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Cited by 408

The DOI matches its Crossref record; 112 of the 112 references it cites were found in a registry or an index.

Trendsin Molecular Medicine: Matrix metalloproteinases and their inhibitors in tumour growth and invasion

Open access
Kähäri, Veli-Matti, Saarialho-Kere, Ulpu·1999·Annals of Medicine

Abstract

Controlled degradation of the extracellular matrix (ECM) is crucial for the growth, invasive capacity, metastasis and angiogenesis of tumours. Matrix metalloproteinases (MMPs), a family of zinc-dependent neutral endopeptidases that are collectively capable of degrading essentially all ECM components, apparently play an important role in all of these aspects of tumour development. In addition, there is recent evidence that MMPs are also important for tumour cell survival. At present, therapeutic intervention on tumour growth and invasion based on the inhibition of MMP activity is under intensive investigation, and several MMP inhibitors are already being used on malignant tumours of various organs in clinical trials. In this review we discuss the role of MMPs and their inhibitors in tumour invasion as a basis for prognostic purposes and for targeted therapeutic intervention in cancer.

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Crossref · 10.3109/07853899909019260

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Metadata

Authors
Kähäri, V.-M., & Saarialho-Kere, U.
Venue
Annals of Medicine
Published
1999-01
Volume
31
Issue
1
Pages
34-45
ISSN
  • 0785-3890 (ISSN-L)
  • 1365-2060
OpenAlex
W2051347744
Cited by
408
References
112
FWCI
12.11
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Trendsin Molecular Medicine: Matrix metalloproteinases and their inhibitors in tumour growth and invasion. https://app.cochat.ai/papers/10.3109/07853899909019260
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Kähäri, V.-M., & Saarialho-Kere, U. (1999). Trendsin Molecular Medicine: Matrix metalloproteinases and their inhibitors in tumour growth and invasion. Annals of Medicine. https://doi.org/10.3109/07853899909019260

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.