Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic catastrophe heterogeneity in bladder cancer

Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic catastrophe heterogeneity in bladder cancer

Abstract Background Mitotic catastrophe is well-known as a major pathway of endogenous tumor death, but the prognostic significance of its heterogeneity regarding bladder cancer (BLCA) remains unclear. Methods Our study focused on digging deeper into the TCGA and GEO databases. Through differential...

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Main Authors: Haojie Dai, Zijie Yu, You Zhao, Ke Jiang, Zhenyu Hang, Xin Huang, Hongxiang Ma, Li Wang, Zihao Li, Ming Wu, Jun Fan, Weiping Luo, Chao Qin, Weiwen Zhou, Jun Nie
Format: Article
Language:English
Published: BMC 2025-04-01
Series:Biology Direct
Subjects:
Bladder cancer
Mitotic catastrophe
Machine learning
Prognosis
Single cell
Mendelian randomization
Online Access:https://doi.org/10.1186/s13062-025-00650-x
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https://doi.org/10.1186/s13062-025-00650-x

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