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<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">JCNR</journal-id><journal-title-group><journal-title>Journal of Clinical and Nursing Research</journal-title></journal-title-group><issn>2208-3685</issn><eissn>2208-3693</eissn><publisher><publisher-name>Bio-Byword Scientific Publishing Pty. Ltd.</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.26689/jcnr.v10i4.14783</article-id><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Prognostic Classification of Hepatocellular Carcinoma Under Incomplete Data Conditions Using a Public Reference Cohort: A Weakly Supervised Multi-Omics Study</title><url>https://artdesignp.com/journal/JCNR/10/4/10.26689/jcnr.v10i4.14783</url><author>LvHanlin,WangXiao,WuKesong,LiLei,WangLei</author><pub-date pub-type="publication-year"><year>2026</year></pub-date><volume>10</volume><issue>4</issue><history><date date-type="pub"><published-time>2026-04-30</published-time></date></history><abstract>Background: Prognostic stratification of hepatocellular carcinoma (HCC) remains difficult because the disease is highly heterogeneous and complete matched multi-omics data are not always available in clinical cohorts.&amp;nbsp; Objective: To develop a weakly supervised multi-omics framework that derives prognostic subtype labels from a public reference cohort and transfers them to cohorts with incomplete data. Methods: This study has analyzed 363 patients in TCGA-LIHC with matched mRNA, miRNA, DNA methylation, and clinical data. Overall-survival-related features were selected by univariate Cox regression, integrated by similarity network fusion (SNF), and clustered by spectral clustering to generate pseudo-labels. TCGA-LIHC was then split 6:4 into training and test sets for supervised modeling. External validation used LIRI-JP, GSE14520, GSE54236, and GSE31384, with models rebuilt on features shared with each cohort. Prognostic performance was evaluated by Kaplan-Meier analysis, log-rank testing, and the concordance index (C-index). Results: A total of 3,890 mRNA features, 150 miRNA features, and 1,889 methylation features were retained. SNF plus spectral clustering identified two subtypes: S1 (n = 257) and S2 (n = 106). S2 had significantly worse overall survival than S1 (log-rank P = 3.891 × 10-9; C-index = 0.866). In internal validation, XGBoost showed the highest AUC (0.983). In external validation, the predicted subtypes remained prognostically informative, with C-index values of 0.857 in LIRI-JP, 0.875 in GSE14520, 0.930 in GSE54236, and 0.883 in GSE31384. Conclusions: In the public datasets included in this study, this weakly supervised framework identified two prognostically distinct HCC subtypes and retained prognostic discrimination after transfer to external cohorts with incomplete data.</abstract><keywords/></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Bertuccio P, Turati F, Carioli G, et al., 2017, Global Trends and Predictions in Hepatocellular Carcinoma Mortality. Journal of Hepatology, 67(2): 302–309.</p><pub-id pub-id-type="doi"/></element-citation></ref><ref id="B2" content-type="article"><label>2</label><element-citation publication-type="journal"><p>Villanueva A, 2019, Hepatocellular Carcinoma. 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