Breast cancer stem cell activity driven by ME18D gene expression in the tumor microenvironment

Abstract

BACKGROUND

Breast cancer is one of the most prevalent malignancies affecting women worldwide, with approximately 2.3 million new cases diagnosed annually. Breast cancer stem cells (BCSCs) play pivotal roles in tumor initiation, progression, metastasis, therapeutic resistance, and disease recurrence. Cancer stem cells possess self-renewal capacity, multipotent differentiation potential, and enhanced tumorigenic activity, but their molecular characteristics and regulatory mechanisms require further investigation.

AIM

To comprehensively characterize the molecular features of BCSCs through multi-omics approaches, construct a prognostic prediction model based on stem cell-related genes, reveal cell-cell communication networks within the tumor microenvironment, and provide theoretical foundation for personalized treatment strategies.

METHODS

Flow cytometry was employed to detect the expression of BCSC surface markers (CD34, CD45, CD29, CD90, CD105). Transcriptomic analysis was performed to identify differentially expressed genes. Least absolute shrinkage and selection operator regression analysis was utilized to screen key prognostic genes and construct a risk scoring model. Single-cell RNA sequencing and spatial transcriptomics were applied to analyze tumor heterogeneity and spatial gene expression patterns. Cell-cell communication network analysis was conducted to reveal interactions between stem cells and the microenvironment.

RESULTS

Flow cytometric analysis revealed the highest expression of CD105 (96.30%), followed by CD90 (68.43%) and CD34 (62.64%), while CD29 showed lower expression (7.16%) and CD45 exhibited the lowest expression (1.19%). Transcriptomic analysis identified 3837 significantly differentially expressed genes (1478 upregulated and 2359 downregulated). Least absolute shrinkage and selection operator regression analysis selected 10 key prognostic genes, and the constructed risk scoring model effectively distinguished between high-risk and low-risk patient groups (P < 0.001). Single-cell analysis revealed tumor cellular heterogeneity, and spatial transcriptomics demonstrated distinct spatial expression gradients of stem cell-related genes. MED18 gene showed significantly higher expression in malignant tissues (P < 0.001) and occupied a central position in cell-cell communication networks, exhibiting significant correlations with tumor cells, macrophages, fibroblasts, and endothelial cells.

CONCLUSION

This study comprehensively characterized the molecular features of BCSCs through multi-omics approaches, identified reliable surface markers and key regulatory genes, and constructed a prognostic prediction model with clinical application value.

Keywords: Breast cancer stem cells; Surface markers; Transcriptomics; Least absolute shrinkage and selection operator regression; Prognostic model

Core Tip: This study integrates flow cytometry, transcriptomics, least absolute shrinkage and selection operator modeling, single-cell and spatial transcriptomics to comprehensively characterize breast cancer stem cells. CD105 was identified as a key surface marker, and a prognostic gene signature including MED18 was developed. MED18 showed strong correlations with tumor, immune, and stromal cells, suggesting a central role in breast cancer stem cell regulation and microenvironment interaction. These findings offer novel insights into tumor heterogeneity and provide potential biomarkers and therapeutic targets for precision treatment in breast cancer.