Evaluating Deconvolution Methods Using Real Bulk RNA-expression Data for Robust Prognostic Insights in Pan-Cancer

Deconvolution of bulk RNA-expression data unlocks the cellular complexity of cancer, yet traditional pseudobulk benchmarks falter in real-world settings where absolute cell proportions are unknown. Here, we introduce a novel real-data framework, leveraging 16 real bulk RNA-expression cohorts (4,576 samples) across eight cancer types to evaluate six deconvolution methods based on relative changes in differentially proportioned (DP) cell types—an impartial and reliable metric. Across three innovative benchmark scenarios—consistency with scRNA-seq, reproducibility across cohorts, and prognostic relevance—ReCIDE, Bisque, and BayesPrism have been demonstrated to be the three most robust deconvolution methods. analysis of ten cancer types revealed matrix cancer-associated fibroblasts (mCAF) as a poor prognosis marker (p = 0.0081) and CLEC9A + dendritic cells (cDC_CLEC9A) as a favorable one (p = 0.016). Furthermore, a prognostic indicator (ASC% - mCAF%) developed using ReCIDE was validated across five TCGA and three GEO cohorts. This study broadens deconvolution benchmarking, offering actionable tools for precision oncology and guiding method selection for translational research.