What changed from existing technology?
This study uses machine learning to find genes linked to immune cells that might help predict how well skin cancer patients respond to immunotherapy treatments.
This is the first study to develop a Treg-related gene signature using machine learning for SKCM, providing a new approach to predict immunotherapy outcomes.
Commercialization signals visible at publication
This is not a success probability or investment opinion. It prioritizes follow-up using evidence available when the paper was published.
Verified evidence · 1
e clinical significance of Treg-related genes in SKCM remains unclear. We constructed a Treg-related gene signature for SKCM using bulk RNA-seq data from
Abstract ↗- 시제품·장치 구현 근거 미확인
- 제조·규모 확장 근거 미확인
- 장기·실사용 검증 근거 미확인
Later patent, company, clinical, and production events are added separately only after verification.
Patent check: Not checked yetCurrent technology stage: Near commercialization
See what changed before reading the full paper.
What still needs to be solved?
Repeated real-world validation, manufacturability, cost, and long-term stability still require follow-up evidence.
What could change if this succeeds?
This gene signature could help personalize immunotherapy for SKCM patients by identifying those likely to benefit from treatment.
Keep following this technology
Revisit changes when new papers, patents, company participation, validation, or products appear.