Application of Novel Transcription Factor Machine Learning Model and Targeted Drug Combination Therapy Strategy in Triple Negative Breast Cancer
Abstract
:1. Introduction
2. Results
2.1. Identification of TF Gene Sets Highly Associated with TNBC
2.2. HTFSS Has Excellent Prognostic Prediction Performance
2.3. Application of HTFSS in Clinical Diagnosis and Prediction of Immunotherapy Response
2.4. The Specific Relationship between HTFSS and TIME Was Analyzed by scRNA-Seq
2.5. Virtual Docking of Hub TF Genes with Targeted Drugs
2.6. Research on the Prognostic Function of Hub TF Target Genes
2.7. DOX Combined with TSA Significantly Inhibited the Proliferation of TNBC Cells
2.8. The Combination of DOX and TSA Significantly Inhibited the Progression of TNBC in Mice, and Calcitriol Could Improve the Cardiac Function of Mice
3. Discussion
4. Materials and Methods
4.1. Data Sources
4.2. Selection of TFs in Hub Gene Sets Highly Related to TNBC
4.3. Construction and Validation of the HTFSS
4.4. Explore the Clinical Application Value of HTFSS and the Predictive Value of Immunotherapy
4.5. scRNA-Seq Cohort Research
4.6. Targeted Drug Screening of Hub TF Genes
4.7. Research on the Prognostic Function of Hub TF Target Genes
4.8. In Vitro Experimental Verification
4.8.1. Cell Culture
4.8.2. CCK-8
4.8.3. Real-Time q-PCR (RT q-PCR)
4.8.4. Western Blotting Analysis
4.9. In Vivo Tumor Model
4.9.1. Construction of Orthotopic Transplanted Tumor in Nude Mice
4.9.2. Immunohistochemical Staining
4.9.3. Hematoxylin and Eosin (H&E) Staining
4.10. Statistical Analysis
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Pang, J.; Li, H.; Zhang, X.; Luo, Z.; Chen, Y.; Zhao, H.; Lv, H.; Zheng, H.; Fu, Z.; Tang, W.; et al. Application of Novel Transcription Factor Machine Learning Model and Targeted Drug Combination Therapy Strategy in Triple Negative Breast Cancer. Int. J. Mol. Sci. 2023, 24, 13497. https://doi.org/10.3390/ijms241713497
Pang J, Li H, Zhang X, Luo Z, Chen Y, Zhao H, Lv H, Zheng H, Fu Z, Tang W, et al. Application of Novel Transcription Factor Machine Learning Model and Targeted Drug Combination Therapy Strategy in Triple Negative Breast Cancer. International Journal of Molecular Sciences. 2023; 24(17):13497. https://doi.org/10.3390/ijms241713497
Chicago/Turabian StylePang, Jianyu, Huimin Li, Xiaoling Zhang, Zhengwei Luo, Yongzhi Chen, Haijie Zhao, Handong Lv, Hongan Zheng, Zhiqian Fu, Wenru Tang, and et al. 2023. "Application of Novel Transcription Factor Machine Learning Model and Targeted Drug Combination Therapy Strategy in Triple Negative Breast Cancer" International Journal of Molecular Sciences 24, no. 17: 13497. https://doi.org/10.3390/ijms241713497