Background: Breast cancer is the most common cancer affecting women in the world today. Matrix metalloproteinases (MMPs) are a family of endopeptidases that can degrade extracellular matrix proteins and promote cell invasion and metastasis. MMPs are differentially expressed and their expressions are often associated with a poor prognosis for patients.
Objective: The aim of this study is to investigate and compare the expression of MMPs in different grades of human breast cancer tissues with normal breast tissues.
Patients And Methods: We collected 39 breast cancer samples (24 grade II and 15 grade III) along with 16 normal breast tissues from outside the tumor margin during cancer removal surgery. The samples were analysed for the expression of all known MMPs using real-time quantitative PCR.
Results: The results indicate that mRNA expressions of MMP-1, -9,-11,-15,-24 and -25 were upregulated in breast cancer tissues when compared to normal breast tissues. But, the mRNA expressions of MMP-10 and MMP-19 were downregulated in cancer tissue. In membrane associated MMPs like MMP-15 and MMP-24 we found a grade dependent increase of their mRNA expression.
Conclusion: Our studies demonstrate that MMPs are differentially regulated in breast cancer tissues and they might play various roles in tumor invasion, metastasis and angiogenesis. Thus, MMPs are of immense value to be studied as diagnostic markers and drug target.
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http://dx.doi.org/10.3233/DMA-130986 | DOI Listing |
Sci Rep
December 2024
Department of Clinical Pharmacy, Baoshan Hospital Affiliated to, Shanghai University of Traditional Chinese Medicine, Shanghai, China.
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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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