Epithelial-mesenchymal transition (EMT) can contribute to tumor invasion, metastasis, and resistance to chemotherapy or hormone therapy. EMT may be induced by a variety of growth factors, such as epidermal growth factor (EGF). Most studies regarding EMT have focused on TGF-β-Smads signaling. The mechanism of EGF-induced EMT via activation of the Smad2/3 in breast cancer cells, MCF-7 and MDA-MB-231, remains unclear. The expression levels of Snail, vimentin, and fibronectin were increased by EGF treatment in a time-dependent manner, while the expression level of E-cadherin was decreased. EGF-induced nuclear co-localization of phospho-Smad2/3 and Snail and cancer cell migration were inhibited by pretreatment with an ERK1/2 inhibitor, PD98059 and a phospho-Smad2 inhibitor, SB203580. Knockdown of Smad2/3 expression suppressed EGF-induced expressions of Snail, vimentin, fibronectin, and cancer cell invasion, suggesting an acquisition of the mesenchymal and migratory phenotype in less aggressive MCF-7 cells. Moreover, MDA-MB-231 cells were shown that EGF-induced EMT, and cell invasion through ERK1/2-phospho-Smad2/3-Snail signaling pathway. We have discovered that EGF-stimulated activation of Smad2/3 upregulated several key EMT markers, inhibited E-cadherin expression, promoted EMT, enhanced migration and invasion in MCF-7 and MDA-MB-231 breast cancer cells. Identification of this molecular mechanism may provide new molecular targets for the development of therapies for metastatic breast cancer.
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http://dx.doi.org/10.18632/oncotarget.13116 | DOI Listing |
Sci Rep
December 2024
Department of Clinical Pharmacy, Baoshan Hospital Affiliated to, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
This study investigates the potential treatment of breast cancer utilizing Gentiana robusta King ex Hook. f. (QJ) through an integrated approach involving network pharmacology, molecular docking, and molecular dynamics simulation.
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December 2024
Department of Breast Surgery, Second Affiliated Hospital of Dalian Medical University, No. 467 Zhongshan Road, Shahekou District, Dalian, China.
Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model.
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