{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Li M"],"funding":["National Natural Science Foundation of China"],"pagination":["38"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12906006"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["27(1)"],"pubmed_abstract":["<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 coh"],"journal":["Genome biology"],"pubmed_title":["Evaluating deconvolution methods using real bulk RNA-expression data for robust prognostic insights across cancer types."],"pmcid":["PMC12906006"],"funding_grant_id":["32170667"],"pubmed_authors":["Tian W","Lee Y","Su Y","Li M","Tang Y"],"additional_accession":[]},"is_claimable":false,"name":"Evaluating deconvolution methods using real bulk RNA-expression data for robust prognostic insights across cancer types.","description":"<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 coh","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Jan","modification":"2026-07-15T16:27:03.41Z","creation":"2026-07-07T03:07:30.37Z"},"accession":"S-EPMC12906006","cross_references":{"pubmed":["41566530"],"doi":["10.1186/s13059-026-03942-1"]}}