Periostin is an extracellular matrix (ECM) protein that is overexpressed in a variety of human cancers, and its functions appear to be linked to tumor growth, metastasis, and angiogenesis. Recent clinical evidence suggests that aberrant periostin expression is correlated with poor outcome in patients with breast cancer. To identify novel tools to regulate the functional role of periostin, we generated benzyl-d(U)TP-modified DNA aptamers that were directed against human periostin (PNDAs) and characterized their functional roles in breast cancer progression. PNDA-3 selectively bound to the FAS-1 domain of periostin with nanomolar affinity and disrupted the interaction between periostin and its cell surface receptors, αvβ3 and αvβ5 integrins. PNDA-3 markedly antagonized the periostin-induced adhesion, migration, and invasion of breast cancer cells and blocked the activation of various components of the αvβ3 and αvβ5 integrin signal transduction pathways. In a 4T1 orthotopic mouse model, PNDA-3 administration significantly reduced primary tumor growth and distant metastasis. Thus, our results demonstrated that periostin-integrin signaling regulates breast cancer progression at multiple levels in tumor cells and the tumor microenvironment. DNA aptamers targeting periostin may potentially be used to inhibit breast cancer progression.
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http://dx.doi.org/10.1038/mt.2013.30 | 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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