Integrating machine learning models with multi-omics analysis to decipher the prognostic significance of mitotic catastrophe heterogeneity in bladder cancer
The model we developed was a powerful predictive tool for BLCA prognosis and revealed the impact of mitotic catastrophe heterogeneity on BLCA in multiple dimensions, which then guided clinical decision-making. Furthermore, we highlighted the potential of ANLN as a BLCA target.
Authors
- Weiping Luo (ORCID: https://orcid.org/0000-0002-6041-4366)
- Zhenyu Hang
- You‐Yang Zhao (ORCID: https://orcid.org/0000-0002-0041-0339)
- Ke Jiang (ORCID: https://orcid.org/0000-0003-3808-111X)
- Weiwen Zhou (ORCID: https://orcid.org/0000-0002-0505-3967)
- Li Wang (ORCID: https://orcid.org/0000-0001-7692-5067)
- Zihao Li (ORCID: https://orcid.org/0000-0002-0034-1783)
- Hongxiang Ma
- Chao Qin (ORCID: https://orcid.org/0000-0003-1915-4272)
- Jun Fan (ORCID: https://orcid.org/0000-0002-5445-437X)
- Jun Nie
- Haojie Dai (ORCID: https://orcid.org/0009-0005-8399-8551)
- Xin Huang (ORCID: https://orcid.org/0000-0002-8505-8425)
- Zhigang Yu (ORCID: https://orcid.org/0000-0002-3093-4491)
- Ming Wu
Institutions
- Nanjing Medical University (CN)
Publication Details
- Journal
- Biology Direct
- Published
- 2025-04-21
- DOI
- https://doi.org/10.1186/s13062-025-00650-x
- Citations
- 10
- Primary Topic
- Bladder and Urothelial Cancer Treatments
- Type
- article
- Field-Weighted Citation Impact
- 20.06