A machine learning-based prognostic signature utilizing MSC proteomics for predicting bladder cancer prognosis and treatment response
The signature based on MSC secreted protein profiles could effectively predict the prognosis of BLCA and provided valuable guidance for treatment and drug resistance.
Authors
- Luyang Zhang (ORCID: https://orcid.org/0000-0002-8958-7789)
- Zhiping Wang
- Zhilong Dong (ORCID: https://orcid.org/0000-0002-4919-8164)
- Suoshi Jing (ORCID: https://orcid.org/0000-0002-7194-5293)
- Hui Ding (ORCID: https://orcid.org/0000-0001-9617-9401)
- Junqiang Tian (ORCID: https://orcid.org/0000-0002-4529-4650)
- Ze Zhang (ORCID: https://orcid.org/0000-0001-6335-6491)
- Enguang Yang (ORCID: https://orcid.org/0000-0002-9028-8065)
- Yufeng Guo (ORCID: https://orcid.org/0009-0008-9153-7335)
- Xinyu Zhang (ORCID: https://orcid.org/0000-0001-8154-9334)
- Pan Li (ORCID: https://orcid.org/0000-0002-9861-8305)
- Yuanfeng Zhang (ORCID: https://orcid.org/0000-0002-4330-072X)
- Li Yang
- Luhua Ji
Institutions
- Lanzhou University Second Hospital (CN)
- Lanzhou University (CN)
Publication Details
- Journal
- Translational Oncology
- Published
- 2025-03-11
- DOI
- https://doi.org/10.1016/j.tranon.2025.102349
- Citations
- 2
- Primary Topic
- Bladder and Urothelial Cancer Treatments
- Type
- article
- Field-Weighted Citation Impact
- 4.01
Funders
- National Natural Science Foundation of China
- Basic and Applied Basic Research Foundation of Guangdong Province