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

TL;DR

This paper evaluates deconvolution methods using real bulk RNA data to identify reliable tools for understanding cancer cell composition and prognosis across multiple cancer types.

Contribution

The study introduces a novel real-data benchmark framework using 18 bulk RNA cohorts to assess deconvolution methods and identifies robust tools for translational research.

Findings

ReCIDE and BayesPrism are robust deconvolution methods across three benchmark scenarios.

Matrix cancer-associated fibroblasts (mCAF) are a consistent prognostic marker across multiple cancers.

A prognostic indicator combining classical monocytes and mCAF cell proportions is significant in multiple TCGA and GEO cohorts.

Abstract

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.

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 cohorts identifies matrix cancer-associated fibroblasts (mCAF) as a prognostic marker with consistent effects across multiple cancers. Building on this finding, we find a prognostic indicator combining classical monocytes and mCAF cell proportions to be significant in five TCGA cohorts, which we further validate in five independent GEO cohorts.

This study broadens deconvolution benchmarking, offering actionable tools for precision oncology and guiding method selection for translational research.

The online version contains supplementary material available at 10.1186/s13059-026-03942-1.

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