Identification and validation of soft tissue sarcoma-specific transcriptomic model for predicting radioresistance
We developed the STS-RRI to predict the radioresistance of patients with STS in the TCGA dataset, showing a higher performance than RSI.
Authors
- Jeong Il Yu (ORCID: https://orcid.org/0000-0002-2009-7263)
- Gyu Sang Yoo (ORCID: https://orcid.org/0000-0002-5542-5263)
- Daechan Park (ORCID: https://orcid.org/0000-0003-4991-5247)
- Do Hoon Lim (ORCID: https://orcid.org/0000-0002-5426-0604)
- Kyo Won Lee (ORCID: https://orcid.org/0000-0002-2722-7817)
- Changhoon Choi (ORCID: https://orcid.org/0000-0002-2847-1168)
- Won Dong Kim
- Jae Berm Park (ORCID: https://orcid.org/0000-0001-9117-2278)
- Woo‐Yoon Park (ORCID: https://orcid.org/0000-0003-0329-5046)
- Jung Eun Kim (ORCID: https://orcid.org/0000-0003-1670-0995)
- Jae Yun Moon
- Sohee Park (ORCID: https://orcid.org/0000-0001-7829-4131)
- Sung Joo Kim
Institutions
- Chungbuk National University (KR)
- Samsung Medical Center (KR)
- New Generation University College (ET)
- Cheju Halla General Hospital (KR)
- National University College (PR)
- Io Therapeutics (United States) (US)
- Chungbuk National University Hospital (KR)
- Ajou University (KR)
- Sungkyunkwan University (KR)
Publication Details
- Journal
- International Journal of Radiation Biology
- Published
- 2025-01-10
- DOI
- https://doi.org/10.1080/09553002.2024.2447509
- Citations
- 1
- Primary Topic
- Sarcoma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 1.52
Funders
- National Research Foundation