Scholarlink

Background <p>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.</p> Methods <p>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.</p> Results <p>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, <i>p</i> = 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.</p> Conclusion <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月20日

查看全文
  • Chen Fang,
  • Jun Dai,
  • Zhiwei Weng,
  • Lianhua Zhang,
  • Yutong Xing,
  • Diamond Liu,
  • Hui Wu,
  • Xuanhao Li,
  • Qiang Liu,
  • Gangling Su

摘要

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.