{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T10:58:00Z","timestamp":1781693880265,"version":"3.54.5"},"reference-count":44,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T00:00:00Z","timestamp":1749513600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2021YFA1301603"],"award-info":[{"award-number":["2021YFA1301603"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&D Program of China","doi-asserted-by":"publisher","award":["2020YFE0202200"],"award-info":[{"award-number":["2020YFE0202200"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32088101"],"award-info":[{"award-number":["32088101"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82203601"],"award-info":[{"award-number":["82203601"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,28]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Omics-based molecular subtyping in large-scale and multicentric cohort studies is a prerequisite for proteomics-driven precision medicine (PDPM). However, maintaining subtypes with robust molecular features and significant prognostic associations across different cohorts remains challenging due to biological heterogeneity and technical inconsistency. Herein, we propose a subtyping algorithm, named Survival Reinforced Patient Stratification (SRPS), to adapt known subtypes from a discovery cohort to another by simultaneously preserving the distinct prognosis and molecular characteristics of each subtype. SRPS was benchmarked on simulated and real-world datasets, demonstrating a 12% increase in classification accuracy and best prognostic discrimination. Moreover, based on the calculated subtype significance score, an \u201cunpopular\u201d protein, peptidyl-prolyl cis-trans isomerase C (PPIC), was identified as the top 1 remarkable protein for subtyping hepatocellular carcinoma (HCC) patients with the worst prognosis. Eventually, PPIC was experimentally validated as\u00a0a pro-cancer protein in HCC, confirming our work as a demonstration of interpretable machine learning-guided biological discovery in PDPM research. SRPS is publicly available at https:\/\/github.com\/PHOENIXcenter\/SRPS and https:\/\/ngdc.cncb.ac.cn\/biocode\/tool\/BT007770.<\/jats:p>","DOI":"10.1093\/gpbjnl\/qzaf052","type":"journal-article","created":{"date-parts":[[2025,6,10]],"date-time":"2025-06-10T14:38:07Z","timestamp":1749566287000},"source":"Crossref","is-referenced-by-count":2,"title":["SRPS: Survival Reinforced Transfer Learning for Multicentric Proteomic Subtyping and Biomarker Discovery"],"prefix":"10.1093","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8593-2277","authenticated-orcid":false,"given":"Linhai","family":"Xie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5013-5163","authenticated-orcid":false,"given":"Pei","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0361-2438","authenticated-orcid":false,"given":"Cheng","family":"Chang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,6,10]]},"reference":[{"key":"2026061706422225800_qzaf052-B1","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1038\/s41586-019-0987-8","article-title":"Proteomics identifies new therapeutic targets of early-stage hepatocellular carcinoma","volume":"567","author":"Jiang","year":"2019","journal-title":"Nature"},{"key":"2026061706422225800_qzaf052-B2","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1016\/j.cell.2019.08.052","article-title":"Integrated proteogenomic characterization of HBV-related hepatocellular carcinoma","volume":"179","author":"Gao","year":"2019","journal-title":"Cell"},{"key":"2026061706422225800_qzaf052-B3","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.cell.2020.06.013","article-title":"Proteogenomic characterization reveals therapeutic vulnerabilities in lung adenocarcinoma","volume":"182","author":"Gillette","year":"2020","journal-title":"Cell"},{"key":"2026061706422225800_qzaf052-B4","doi-asserted-by":"crossref","first-page":"1436","DOI":"10.1016\/j.cell.2020.10.036","article-title":"Proteogenomic landscape of breast cancer tumorigenesis and targeted therapy","volume":"183","author":"Krug","year":"2020","journal-title":"Cell"},{"key":"2026061706422225800_qzaf052-B5","doi-asserted-by":"crossref","first-page":"245","DOI":"10.1016\/j.cell.2020.05.043","article-title":"Integrative proteomic characterization of human lung adenocarcinoma","volume":"182","author":"Xu","year":"2020","journal-title":"Cell"},{"key":"2026061706422225800_qzaf052-B6","doi-asserted-by":"crossref","first-page":"4348","DOI":"10.1016\/j.cell.2021.07.016","article-title":"A proteogenomic portrait of lung squamous cell carcinoma","volume":"184","author":"Satpathy","year":"2021","journal-title":"Cell"},{"key":"2026061706422225800_qzaf052-B7","doi-asserted-by":"crossref","first-page":"530","DOI":"10.1038\/415530a","article-title":"Gene expression profiling predicts clinical outcome of breast cancer","volume":"415","author":"Van't Veer","year":"2002","journal-title":"Nature"},{"key":"2026061706422225800_qzaf052-B8","doi-asserted-by":"crossref","first-page":"1999","DOI":"10.1056\/NEJMoa021967","article-title":"A gene-expression signature as a predictor of survival in breast cancer","volume":"347","author":"van de Vijver","year":"2002","journal-title":"N Engl J Med"},{"key":"2026061706422225800_qzaf052-B9","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1093\/jnci\/djj329","article-title":"Validation and clinical utility of a 70-gene prognostic signature for women with node-negative breast cancer","volume":"98","author":"Buyse","year":"2006","journal-title":"J Natl Cancer Inst"},{"key":"2026061706422225800_qzaf052-B10","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1056\/NEJMoa1602253","article-title":"70-gene signature as an aid to treatment decisions in early-stage breast cancer","volume":"375","author":"Cardoso","year":"2016","journal-title":"N Engl J Med"},{"key":"2026061706422225800_qzaf052-B11","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn"},{"key":"2026061706422225800_qzaf052-B12","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"2026061706422225800_qzaf052-B13","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1038\/s41592-019-0619-0","article-title":"Fast, sensitive and accurate integration of single-cell data with Harmony","volume":"16","author":"Korsunsky","year":"2019","journal-title":"Nat Methods"},{"key":"2026061706422225800_qzaf052-B14","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s13059-019-1850-9","article-title":"A benchmark of batch-effect correction methods for single-cell RNA sequencing data","volume":"21","author":"Tran","year":"2020","journal-title":"Genome Biol"},{"key":"2026061706422225800_qzaf052-B15","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1093\/biostatistics\/kxv027","article-title":"Methods that remove batch effects while retaining group differences may lead to exaggerated confidence in downstream analyses","volume":"17","author":"Nygaard","year":"2016","journal-title":"Biostatistics"},{"key":"2026061706422225800_qzaf052-B16","first-page":"1","article-title":"Domain-adversarial training of neural networks","volume":"17","author":"Ganin","year":"2016","journal-title":"J Mach Learn Res"},{"key":"2026061706422225800_qzaf052-B17","first-page":"3723","article-title":"Maximum classifier discrepancy for unsupervised domain adaptation","author":"Saito","year":"2018","journal-title":"Proc IEEE Conf Comput Vis Pattern Recog"},{"key":"2026061706422225800_qzaf052-B18","first-page":"19276","article-title":"Domain adaptation with conditional distribution matching and generalized label shift","author":"Des Combes","year":"2020","journal-title":"Adv Neural Inf Process Syst"},{"key":"2026061706422225800_qzaf052-B19","doi-asserted-by":"crossref","first-page":"283","DOI":"10.1038\/s42256-020-0180-7","article-title":"An interpretable mortality prediction model for COVID-19 patients","volume":"2","author":"Yan","year":"2020","journal-title":"Nat Mach Intell"},{"key":"2026061706422225800_qzaf052-B20","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1038\/s41586-021-03922-4","article-title":"Biologically informed deep neural network for prostate cancer discovery","volume":"598","author":"Elmarakeby","year":"2021","journal-title":"Nature"},{"key":"2026061706422225800_qzaf052-B21","first-page":"1135","article-title":"\u201cWhy should I trust you?\u201d explaining the predictions of any classifier","author":"Ribeiro","year":"2016","journal-title":"Conf North Am Chapter Assoc Comput Linguist"},{"key":"2026061706422225800_qzaf052-B22","author":"Ancona","year":"2018"},{"key":"2026061706422225800_qzaf052-B23","first-page":"4768","article-title":"A unified approach to interpreting model predictions","author":"Lundberg","year":"2017","journal-title":"Adv Neural Inf Process Syst"},{"key":"2026061706422225800_qzaf052-B24","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1023\/A:1022672621406","article-title":"Simple statistical gradient-following algorithms for connectionist reinforcement learning","volume":"8","author":"Williams","year":"1992","journal-title":"Mach Learn"},{"key":"2026061706422225800_qzaf052-B25","first-page":"2204","article-title":"Recurrent models of visual attention","author":"Mnih","year":"2014","journal-title":"Adv Neural Inf Process Syst"},{"key":"2026061706422225800_qzaf052-B26","first-page":"2048","article-title":"Show, attend and tell: neural image caption generation with visual attention","volume":"37","author":"Xu","year":"2015","journal-title":"Int Conf Mach Learn"},{"key":"2026061706422225800_qzaf052-B27","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1186\/s13059-017-1305-0","article-title":"Splatter: simulation of single-cell RNA sequencing data","volume":"18","author":"Zappia","year":"2017","journal-title":"Genome Biol"},{"key":"2026061706422225800_qzaf052-B28","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v097.i03","article-title":"Simulating survival data using the simsurv R package","volume":"97","author":"Brilleman","year":"2021","journal-title":"J Stat Softw"},{"key":"2026061706422225800_qzaf052-B29","doi-asserted-by":"crossref","first-page":"101315","DOI":"10.1016\/j.xcrm.2023.101315","article-title":"Integrated omics landscape of hepatocellular carcinoma suggests proteomic subtypes for precision therapy","volume":"4","author":"Xing","year":"2023","journal-title":"Cell Rep Med"},{"key":"2026061706422225800_qzaf052-B30","author":"Mouli","year":"2019"},{"key":"2026061706422225800_qzaf052-B31","doi-asserted-by":"crossref","first-page":"482","DOI":"10.1038\/s41596-018-0103-9","article-title":"Pathway enrichment analysis and visualization of omics data using g:Profiler, GSEA, Cytoscape and EnrichmentMap","volume":"14","author":"Reimand","year":"2019","journal-title":"Nat Protoc"},{"key":"2026061706422225800_qzaf052-B32","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/1471-2105-14-7","article-title":"GSVA: gene set variation analysis for microarray and RNA-Seq data","volume":"14","author":"H\u00e4nzelmann","year":"2013","journal-title":"BMC Bioinformatics"},{"key":"2026061706422225800_qzaf052-B33","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1186\/cc2955","article-title":"Statistics review 12: survival analysis","volume":"8","author":"Bewick","year":"2004","journal-title":"Crit Care"},{"key":"2026061706422225800_qzaf052-B34","doi-asserted-by":"crossref","first-page":"2543","DOI":"10.1001\/jama.1982.03320430047030","article-title":"Evaluating the yield of medical tests","volume":"247","author":"Harrell","year":"1982","journal-title":"JAMA"},{"key":"2026061706422225800_qzaf052-B35","first-page":"2825","article-title":"Scikit-learn: machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J Mach Learn Res"},{"key":"2026061706422225800_qzaf052-B36","first-page":"1647","article-title":"Conditional adversarial domain adaptation","author":"Long","year":"2018","journal-title":"Adv Neural Inf Process Syst"},{"key":"2026061706422225800_qzaf052-B37","author":"Zhang","year":"2019"},{"key":"2026061706422225800_qzaf052-B38","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1055\/s-2007-1007122","article-title":"Prognosis of hepatocellular carcinoma: the BCLC staging classification","volume":"19","author":"Llovet","year":"1999","journal-title":"Semin Liver Dis"},{"key":"2026061706422225800_qzaf052-B39","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1038\/nature14236","article-title":"Human-level control through deep reinforcement learning","volume":"518","author":"Mnih","year":"2015","journal-title":"Nature"},{"key":"2026061706422225800_qzaf052-B40","first-page":"6276","article-title":"Learning with training wheels: speeding up training with a simple controller for deep reinforcement learning","author":"Xie","year":"2018","journal-title":"IEEE Int Conf Robot Autom"},{"key":"2026061706422225800_qzaf052-B41","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1126\/sciadv.aap7885","article-title":"Deep reinforcement learning for de novo drug design","volume":"4","author":"Popova","year":"2018","journal-title":"Sci Adv"},{"key":"2026061706422225800_qzaf052-B42","author":"Jang","year":"2017"},{"key":"2026061706422225800_qzaf052-B43","doi-asserted-by":"crossref","first-page":"23086","DOI":"10.1074\/jbc.M114.570911","article-title":"Depletion of cyclophilins B and C leads to dysregulation of endoplasmic reticulum redox homeostasis","volume":"289","author":"Stocki","year":"2014","journal-title":"J Biol Chem"},{"key":"2026061706422225800_qzaf052-B44","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.toxlet.2021.06.021","article-title":"Ppic modulates CCl4-induced liver fibrosis and TGF-\u03b2-caused mouse hepatic stellate cell activation and regulated by miR-137-3p","volume":"350","author":"Yang","year":"2021","journal-title":"Toxicol Lett"}],"container-title":["Genomics, Proteomics &amp; Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/gpb\/advance-article-pdf\/doi\/10.1093\/gpbjnl\/qzaf052\/63464626\/qzaf052.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/gpb\/article-pdf\/23\/5\/qzaf052\/63464626\/qzaf052.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/gpb\/article-pdf\/23\/5\/qzaf052\/63464626\/qzaf052.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T10:42:43Z","timestamp":1781692963000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/gpb\/article\/doi\/10.1093\/gpbjnl\/qzaf052\/8159978"}},"subtitle":[],"editor":[{"given":"Yu","family":"Xue","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2025,6,10]]},"references-count":44,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,10,28]]}},"URL":"https:\/\/doi.org\/10.1093\/gpbjnl\/qzaf052","relation":{},"ISSN":["1672-0229","2210-3244"],"issn-type":[{"value":"1672-0229","type":"print"},{"value":"2210-3244","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2025,10]]},"published":{"date-parts":[[2025,6,10]]},"article-number":"qzaf052"}}