Guo DY, Liu ZY, Yi QC. Breast cancer stem cell activity driven by ME18D gene expression in the tumor microenvironment. World J Stem Cells 2026; 18(1): 111348 [DOI: 10.4252/wjsc.v18.i1.111348]
Corresponding Author of This Article
Qian-Chuan Yi, MS, Department of General Surgery, University-Town Hospital of Chongqing Medical University, No. 55 Daxuecheng Middle Road, Shapingba District, Chongqing 401331, China. anyway173@163.com
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Jan 26, 2026 (publication date) through Jan 26, 2026
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World Journal of Stem Cells
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Guo DY, Liu ZY, Yi QC. Breast cancer stem cell activity driven by ME18D gene expression in the tumor microenvironment. World J Stem Cells 2026; 18(1): 111348 [DOI: 10.4252/wjsc.v18.i1.111348]
World J Stem Cells. Jan 26, 2026; 18(1): 111348 Published online Jan 26, 2026. doi: 10.4252/wjsc.v18.i1.111348
Breast cancer stem cell activity driven by ME18D gene expression in the tumor microenvironment
De-Yang Guo, Zhang-Yi Liu, Qian-Chuan Yi
De-Yang Guo, Zhang-Yi Liu, Department of Breast and Thyroid Vascular Surgery, Yongchuan Hospital Affiliated to Chongqing Medical University, Chongqing 402160, China
Qian-Chuan Yi, Department of General Surgery, University-Town Hospital of Chongqing Medical University, Chongqing 401331, China
Co-first authors: De-Yang Guo and Zhang-Yi Liu.
Author contributions: Guo DY and Liu ZY contributed equally to this work as co-first authors. Guo DY and Liu ZY designed the study, performed flow cytometry analysis and data collection, conducted bioinformatics analyses including least absolute shrinkage and selection operator regression modeling, and drafted the initial manuscript; Yi QC conceived the research project, supervised the study design and execution, provided critical intellectual input for data interpretation, revised the manuscript for important intellectual content. All authors reviewed and approved the final version of the manuscript for submission.
Supported by the Natural Science Foundation of Yongchuan District, No. 2023yc-jckx20021.
Institutional review board statement: The study was reviewed and approved by the Medical Ethics Committee of Yongchuan Hospital of Chongqing Medical University, approval No. 2023 LLS030.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
Data sharing statement: The datasets analyzed in this study are publicly available or can be obtained from the corresponding author upon reasonable request.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Qian-Chuan Yi, MS, Department of General Surgery, University-Town Hospital of Chongqing Medical University, No. 55 Daxuecheng Middle Road, Shapingba District, Chongqing 401331, China. anyway173@163.com
Received: July 1, 2025 Revised: August 7, 2025 Accepted: November 18, 2025 Published online: January 26, 2026 Processing time: 202 Days and 18.6 Hours
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.
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.
