Machine learning model in multi-omics perspective demystifies the prognostic significance of crotonylation heterogeneity in clear cell renal cell carcinoma
This study establishes a crotonylation-based prognostic model that effectively stratifies ccRCC risk and elucidates key mechanisms linking crotonylation heterogeneity to immune evasion, mutational burden, and metabolic reprogramming. The model offers clinical utility for personalized therapy selection.
Authors
- Weiping Luo (ORCID: https://orcid.org/0000-0002-6041-4366)
- Zhenyu Hang
- Kai Zhao
- 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)
- Chao Qin (ORCID: https://orcid.org/0000-0003-1915-4272)
- Jun Nie
- Haojie Dai (ORCID: https://orcid.org/0009-0005-8399-8551)
- Xin Huang (ORCID: https://orcid.org/0000-0002-8505-8425)
Institutions
- Nantong University (CN)
- Changzhou No.2 People's Hospital (CN)
- Nanjing Medical University (CN)
Publication Details
- Journal
- BMC Urology
- Published
- 2025-09-15
- DOI
- https://doi.org/10.1186/s12894-025-01914-4
- Citations
- 5
- Primary Topic
- Renal cell carcinoma treatment
- Type
- article
- Field-Weighted Citation Impact
- 7.59