biostudies-literatureLi MNational Natural Science Foundation of China38https://www.ebi.ac.uk/biostudies/studies/S-EPMC12906006biostudies-literatureUnknown27(1)<h4>Background</h4>Deconvolution of bulk RNA-expression data unlocks the cellular complexity of cancer, yet traditional pseudobulk benchmarks may not always be reliable in real-world settings where absolute cell proportions are unknown.<h4>Results</h4>Here, we introduce a novel real-data framework, leveraging 18 real bulk RNA-expression cohorts (5,891 samples) across nine cancer types to evaluate five deconvolution methods based on differentially proportioned (DP) and prognosis-related (PR) cell types. Across three innovative benchmark scenarios-consistency with scRNA-seq, reproducibility across cohorts, and reproducibility of prognostic relevance-ReCIDE and BayesPrism stand out as two robust deconvolution methods. Application of a pan-cancer analysis based on the deconvolution of TCGA cohGenome biologyEvaluating deconvolution methods using real bulk RNA-expression data for robust prognostic insights across cancer types.PMC1290600632170667Tian WLee YSu YLi MTang YfalseEvaluating deconvolution methods using real bulk RNA-expression data for robust prognostic insights across cancer types.<h4>Background</h4>Deconvolution of bulk RNA-expression data unlocks the cellular complexity of cancer, yet traditional pseudobulk benchmarks may not always be reliable in real-world settings where absolute cell proportions are unknown.<h4>Results</h4>Here, we introduce a novel real-data framework, leveraging 18 real bulk RNA-expression cohorts (5,891 samples) across nine cancer types to evaluate five deconvolution methods based on differentially proportioned (DP) and prognosis-related (PR) cell types. Across three innovative benchmark scenarios-consistency with scRNA-seq, reproducibility across cohorts, and reproducibility of prognostic relevance-ReCIDE and BayesPrism stand out as two robust deconvolution methods. Application of a pan-cancer analysis based on the deconvolution of TCGA coh2026-01-01T00:00:00Z2026 Jan2026-07-15T16:27:03.41Z2026-07-07T03:07:30.37ZS-EPMC129060064156653010.1186/s13059-026-03942-1