Construction of a stromal cell-related prognostic signature based on a 101-combination machine learning framework for predicting prognosis and immunotherapy response in triple-negative breast cancer

Construction of a stromal cell-related prognostic signature based on a 101-combination machine learning framework for predicting prognosis and immunotherapy response in triple-negative breast cancer

BackgroundTriple-negative breast cancer (TNBC) is a highly aggressive subtype with limited therapeutic targets and poor immunotherapy outcomes. The tumor microenvironment (TME) plays a key role in cancer progression. Advances in single-cell transcriptomics have highlighted the impact of stromal cell...

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Bibliographic Details
Main Authors: Fanrong Li, Congnan Jin, Yacheng Pan, Zheng Zhang, Liying Wang, Jieqiong Deng, Yifeng Zhou, Binbin Guo, Shenghua Zhang
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-05-01
Series:Frontiers in Immunology
Subjects:
triple-negative breast cancer
machine learning
prognosis
immunotherapy
tumor microenvironment
Online Access:https://www.frontiersin.org/articles/10.3389/fimmu.2025.1544348/full
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https://www.frontiersin.org/articles/10.3389/fimmu.2025.1544348/full

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