Knowledge-guided graph fusion of mRNA profiles for interpretable cancer subtyping

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-gene 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.