Volume 27, Issue 5 (Iran South Med J 2025)
Iran South Med J 2025, 27(5): 394-408 |
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Khorshidi M, Manoochehri H, AAlikhani M A, Ezatzadeh M. Identification of Key Hub Genes Associated with Breast Cancer Stem Cells to Over-come Therapy Resistance. Iran South Med J 2025; 27 (5) :394-408
URL: http://ismj.bpums.ac.ir/article-1-2142-en.html
URL: http://ismj.bpums.ac.ir/article-1-2142-en.html
1- Student Research Committee, Bushehr University of Medical Sciences, Bushehr, Iran
Department of Medical Biotechnology, School of Paramedicine, Bushehr University of Medical Sciences, Bushehr, Iran
2- The Persian Gulf Marine Biotechnology Research Center, The Persian Gulf Biomedical Sciences Research Institute, Bushehr University of Medical Sciences, Bushehr, Iran ,Manoochehry.hamed@gmail.com
3- Department of Medical Biotechnology, School of Paramedicine, Bushehr University of Medical Sciences, Bushehr, Iran
Department of Medical Biotechnology, School of Paramedicine, Bushehr University of Medical Sciences, Bushehr, Iran
2- The Persian Gulf Marine Biotechnology Research Center, The Persian Gulf Biomedical Sciences Research Institute, Bushehr University of Medical Sciences, Bushehr, Iran ,
3- Department of Medical Biotechnology, School of Paramedicine, Bushehr University of Medical Sciences, Bushehr, Iran
Abstract: (67 Views)
Background: Breast cancer is one of the most common and fatal malignancies in women. The presence of cancer stem cells (CSCs), which play a crucial role in metastasis, therapeutic resistance, and disease recurrence, presents a major treatment challenge. Identifying CSCs-specific therapeutic targets through systems biology approaches can lead to more effective treatment strategies
Materials and Methods: Three gene expression datasets related to breast cancer— GSE7513 (Cancer stem/non-stem cells), GSE15852 (tumor/normal tissues), and GSE76540 (doxorubicin resistant/sensitive cells)— were obtained from the GEO database. Data were analyzed using GEO2R, DAVID, STRING, and Cytoscape. Hub genes were identified based on network parameters (GSE7513 and GSE15852 datasets) and compared with differentially expressed genes in GSE76540. Additionally, gene expression levels, treatment response prediction, and drug–gene interactions were evaluated using TNMplot, ROC Plotter, and DGIdb.
Results: Gene expression data from the datasets included 1446, 344, and 1826 DEGs in GSE7513, GSE15852, and GSE76540, respectively. Comparative analysis between GSE7513 and GSE15852 identified 75 common DEGs. Protein– protein interaction network analysis output was three clusters and seven hub genes, four of them overlapped with DEGs in GSE76540. AGR2, GATA3, and KRT19 genes were selected based on TNM. KRT19 and GATA3 were associated with patients' response to treatment. According to the DGIdb, GATA3 is a potentially druggable target with existing therapeutic compounds.
Conclusion: This study suggests that GATA3 may serve as a biomarker for predicting treatment response and as a potential therapeutic target in breast cancer.
Materials and Methods: Three gene expression datasets related to breast cancer— GSE7513 (Cancer stem/non-stem cells), GSE15852 (tumor/normal tissues), and GSE76540 (doxorubicin resistant/sensitive cells)— were obtained from the GEO database. Data were analyzed using GEO2R, DAVID, STRING, and Cytoscape. Hub genes were identified based on network parameters (GSE7513 and GSE15852 datasets) and compared with differentially expressed genes in GSE76540. Additionally, gene expression levels, treatment response prediction, and drug–gene interactions were evaluated using TNMplot, ROC Plotter, and DGIdb.
Results: Gene expression data from the datasets included 1446, 344, and 1826 DEGs in GSE7513, GSE15852, and GSE76540, respectively. Comparative analysis between GSE7513 and GSE15852 identified 75 common DEGs. Protein– protein interaction network analysis output was three clusters and seven hub genes, four of them overlapped with DEGs in GSE76540. AGR2, GATA3, and KRT19 genes were selected based on TNM. KRT19 and GATA3 were associated with patients' response to treatment. According to the DGIdb, GATA3 is a potentially druggable target with existing therapeutic compounds.
Conclusion: This study suggests that GATA3 may serve as a biomarker for predicting treatment response and as a potential therapeutic target in breast cancer.
Keywords: Breast Neoplasm, Cancer Stem Cells, Chemoresistance, Systems Biology, Protein– Protein Interaction
Type of Study: Original |
Subject:
medical Genetics
Received: 2025/04/15 | Accepted: 2025/05/11 | Published: 2025/06/1
Received: 2025/04/15 | Accepted: 2025/05/11 | Published: 2025/06/1
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