lambin P, Rios-Velazquez E, Leijenaar R, Carvalho S, van Stiphout RG, Granton P, et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer. 2012;48(4):441\u20136.","journal-title":"Eur J Cancer"},{"issue":"10","key":"1024_CR18","doi-asserted-by":"publisher","first-page":"883","DOI":"10.1056\/NEJMoa1113205","volume":"366","author":"M Gerlinger","year":"2012","unstructured":"Gerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366(10):883\u201392.","journal-title":"N Engl J Med"},{"issue":"4","key":"1024_CR19","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1016\/j.cell.2019.10.007","volume":"179","author":"DJ Clark","year":"2019","unstructured":"Clark DJ, Dhanasekaran SM, Petralia F, Pan J, Song X, Hu Y, et al. Integrated proteogenomic characterization of clear cell renal cell Carcinoma. Cell. 2019;179(4):964-983.e931.","journal-title":"Cell"},{"issue":"6","key":"1024_CR20","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1016\/j.ccell.2021.04.014","volume":"39","author":"A Bagaev","year":"2021","unstructured":"Bagaev A, Kotlov N, Nomie K, Svekolkin V, Gafurov A, Isaeva O, et al. Conserved pan-cancer microenvironment subtypes predict response to immunotherapy. Cancer Cell. 2021;39(6):845-865.e847.","journal-title":"Cancer Cell"},{"key":"1024_CR21","doi-asserted-by":"crossref","unstructured":"The Cancer Genome Atlas Research Network. Comprehensive molecular characterization of clear cell renal cell carcinoma. Nature. 2013;499(7456):43\u201349","DOI":"10.1038\/nature12222"},{"issue":"4","key":"1024_CR22","doi-asserted-by":"publisher","first-page":"736","DOI":"10.1016\/j.cell.2017.04.016","volume":"169","author":"S Chevrier","year":"2017","unstructured":"Chevrier S, Levine JH, Zanotelli VRT, Silina K, Schulz D, Bacac M, et al. An immune atlas of clear cell renal cell carcinoma. Cell. 2017;169(4):736-749.e718.","journal-title":"Cell"},{"issue":"8","key":"1024_CR23","doi-asserted-by":"publisher","first-page":"3090","DOI":"10.1172\/JCI91095","volume":"127","author":"J Jeschke","year":"2017","unstructured":"Jeschke J, Bizet M, Desmedt C, Calonne E, Dedeurwaerder S, Garaud S, et al. DNA methylation-based immune response signature improves patient diagnosis in multiple cancers. J Clin Invest. 2017;127(8):3090\u2013102.","journal-title":"J Clin Invest"},{"issue":"1","key":"1024_CR24","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1186\/s13045-021-01105-2","volume":"14","author":"Y Lei","year":"2021","unstructured":"Lei Y, Tang R, Xu J, Wang W, Zhang B, Liu J, et al. Applications of single-cell sequencing in cancer research: progress and perspectives. J Hematol Oncol. 2021;14(1):91.","journal-title":"J Hematol Oncol"},{"issue":"5","key":"1024_CR25","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1016\/j.ccell.2021.03.007","volume":"39","author":"C Krishna","year":"2021","unstructured":"Krishna C, DiNatale RG, Kuo F, Srivastava RM, Vuong L, Chowell D, et al. Single-cell sequencing links multiregional immune landscapes and tissue-resident T cells in ccRCC to tumor topology and therapy efficacy. Cancer Cell. 2021;39(5):662-677.e666.","journal-title":"Cancer Cell"},{"issue":"1","key":"1024_CR26","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1152\/physrev.00048.2019","volume":"101","author":"G Biffi","year":"2021","unstructured":"Biffi G, Tuveson DA. Diversity and biology of cancer-associated fibroblasts. Physiol Rev. 2021;101(1):147\u201376.","journal-title":"Physiol Rev"},{"issue":"1","key":"1024_CR27","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1186\/s12943-021-01428-1","volume":"20","author":"X Mao","year":"2021","unstructured":"Mao X, Xu J, Wang W, Liang C, Hua J, Liu J, et al. Crosstalk between cancer-associated fibroblasts and immune cells in the tumor microenvironment: new findings and future perspectives. Mol Cancer. 2021;20(1):131.","journal-title":"Mol Cancer"},{"issue":"9","key":"1024_CR28","doi-asserted-by":"publisher","first-page":"1330","DOI":"10.1158\/2159-8290.CD-19-1384","volume":"10","author":"Y Kieffer","year":"2020","unstructured":"Kieffer Y, Hocine HR, Gentric G, Pelon F, Bernard C, Bourachot B, et al. Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer. Cancer Discov. 2020;10(9):1330\u201351.","journal-title":"Cancer Discov"},{"issue":"3","key":"1024_CR29","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, DeNardo DG, Egeblad M, Evans RM, et al. A framework for advancing our understanding of cancer-associated fibroblasts. Nat Rev Cancer. 2020;20(3):174\u201386.","journal-title":"Nat Rev Cancer"},{"issue":"5","key":"1024_CR30","doi-asserted-by":"publisher","first-page":"661","DOI":"10.1016\/j.annonc.2021.02.006","volume":"32","author":"DJ McGrail","year":"2021","unstructured":"McGrail DJ, Pili\u00e9 PG, Rashid NU, Voorwerk L, Slagter M, Kok M, et al. High tumor mutation burden fails to predict immune checkpoint blockade response across all cancer types. Ann Oncol. 2021;32(5):661\u201372.","journal-title":"Ann Oncol"},{"issue":"11","key":"1024_CR31","doi-asserted-by":"publisher","first-page":"1733","DOI":"10.1038\/s41591-020-1044-8","volume":"26","author":"RJ Motzer","year":"2020","unstructured":"Motzer RJ, Robbins PB, Powles T, Albiges L, Haanen JB, Larkin J, et al. Avelumab plus axitinib versus sunitinib in advanced renal cell carcinoma: biomarker analysis of the phase 3 JAVELIN Renal 101 trial. Nat Med. 2020;26(11):1733\u201341.","journal-title":"Nat Med"},{"issue":"4","key":"1024_CR32","doi-asserted-by":"publisher","first-page":"462","DOI":"10.1016\/j.ccell.2015.02.015","volume":"27","author":"B Ruffell","year":"2015","unstructured":"Ruffell B, Coussens LM. Macrophages and therapeutic resistance in cancer. Cancer Cell. 2015;27(4):462\u201372.","journal-title":"Cancer Cell"},{"issue":"1","key":"1024_CR33","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1016\/j.immuni.2019.12.011","volume":"52","author":"PS Hegde","year":"2020","unstructured":"Hegde PS, Chen DS. Top 10 challenges in cancer immunotherapy. Immunity. 2020;52(1):17\u201335.","journal-title":"Immunity"},{"issue":"6574","key":"1024_CR34","doi-asserted-by":"publisher","DOI":"10.1126\/science.abe6474","volume":"374","author":"L Zheng","year":"2021","unstructured":"Zheng L, Qin S, Si W, Wang A, Xing B, Gao R, et al. Pan-cancer single-cell landscape of tumor-infiltrating T cells. Science. 2021;374(6574):abe6474.","journal-title":"Science"},{"issue":"1","key":"1024_CR35","doi-asserted-by":"publisher","first-page":"35","DOI":"10.1016\/j.cell.2016.02.065","volume":"165","author":"W Hugo","year":"2016","unstructured":"Hugo W, Zaretsky JM, Sun L, Song C, Moreno BH, Hu-Lieskovan S, et al. Genomic and transcriptomic features of response to anti-PD-1 therapy in metastatic melanoma. Cell. 2016;165(1):35\u201344.","journal-title":"Cell"},{"issue":"1","key":"1024_CR36","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-12159-9","volume":"10","author":"H Jung","year":"2019","unstructured":"Jung H, Kim HS, Kim JY, Sun JM, Ahn JS, Ahn MJ, et al. DNA methylation loss promotes immune evasion of tumours with high mutation and copy number load. Nat Commun. 2019;10(1):4278.","journal-title":"Nat Commun"},{"issue":"21","key":"1024_CR37","doi-asserted-by":"publisher","first-page":"5309","DOI":"10.1016\/j.cell.2021.09.020","volume":"184","author":"G Morad","year":"2021","unstructured":"Morad G, Helmink BA, Sharma P, Wargo JA. Hallmarks of response, resistance, and toxicity to immune checkpoint blockade. Cell. 2021;184(21):5309\u201337.","journal-title":"Cell"},{"issue":"10","key":"1024_CR38","doi-asserted-by":"publisher","first-page":"764","DOI":"10.1016\/j.tcb.2020.07.003","volume":"30","author":"B Bakir","year":"2020","unstructured":"Bakir B, Chiarella AM, Pitarresi JR, Rustgi AK. EMT, MET, plasticity, and tumor metastasis. Trends Cell Biol. 2020;30(10):764\u201376.","journal-title":"Trends Cell Biol"},{"issue":"1","key":"1024_CR39","doi-asserted-by":"publisher","first-page":"402","DOI":"10.1038\/s41392-021-00791-1","volume":"6","author":"X Hu","year":"2021","unstructured":"Hu X, Li J, Fu M, Zhao X, Wang W. The JAK\/STAT signaling pathway: from bench to clinic. Signal Transduct Target Ther. 2021;6(1):402.","journal-title":"Signal Transduct Target Ther"},{"issue":"4","key":"1024_CR40","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1038\/s43018-021-00195-8","volume":"2","author":"A Passaro","year":"2021","unstructured":"Passaro A, J\u00e4nne PA, Mok T, Peters S. Overcoming therapy resistance in EGFR-mutant lung cancer. Nat Cancer. 2021;2(4):377\u201391.","journal-title":"Nat Cancer"},{"issue":"6","key":"1024_CR41","doi-asserted-by":"publisher","first-page":"356","DOI":"10.1038\/s41568-022-00456-3","volume":"22","author":"S Goel","year":"2022","unstructured":"Goel S, Bergholz JS, Zhao JJ. Targeting CDK4 and CDK6 in cancer. Nat Rev Cancer. 2022;22(6):356\u201372.","journal-title":"Nat Rev Cancer"},{"issue":"1","key":"1024_CR42","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1038\/s41392-021-00572-w","volume":"6","author":"L Zhong","year":"2021","unstructured":"Zhong L, Li Y, Xiong L, Wang W, Wu M, Yuan T, et al. Small molecules in targeted cancer therapy: advances, challenges, and future perspectives. Signal Transduct Target Ther. 2021;6(1):201.","journal-title":"Signal Transduct Target Ther"},{"issue":"9","key":"1024_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2023.100804","volume":"4","author":"S Kapoor","year":"2023","unstructured":"Kapoor S, Narayanan A. Leakage and the reproducibility crisis in machine-learning-based science. Patterns (NY). 2023;4(9):100804.","journal-title":"Patterns (NY)"},{"issue":"1","key":"1024_CR44","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1186\/s13059-019-1850-9","volume":"21","author":"HTN Tran","year":"2020","unstructured":"Tran HTN, Ang KS, Chevrier M, Zhang X, Lee NYS, Goh M, et al. A benchmark of batch-effect correction methods for single-cell RNA sequencing data. Genome Biol. 2020;21(1):12.","journal-title":"Genome Biol"},{"issue":"6","key":"1024_CR45","doi-asserted-by":"publisher","first-page":"968","DOI":"10.1242\/dev.140566","volume":"144","author":"J Drost","year":"2017","unstructured":"Drost J, Clevers H. Translational applications of adult stem cell-derived organoids. Development. 2017;144(6):968\u201375.","journal-title":"Development"}],"container-title":["Human Genomics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40246-026-01024-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40246-026-01024-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40246-026-01024-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T04:51:24Z","timestamp":1789447884000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1186\/s40246-026-01024-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,15]]},"references-count":45,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2026,12]]}},"alternative-id":["1024"],"URL":"https:\/\/doi.org\/10.1186\/s40246-026-01024-8","relation":{},"ISSN":["1479-7364"],"issn-type":[{"value":"1479-7364","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,15]]},"assertion":[{"value":"29 May 2026","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 July 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 September 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This study was performed in line with the principles of the Declaration of Helsinki. Approval for the collection and use of clinical tissue samples was granted by the Ethics Committee of The First Affiliated Hospital of Zhengzhou University (Approval No. 2022-KY-1089-001).","order":1,"name":"Ethics","label":"Ethics approval","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Informed consent was obtained from all individual participants included in the study prior to surgical resection and sample collection.","order":2,"name":"Ethics","label":"Consent to participate","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":"138"}}