Integrated machine learning algorithms identify KIF15 as a potential prognostic biomarker and correlated with stemness in triple-negative breast cancer-Reference-Cited by-同舟云学术

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Integrated machine learning algorithms identify KIF15 as a potential prognostic biomarker and correlated with stemness in triple-negative breast cancer

Published:2024-09-13 Issue:1 Volume:14 Page:
ISSN:2045-2322
Container-title:Scientific Reports
language:en
Short-container-title:Sci Rep

Author:

Guo Qiaonan,Qiu Pengjun,Pan Kelun,Liang Huikai,Liu Zundong,Lin Jianqing

Funder

Natural Science Foundation of Fujian Province

Doctoral Special Foundation of the Second Affiliated Hospital of Fujian Medical University

Publisher

Springer Science and Business Media LLC

Link

https://www.nature.com/articles/s41598-024-72406-y.pdf

Reference70 articles.

1. Siegel, R., Miller, K., Wagle, N. & Jemal, A. Cancer statistics, 2023. CA Cancer J. Clin. 73, 17–48. https://doi.org/10.3322/caac.21763 (2023).

2. Liedtke, C. et al. Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 26, 1275–1281. https://doi.org/10.1200/jco.2007.14.4147 (2008).

3. Liu, S. & Wicha, M. Targeting breast cancer stem cells. J. Clin. Oncol. Off. J. Am. Soc. Clin. Oncol. 28, 4006–4012. https://doi.org/10.1200/jco.2009.27.5388 (2010).

4. Dick, J. Breast cancer stem cells revealed. Proc. Natl. Acad. Sci. U. S. A. 100, 3547–3549. https://doi.org/10.1073/pnas.0830967100 (2003).

5. Bai, X., Ni, J., Beretov, J., Graham, P. & Li, Y. Cancer stem cell in breast cancer therapeutic resistance. Cancer Treat. Rev. 69, 152–163. https://doi.org/10.1016/j.ctrv.2018.07.004 (2018).

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