DeSCENT: Deconvolutional Single-Cell RNA-seq Enhances Transcriptome-based Cancer Survival Analysis

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ID: 339122
2026
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Abstract
MOTIVATION: Single-cell RNA-seq captures tumor heterogeneity, but it is rarely available together with patient survival outcomes. Bulk RNA-seq cohorts provide survival data but lack direct measurements of cellular expression. RESULTS: DeSCENT generates reference-informed cellular profiles from bulk RNA-seq and combines both representations for survival prediction. We evaluated DeSCENT with three survival models on eight TCGA cancer cohorts. Compared with Bulk, DeSCENT improved average C-index and generally improved time-dependent AUC, while integrated Brier score results were mixed. External validation showed higher average C-index than all three baselines, although performance was lower than in internal evaluation. These findings support the use of cellular representations in the evaluated transcriptome-only setting. AVAILABILITY AND IMPLEMENTATION: Code: https://github.com/YonghaoZhao722/DeSCENT. Archival snapshot: doi:10.5281/zenodo.22073099. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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openalex_W7138951549 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Yonghao Zhao, Zeyu You, Yu Shen, Jielei Chu, Xun Gong, Tianrui Li, Ziqiang Wang, Chuan Shan Xu, ZhiPeng Luo, Yazhou He
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag810
URL
https://doi.org/10.1093/bioinformatics/btag810
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