Unveiling the power of Treg.Sig: a novel machine-learning derived signature for predicting ICI response in melanoma
The study developed a promising Treg.Sig signature that predicts ICI response of melanomas and could be used for selecting patients for immunotherapy. Meanwhile, our study potentially paves the way for overcoming immune resistance by targeting Treg-associated genes.
Authors
- Guanchao Sun (ORCID: https://orcid.org/0009-0009-8439-2884)
- Xiao Ding (ORCID: https://orcid.org/0000-0002-5512-2131)
- Yong Liao (ORCID: https://orcid.org/0000-0002-2940-2762)
- Yunlong Fan (ORCID: https://orcid.org/0000-0002-2647-9609)
- Shengwei Lai
- Haiyang Li (ORCID: https://orcid.org/0000-0002-8205-4875)
- Jiaman Yang
- Rongya Yang (ORCID: https://orcid.org/0000-0001-8201-9394)
- Xin Yang (ORCID: https://orcid.org/0000-0002-7343-8412)
- Yulin Xie
- Xingyue Zhang (ORCID: https://orcid.org/0000-0002-0882-2610)
- Shuaifei Ji
- Shuo Yang (ORCID: https://orcid.org/0000-0002-6834-4905)
- Gé Gé
Institutions
- Hebei Medical University (CN)
- Chinese PLA General Hospital (CN)
- Third Hospital of Hebei Medical University (CN)
- Zhujiang Hospital (CN)
- PLA Academy of Military Science (CN)
- Southern Medical University (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2025-03-28
- DOI
- https://doi.org/10.3389/fimmu.2025.1508638
- Primary Topic
- Cutaneous Melanoma Detection and Management
- Type
- article
- Field-Weighted Citation Impact
- 0.00