Evaluating deconvolution methods using real bulk RNA-expression data for robust prognostic insights across cancer types.

ABSTRACT:

Background

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.

Results

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 coh

SUBMITTER: Li M 

PROVIDER: S-EPMC12906006 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature