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

Key Points

• The research aims to explore the stemness landscape of triple-negative breast cancer (TNBC) and identify key regulatory drivers.
• Integrated single-cell mapping with spatial analysis
• Machine learning algorithms to predict stemness landscapes
• Identification of core genes associated with stemness
• Core genes were linked to the maintenance of stemness in TNBC
• Potential molecular targets for personalized therapies were identified