{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T23:56:05Z","timestamp":1790898965391,"version":"4.1.0"},"reference-count":30,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"vor","delay-in-days":273,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002855","name":"Ministry of Science and Technology of the People\u2019s Republic of China","doi-asserted-by":"publisher","award":["2023YFC3605800"],"award-info":[{"award-number":["2023YFC3605800"]}],"id":[{"id":"10.13039\/501100002855","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004608","name":"Natural Science Foundation of Jiangsu Province","doi-asserted-by":"publisher","award":["BK20222008"],"award-info":[{"award-number":["BK20222008"]}],"id":[{"id":"10.13039\/501100004608","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Nanjing Science and Technology Bureau","award":["202205053"],"award-info":[{"award-number":["202205053"]}],"id":[{"id":"https:\/\/ror.org\/031gc0z89","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Provincial Department of Science and Technology in Jiangsu Province","award":["BE2023781"],"award-info":[{"award-number":["BE2023781"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Accurate cancer subtype classification from single-transcriptome data remains challenging because of biological heterogeneity, measurement noise, and limited sample sizes. This study examined whether disease\u2013gene priors add predictive information under strict separation of association-edge, patient-sample, and external-transfer boundaries. It also audited a historically selected 12-gene subset without claiming de novo discovery. Important mRNA Identification through Hybrid Fusion (ImRIHF) uses a layer attention graph convolutional network to learn cancer-specific association scores. A graph convolutional network for disease classification (GCNDC) then classifies subtypes after score-guided expression weighting. Association folds were masked before both Gaussian kernels and the heterogeneous graph were rebuilt. Patient preprocessing, feature selection, graph construction, tuning, and fitting remained training-contained. Twenty repeated five-fold refits described stability, while separate locked audits supplied paired effects. Patient-disjoint external transfer used frozen development predictors and no target-cohort fitting. Matched GCNDC and full-transcriptome controls bounded interpretation within one fixed 29-coordinate multiplicity family. UCEC remained feasibility-only because its locked audit contained 10 MSEAC cases. Leakage-controlled association AUPR and AUROC were 0.932 and 0.941, respectively. Non-UCEC locked effects versus matched GCNDC ranged from $+0.027$ to $+0.035$; the UCEC effect was $+0.032$ and remained descriptive. Strict-common external effects were $+0.034$ in METABRIC, $+0.026$ in CGGA, and $+0.036$ in GSE14333. Of 12 historical challenge genes, 11 mapped, 4 were BH-significant, and 2 were both direction-concordant and BH-significant. The frozen disease-guided rule showed modest positive differences from matched controls in the named retrospective cohorts. Full-transcriptome controls, UCEC feasibility limits, and source-specific transfer boundaries restrict this result. The analyses do not establish generic superiority, prospective validity, clinical utility, population generalization, causality, or a validated biomarker panel.<\/jats:p>","DOI":"10.1093\/bfgp\/elag009","type":"journal-article","created":{"date-parts":[[2026,8,19]],"date-time":"2026-08-19T11:52:51Z","timestamp":1787140371000},"source":"Crossref","is-referenced-by-count":0,"title":["Knowledge-guided graph fusion of mRNA profiles for interpretable cancer subtyping"],"prefix":"10.1093","volume":"25","author":[{"given":"Jie","family":"Ni","sequence":"first","affiliation":[{"name":"School of Biological Science and Medical Engineering, Southeast University , No. 2 Southeast University Road, Jiangning District, Nanjing, Jiangsu 211102 ,","place":["China"]},{"name":"Department of Medical Informatics, School of Biomedical Engineering and Informatics, Nanjing Medical University , 101 Longmian Avenue, Jiangning District, Nanjing, Jiangsu 211166 ,","place":["China"]},{"name":"Data Science Group, Digital Science Center, University of Innsbruck , Innrain 15, 6020 Innsbruck, Tirol ,","place":["Austria"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinting","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Biological Science and Medical Engineering, Southeast University , No. 2 Southeast University Road, Jiangning District, Nanjing, Jiangsu 211102 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingyang","family":"Li","sequence":"additional","affiliation":[{"name":"School of Biological Science and Medical Engineering, Southeast University , No. 2 Southeast University Road, Jiangning District, Nanjing, Jiangsu 211102 ,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Biological Science and Medical Engineering, Southeast University , No. 2 Southeast University Road, Jiangning District, Nanjing, Jiangsu 211102 ,","place":["China"]},{"name":"Department of Information , the First Affiliated Hospital, Nanjing Medical University, 300 Guangzhou Road, Gulou District, Nanjing, Jiangsu 210029,","place":["China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adam","family":"Jatowt","sequence":"additional","affiliation":[{"name":"Data Science Group , Digital Science Center, University of Innsbruck, Innrain 15, 6020 Innsbruck, Tirol,","place":["Austria"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,10,1]]},"reference":[{"key":"2026100119451591700_ref1","doi-asserted-by":"publisher","first-page":"630","DOI":"10.1038\/s41576-020-0258-4","article-title":"mRNAs, proteins and the emerging principles of gene expression control","volume":"21","author":"Buccitelli","year":"2020","journal-title":"Nat Rev 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