Spatial multi-omics analysis of tumor-stroma boundary cell features for predicting breast cancer progression and therapy response DOI Creative Commons
Yuanyuan Wu, Youyang Shi,

Zhanyang Luo

et al.

Frontiers in Cell and Developmental Biology, Journal Year: 2025, Volume and Issue: 13

Published: March 26, 2025

Background The tumor boundary of breast cancer represents a highly heterogeneous region. In this area, the interactions between malignant and non-malignant cells influence progression, immune evasion, drug resistance. However, spatial transcriptional profile its role in prognosis treatment response remain unclear. Method Utilizing Cottrazm algorithm, we reconstructed intricate boundaries identified differentially expressed genes (DEGs) associated with these regions. Cell-cell co-positioning analysis was conducted using SpaCET, which revealed key tumor-associated macrophage (TAMs) cancer-associated fibroblasts (CAFs). Additionally, Lasso regression employed to develop body signature (MBS), subsequently validated TCGA dataset for prediction assessment. Results Our research indicates that is characterized by rich reconstruction extracellular matrix (ECM), immunomodulatory regulation, epithelial-to-mesenchymal transition (EMT), underscoring significance progression. Spatial colocalization reveals significant interaction CAFs M2-like (TAM), contributes exclusion MBS score effectively stratifies patients into high-risk groups, survival outcomes exhibiting high scores being significantly poorer. Furthermore, sensitivity demonstrates high-MB tumors had poor chemotherapy strategies, highlighting modulating therapeutic efficacy. Conclusion Collectively, investigate transcription group bulk data elucidate characteristics molecules cancer. CAF-M2 phenotype emerges as critical determinant immunosuppression resistance, suggesting targeting may improve responses. serves novel prognostic tool offers potential strategies guiding personalized approaches

Language: Английский

Spatial multi-omics analysis of tumor-stroma boundary cell features for predicting breast cancer progression and therapy response DOI Creative Commons
Yuanyuan Wu, Youyang Shi,

Zhanyang Luo

et al.

Frontiers in Cell and Developmental Biology, Journal Year: 2025, Volume and Issue: 13

Published: March 26, 2025

Background The tumor boundary of breast cancer represents a highly heterogeneous region. In this area, the interactions between malignant and non-malignant cells influence progression, immune evasion, drug resistance. However, spatial transcriptional profile its role in prognosis treatment response remain unclear. Method Utilizing Cottrazm algorithm, we reconstructed intricate boundaries identified differentially expressed genes (DEGs) associated with these regions. Cell-cell co-positioning analysis was conducted using SpaCET, which revealed key tumor-associated macrophage (TAMs) cancer-associated fibroblasts (CAFs). Additionally, Lasso regression employed to develop body signature (MBS), subsequently validated TCGA dataset for prediction assessment. Results Our research indicates that is characterized by rich reconstruction extracellular matrix (ECM), immunomodulatory regulation, epithelial-to-mesenchymal transition (EMT), underscoring significance progression. Spatial colocalization reveals significant interaction CAFs M2-like (TAM), contributes exclusion MBS score effectively stratifies patients into high-risk groups, survival outcomes exhibiting high scores being significantly poorer. Furthermore, sensitivity demonstrates high-MB tumors had poor chemotherapy strategies, highlighting modulating therapeutic efficacy. Conclusion Collectively, investigate transcription group bulk data elucidate characteristics molecules cancer. CAF-M2 phenotype emerges as critical determinant immunosuppression resistance, suggesting targeting may improve responses. serves novel prognostic tool offers potential strategies guiding personalized approaches

Language: Английский

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