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A machine learning-based epigenetic signature reveals YTHDC1 stabilizes POU5F1 to oppose tumor progression

Chen Fang et al · BMC · 2026

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Abstract Background The prognosis and progression mechanisms of bladder cancer (BLCA) are highly heterogeneous, driven by complex genetic and epigenetic alterations. This study aimed to construct a robust prognostic signature using epigenetic modification-related genes and to investigate the underlying molecular mechanisms driving its predictive power. Methods We developed a prognostic signature by applying a machine learning-based approach to screen epigenetic genes in the TCGA (The Cancer Genome Atlas)-BLCA cohort. Its performance was rigorously evaluated against 101 other machine learning algorithms and 110 previously published signatures across four independent validation datasets (IMvigor210, E-MTAB-4321, GSE31684, GSE48075). Associations with clinical, genetic, and transcriptomic features were analyzed. Immune infiltration, cell-cell interactions, and drug responses were assessed using both bulk and single-cell RNA-seq data. The functional role of a key signature gene, YTHDC1 (YTH Domain-Containing 1), was investigated through in vitro assays. Results A six-gene epigenetic signature was constructed. It significantly stratified patients into high- and low-risk groups with distinct overall survival (median survival 20.5 vs. 86.8 months, HR = 2.12, p = 7.7e-7). Our signature demonstrated superior predictive accuracy (C-index and 1-year AUROC) compared to other models. High-risk scores correlated with adverse clinical features (e.g., advanced stage), elevated PD-1/PD-L1, higher genomic instability, and immunosuppressive microenvironments. Single-cell analysis revealed altered T-cell interactions in high-risk cases. Mechanistically, YTHDC1 was shown to bind and stabilize POU5F1 (OCT4) mRNA, thereby inhibiting proliferation and migration in BLCA cell lines (T24, 5637). This anti-tumor effect was dependent on POU5F1. Conclusion The machine learning-derived epigenetic signature is a robust indicator of BLCA heterogeneity across multiple dimensions. YTHDC1, a core component, inhibits cancer progression by stabilizing POU5F1 mRNA, highlighting a novel regulatory axis.

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APA 7

al, C. F. E. (2026). A machine learning-based epigenetic signature reveals YTHDC1 stabilizes POU5F1 to oppose tumor progression. https://doi.org/10.1186/s12967-026-08010-7

MLA

al, Chen Fang et. "A machine learning-based epigenetic signature reveals YTHDC1 stabilizes POU5F1 to oppose tumor progression." 2026. https://doi.org/10.1186/s12967-026-08010-7.

Chicago

al, Chen Fang et. 2026. "A machine learning-based epigenetic signature reveals YTHDC1 stabilizes POU5F1 to oppose tumor progression.". https://doi.org/10.1186/s12967-026-08010-7.

Harvard

al, C. F. E. 2026, A machine learning-based epigenetic signature reveals YTHDC1 stabilizes POU5F1 to oppose tumor progression, BMC, available at: https://doi.org/10.1186/s12967-026-08010-7 [Accessed 6 Aug. 2026].

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Title
A machine learning-based epigenetic signature reveals YTHDC1 stabilizes POU5F1 to oppose tumor progression
Author / contributors
Chen Fang et al
Publisher
BMC
Publication year
2026
ISSN
1479-5876
ISSN
1479-5876
Language
English
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