Unveiling the power of Treg.Sig: a novel machine-learning derived signature for predicting ICI response in melanoma.

ABSTRACT:

Background

Although immune checkpoint inhibitor (ICI) represents a significant breakthrough in cancer immunotherapy, only a few patients benefit from it. Given the critical role of Treg cells in ICI treatment resistance, we explored a Treg-associated signature in melanoma, which had never been elucidated yet.

Methods

A new Treg signature, Treg.Sig, was created using a computational framework guided by machine learning, utilizing transcriptome data from both single-cell RNA-sequencing (scRNA-seq) and bulk RNA-sequencing (bulk-seq). Among the 10 Treg.Sig genes, hub gene STAT1's function was further validated in ICI resistance in melanoma mice receiving anti-PD-1 treatment.

Results

Treg.Sig, based on machine learning, was able to forecast survival outcomes for melanoma a

SUBMITTER: Fan Y 

PROVIDER: S-EPMC11985843 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature