Machine learning constructs a ferroptosis related signature for predicting prognosis and drug sensitivity in lung cancer
The FRS represents a powerful and clinically relevant tool for predicting the survival of lung cancer patients, offering new insights into personalized treatment and therapeutic decision-making.
Authors
- Zhengzhi Zou (ORCID: https://orcid.org/0000-0002-1893-7649)
- Qing Tang (ORCID: https://orcid.org/0000-0002-2128-4810)
- Chunlin Fu (ORCID: https://orcid.org/0000-0002-5565-4883)
- Weihong Sun (ORCID: https://orcid.org/0000-0002-6292-903X)
- Benxin Hou
- Yanjun Mi (ORCID: https://orcid.org/0000-0002-8164-6414)
- Zihao Li (ORCID: https://orcid.org/0009-0000-6613-6072)
- Qing Xia (ORCID: https://orcid.org/0000-0003-4373-2722)
- Yuan Liao
- Yibing Chen (ORCID: https://orcid.org/0000-0002-9117-3614)
- Zhenqing Han
Institutions
- Sun Yat-sen University (CN)
- Shanghai Jiao Tong University (CN)
- South China Normal University (CN)
- Renji Hospital (CN)
- First Affiliated Hospital of Xiamen University (CN)
- Sanya Central Hospital (CN)
- Third Affiliated Hospital of Sun Yat-sen University (CN)
- First Affiliated Hospital of Zhengzhou University (CN)
Publication Details
- Journal
- Cellular Oncology
- Published
- 2025-10-22
- DOI
- https://doi.org/10.1007/s13402-025-01121-1
- Primary Topic
- Ferroptosis and cancer prognosis
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China