ArticleJournal of translational medicine2026
A machine learning-based epigenetic signature reveals YTHDC1 stabilizes POU5F1 to oppose tumor progression.
Article in Journal of translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundThe 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.
methodsWe 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.
resultsA 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.
conclusionThe 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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