Evidence map›Paper›PMID 41429574›Full record

ArticleBioinformatics (Oxford, England)2026

scSurv: a deep generative model for single-cell survival analysis.

Chikara Mizukoshi, Yasuhiro Kojima, Shuto Hayashi, Ko Abe, Daisuke Kasugai, Teppei Shimamura

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Stromal FAPClinical and translational medicine · 2026
    Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Chikara MizukoshiDepartment of Computational and Systems Biology, Division of Biological Data Science, Medical Research Laboratory, Institute for Integrated Research, Institute of Science Tokyo, Tokyo 113-8510, Japan.ORCID 0009-0009-4489-4636
Yasuhiro KojimaLaboratory of Computational Life Science, National Cancer Center Research Institute, Tokyo, 104-0045, Japan.
Shuto HayashiDepartment of Computational and Systems Biology, Division of Biological Data Science, Medical Research Laboratory, Institute for Integrated Research, Institute of Science Tokyo, Tokyo 113-8510, Japan.ORCID 0000-0002-1623-0738
Ko AbeDepartment of Computational and Systems Biology, Division of Biological Data Science, Medical Research Laboratory, Institute for Integrated Research, Institute of Science Tokyo, Tokyo 113-8510, Japan.
Daisuke KasugaiDepartment of Emergency and Critical Care Medicine, Graduate School of Medicine, Nagoya University, Nagoya, Aichi 466-8550, Japan.
Teppei ShimamuraDepartment of Computational and Systems Biology, Division of Biological Data Science, Medical Research Laboratory, Institute for Integrated Research, Institute of Science Tokyo, Tokyo 113-8510, Japan.ORCID 0009-0009-7713-2531

Funding

Advanced Genome Research and Bioinformatics Study to Facilitate Medical Innovation JP25tm0424226AMEDBrain/MINDS Health and Diseases JP25wm0625519Core Research for Evolutional Science and Technology JP25gm2010002Grant-in-Aid for Transformative Research Areas (A) 23H04938Interdisciplinary Cutting-edge Research JP25wm0325068Japan Agency for Medical Research and Development (AMED)Japan Science and Technology Agency (JST) JPMJMS2025Japan Society for the Promotion of Science (JSPS)JSPS Grant-in-Aid for Early-Career Scientists 23K16991Medical Research Center Initiative for High Depth Omics and Multilayered Stress Diseases at the Institute of Science TokyoMoonshot R&D Program JP25zf0127012National Cancer Center Research and Development Fund 2024-A-6P-PROMOTE 24ama221609h0001Project Promoting Support for Drug Discovery JP25nk0101112Shirokane supercomputer at the Human Genome CenterTransformative Research Areas 22H04925TSUBAME3.0 supercomputer at the Institute of Science TokyoUniversity of Tokyo
6 · The paper itself

Abstract

motivationSingle-cell omics analysis has unveiled the heterogeneity of various cell types within tumors. However, no methodology currently reveals how this heterogeneity influences cancer patient survival at single-cell resolution. Here, we introduce scSurv, combining a Cox proportional hazards model with a deep generative model of single-cell transcriptome, to estimate individual cellular contributions to clinical outcomes.

resultsThe accuracy of scSurv was validated using both simulated and real datasets. This method identifies cells associated with favorable or adverse prognoses and extracts genes correlated with their contribution levels. In melanoma, scSurv reproduces known prognostic macrophage classifications and facilitates hazard mapping through spatial transcriptomics in renal cell carcinoma. We also identified genes consistently associated with prognosis across multiple cancers and demonstrated the applicability of this method to infectious diseases. scSurv is a novel framework for quantifying the heterogeneity of individual cellular effects on clinical outcomes. AVAILABILITY: The implementation of scSurv is available on GitHub (https://github.com/3254c/scSurv) and Zenodo (https://doi.org/10.5281/zenodo.17793054).

Indexed as

NeoplasmsSingle-Cell AnalysisSoftwareCarcinoma, Renal CellCell SurvivalComputational BiologyGene Expression ProfilingHumansMelanomaPrognosisProportional Hazards ModelsTranscriptome

Identifiers

PMID41429574
PMCPMC12797213

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.