REDE: A Quantitative Framework for Differential-Expression Reproducibility and Diagnostic Transfer Across Nine Cohorts and Three Cancers

Abstract

Differential-expression analyses often turn cohort-specific significance into claims of stable gene signatures or diagnostic biomarkers. We evaluated which layers of evidence reproduce across independent datasets and whether discovery-derived panels retain locked tumor-versus-non-tumor classification performance. Nine public microarray cohorts were organized into fixed discovery, validation, and external-test experiments for pancreatic ductal adenocarcinoma, breast cancer, and lung cancer. Reproducibility was assessed for DEG burden, exact membership, top-rank overlap, signed effects, prespecified gene confirmation, and Hallmark pathways. We also introduced REDE-2Fold, in which each discovery cohort is split once at patient level, differential expression is performed independently in both folds, and only same-direction genes selected in both are retained. Four training-only panels were then evaluated with locked logistic models and thresholds: all discovery DEGs, the top 19 discovery DEGs, all REDE-2Fold genes, and the top 19 REDE-2Fold genes. Broad DEG-list confirmation in both independent cohorts ranged from 15.5% to 39.5%, rising to 50.1% to 84.3% for large effects. Pathway replication ranged from 52.2% to 88.9%. Compact 19-gene panels retained high external ROC-AUC, but locked operating points were often unstable: some models with ROC-AUC near 1.0 showed zero specificity or very low sensitivity. These results define a hierarchy from thresholded membership through effect, pathway, discrimination, and operating-point transfer. REDE provides a seven-level quantitative framework for matching transcriptomic claims to the evidence actually tested, while REDE-2Fold offers a minimum internal feature-stability procedure that strengthens but does not replace independent validation.