Abstract Motivation Foundation models trained on large-scale single-cell transcriptomes can capture rich molecular representations of cellular states, yet their potential for cancer survival prediction from bulk RNA-seq data remains largely unexplored. Results We applied the single-cell foundation model scFoundation to derive patient-level embeddings across 25 cancer types from TCGA and systematically evaluated their prognostic value under both cancer-specific and pan-cancer settings. To leverage complementary information, we developed an Embedding–Gene–Survival Prediction (EGSP) model that integrates foundation model embeddings with gene expression and clinical variables. EGSP achieved a mean concordance index (C-index) of 0.724 across cancers and exceeded 0.8 in seven cancer types, consistently outperforming single-modality models and existing multi-omics survival approaches. Comparative analyses showed that embeddings derived from pretrained scFoundation weights exhibited lower redundancy with gene expression while retaining complementary prognostic signals relative to pan-cancer fine-tuned embeddings. Explainable AI analyses further revealed that prognostic embeddings capture interpretable biological programs related to tumor differentiation, immune activity, and tumor-intrinsic growth, enabling transparent survival prediction at both cohort and patient levels. Overall, single-cell foundation model embeddings provide biologically meaningful and partially non-redundant survival signals that substantially improve bulk RNA-seq–based prognostic modeling. Availability and implementation https://github.com/weiliu123/EGSP.
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W. Y. Liu
Q. Wang
Lin Long
Bioinformatics Advances
Shantou University
Shantou University Medical College
Heilongjiang Institute of Technology
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Liu et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69ba431a4e9516ffd37a3f3f — DOI: https://doi.org/10.1093/bioadv/vbag076