Integrated single-cell and spatial mapping coupled with machine learning unveils core stemness landscapes and regulatory drivers in triple-negative breast cancer

Our predictive model offers a novel perspective on the stemness landscape of TNBC. These core genes play key roles in maintaining stemness and also serve as potential molecular targets for personalized therapies aimed at TNBC stem-like cells.

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Publication Details

Journal
Discover Oncology
Published
2026-03-23
DOI
https://doi.org/10.1007/s12672-026-04824-5
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00
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article

Integrated single-cell and spatial mapping coupled with machine learning unveils core stemness landscapes and regulatory drivers in triple-negative breast cancer

Zhenzhong Huo, Tiansong Yang, Weibo Sun, Chun Lou
Discover Oncology
Single-cell and spatial transcriptomics
article

Integrated single-cell and spatial mapping coupled with machine learning unveils core stemness landscapes and regulatory drivers in triple-negative breast cancer

Zhenzhong Huo, Tiansong Yang, Weibo Sun, Chun Lou
article en

Abstract

Our predictive model offers a novel perspective on the stemness landscape of TNBC. These core genes play key roles in maintaining stemness and also serve as potential molecular targets for personalized therapies aimed at TNBC stem-like cells.

Discover Oncology
Harbin Medical University (CN), Heilongjiang University of Chinese Medicine (CN), First Affiliated Hospital of Heilongjiang University of Chinese Medicine (CN), Third Affiliated Hospital of Harbin Medical University (CN)
Openalex Percentile: Top 11%
Single-cell and spatial transcriptomics
DOIOpen Access PDFOpenAlex
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