NeuroMetBench: patient-aware evidence bounds for transcript-derived metabolic representations across cancer proteogenomic contexts

Transcript-derived metabolic representations connect gene expression with pathway state, but interpretation depends on sampling unit, endpoint, cohort, and validation evidence. NeuroMetBench is a patient-aware methodology that keeps target quantity, independent unit, endpoint mapping, coverage, uncertainty, transport, numerical diagnostics, and provenance separate rather than collapsing them into a universal score. A frozen three-gene serine score showed RNA-protein rank concordance in 89 lung squamous cell carcinoma (LSCC) patients (Spearman [Formula: see text], 95% confidence interval (CI) 0.8496 to 0.9388) and in a patient-disjoint 90-patient central nervous system/high-grade-glioma (CNS/HGG) cohort ([Formula: see text], 95% CI 0.4697 to 0.7507). Reviewer-driven age/sex sensitivities remained positive in complete cases (LSCC [Formula: see text], partial [Formula: see text]; CNS/HGG [Formula: see text], partial [Formula: see text]), but CNS/HGG complete-case selection was associated with the frozen RNA-score distribution and cannot rule out demographic confounding. A preanalysis-frozen Tel Aviv glioblastoma test was NOT RUN because public RNA/proteomics deposits yielded zero source-documented patient cross-matches (minimum [Formula: see text]). A separately frozen Sun Yat-sen University Cancer Center (SYSUCC) analysis found 20 exact RNA/protein-matched glioblastomas and positive concordance ([Formula: see text], 95% CI 0.1252 to 0.9101; permutation [Formula: see text]). By contrast, RNA score correspondence with measured L-serine in 75 same-cohort patients was weak and uncertain ([Formula: see text], 95% CI -0.0803 to 0.3737; permutation [Formula: see text]). Evidence strength is therefore axis-specific: cross-modal proxy concordance does not establish metabolic flux, biochemical validation, universal method superiority, framework-wide external validation, or calibration.